While the field offers promising career prospects with a projected 27.9% growth by 2026 according to the U.S. Bureau of Labor Statistics, success demands mastery of both technical and soft skills.

This comprehensive guide outlines the essential steps to launch your data science career in 2025, from educational requirements and skill development to practical experience through internships and certifications.

• What is Data Science?
• What does a data science engineer do?
• How to start your data science career
• Important data science skills to master
• Roles and Responsibilities of a data science engineer
• Is it hard to get a job as a Data Scientist?
• Is a career in data science worth it?
• Final Words

What is Data Science?

Data science is the field of study that combines subject-matter knowledge, mathematics, statistics, artificial intelligence, and computer engineering to draw out useful insights from data.

Data science’s primary purpose is to collect, process, analyse, and filter information from raw data.

Data science uses the most advanced hardware, programming languages, and algorithms to resolve data-related issues.

The future of artificial intelligence lies in it.

Simply expressed, data science is all about:

• Presenting the right questions and analyzing the raw data
• Using various advanced and effective algorithms to model the data
• Visualizing the data to gain a deeper understanding
• Understanding the data to find the final result and make better decisions

What Does a Data Science Engineer Do?

A data engineer is responsible for creating systems for gathering, validating, and preparing high-quality data. Data engineers collect and prepare the data, while data scientists use the data to support better business decisions.

The data engineer/ strategist’s job is to find and integrate new datasets that can be used to expand the functionality of the business and collaborate with the technical team to develop data products.

Relevant read: How long does it take to learn data science?

How To Start Your Data Science Career?

There are a few questions you should ask yourself before planning to pursue a career as a data scientist:

• Do you love statistics, numbers, and graphs? Data science is entirely based on Maths and statistics. If you don’t like math, statistics, and numbers this is a red flag for you. If you don’t like working with numbers, you will find your job frustrating later in your career.
• Before advancing to a justified data scientist position, are you willing to learn and work at the entry-level?
• Are you able to program easily? Data scientists should be comfortable using a variety of programming languages, including R and Python.

You can move forward and begin learning the data science abilities once you have a positive answer to the questions above.

Let’s take a look at the steps to Becoming a Data Scientist

Step 1. Get an undergraduate degree in data science

To become a data scientist, you must first get an undergraduate degree in data science or a closely related field.

Most employers will want to see a bachelor’s degree to hire you as a data scientist. Getting a bachelor’s degree in a field like data science, statistics, or computer science is an excellent way to get started in this profession.

 A senior professional with a bachelor’s or master’s degree in IT/ computer engineering can pursue a career in data science by upgrading and retraining through online data science courses in the US.

Otherwise, if you’re looking for a data science boot camp, post-graduation course or master’s in data science, GreatLearning’s online data science courses are a good place to start.

Here are some of the latest data science courses that you can enroll:

• MIT data science
• PG data science course
• Data science course in IIT

Find the latest data science job statistics.

Step 2. Develop your skill set

Even though a Bachelor’s degree may give you a theoretical understanding of the topic, it is important to brush up on your programming, technical, and non-technical skills.

Below are some of the most necessary skills you require for this job.

•  Programming languages like  Python, R, SQL, SAS, Java, Julia, JavaScript, Swift, Go, MATLAB, SAS
• Technical skills include machine learning, deep learning, processing large data sets, data visualization, data wrangling, statistical analysis and computing, mathematics, programming, statistics, and big data.
• Non-technical skills like strong business acumen, strong communication skills, and great intuition regarding data that you are working on.

Check out our list of top data science books that will upgrade your skill sets.

Step 3. Get Certifications in Data Science

Getting Data Science Online Courses & Certifications is a great way to showcase your skills.  Instead of buying random data science courses on the internet, try to get free certification from trustworthy sources like Simplilearn, Naukri, and LinkedIn.

Here are some data science courses and certification programs to help you on your way:

• Tableau Certification Training Course
• Expert instructor-led data science online course (6 months)
• Power BI Certification Course
• Data Science Online Courses
• Certifications in Data Analytics
• Data science with Python
• Data engineering and smart analytics

These two are the most widely used tools among data scientist professionals and would be an excellent addition to begin your career journey.

Step 4. Find Internships and Entry-Level Jobs

The best way to enter the data science field is through an internship. Find a position that can help you improve your skills and help you to grow in your career.

After your internship, you can either stay with the same firm (if they are hiring) or hunt for entry-level opportunities for data scientists, data analysts, and data engineers.

You can earn experience and work your way up in your career as your knowledge and skills expand.

At WebPipl, we use AI and NLP technology to help identify top tech candidates, but we also rely heavily on our recruiters to ensure that we’re selecting candidates who are the right fit for our company culture.

To crack interviews with tech recruiters, candidates should focus not only on showcasing their technical skills, but also on demonstrating their ability to collaborate with others, solve problems creatively, and communicate their ideas effectively.

– Contributed by Raju Thammala (founder of WebPipl)

Relevant read: Learn how to get a data science internship (full process)

Step 5. Go for Higher education

While a bachelor’s degree may be sufficient to get you started in the ideal job and gain the necessary real-world work experience, a master’s degree in data science would help you to enhance your knowledge and empower you to grow in your field.

This work experience can make you more appealing to an employer for a high-level Position like a Data scientist. Here are some master’s degree options available:

• Master of Computer Science in Data Science
• Master of Data and Network Analysis
• Master of Applied Data Science
• Master of Science in Data Science

Important Data Science Skills To Master

There are two different types of skills required to master data science:

1. Technical Skills

Statistical analysis and computing, Machine Learning, Deep Learning, Processing large data sets, Data Visualization, Data Wrangling, Programming, and ig Data are some important technical skills that are required in master data science

2. Non-technical skills

Some of the most important Non-technical skills include Strong Business Acumen, Strong Communication Skills, and Great Data Intuition. You need to have the skill to make the right judgments and make quick decisions in this job.

Roles and Responsibilities of a data science engineer

1. Collecting data from different sources

Identifies and pulls relevant data from diverse sources via SQL, APIs, and web scraping into cloud data warehouses.

• Identify relevant data sources such as databases, APIs, websites, sensors
• Build data collection pipelines to gather, extract and move data
• Clean unstructured data to prepare it for analysis

2. Exploratory data analysis

Summarizes visualizes and statistically explores the raw data to uncover initial patterns, anomalies and hypotheses.

• Create data summaries, visualizations to understand data patterns
• Identify relationships between variables, data discrepancies
• Perform statistical analysis to derive initial insights from the data

3. Preparing data for modelling

Cleans data, imputes missing values, engineers helpful features and selects meaningful variables so models can best leverage the data.

• Handle missing data, outliers
• Engineer new features by transforming variables
• Perform feature selection to remove irrelevant variables
• Encode categorical variables for machine learning

4. Building machine learning models

• Try out different modelling techniques like classification, regression
• Fine-tune model hyperparameters to improve performance
• Evaluate models using relevant accuracy metrics
• Perform cross-validation to measure robustness

5. Deploying and maintaining models

Containerizes models via Docker, pushes them to prod via DevOps pipelines then monitors their performance to ensure continued reliability and retrains as appropriate.

• Put models into production via tools like SQL, Python
• Monitor model performance to detect concept drift
• Retrain models on new data to keep them relevant

6. Software engineering

Leverages languages like Python and R to write clean, reusable code under version control to track updates and enable seamless collaboration with others.

• Write reusable modular code for data processing tasks
• Version control code changes using Git and collaborate using GitHub
• Containerize data science workflows with Docker for reproducibility

Here’s a data science career tip from a data scientist:

Focus on Core Fundamentals

Start by gaining a solid understanding of statistics, mathematics, and programming, and then apply these skills by working on projects that interest you to gain practical experience and build a portfolio that showcases your abilities.

Continuously learn and stay up-to-date with the latest advancements in the field. Network with other professionals and attend industry events to expand your knowledge and build connections in the field. 

As a data scientist, it’s crucial to have strong documentation skills in order to effectively communicate technical details, insights, and results to stakeholders. Documentation not only helps to ensure the reproducibility and transparency of your analysis but also facilitates collaboration and knowledge transfer within teams and projects.

I highly recommend investing time and effort into learning how to create effective data science documentation.

Contributed by Edward Pinela III, Senior information technology project analyst and CEO at Lightray Solutions

Is it hard to get a job as a Data Scientist?

A Data Science job is not as easy as it looks. It is not a field where you can get a job after taking a 15-day course. Data Science requires discipline, and determination while learning.

Data science is the field of people who are interested in numbers, Stats, and engineering.

Most people choose this career because of its perks and high demand, despite the fact that this field does not interest them in any way.

“Businesses seek out talented individuals for Data scientist positions. Getting a data science course certificate does not make you a data scientist.

You need expertise, Knowledge, and years of experience to be a data scientist.”

Ronald Van Loon (Big data and Analytics Course Advisor at Simplilearn)

In an interview with Simplilearn, he talks about developments that will drive the need for more data scientists in the future and the current state of demand for data scientists.

Although, you can apply for entry-level data science jobs like data engineer and data analyst with an undergraduate degree and data science courses.

Not sure which job profile to choose in data science?

Check out our guide on different job types in data science where we have shared important job profiles to consider before choosing your career.

Is a Career In Data Science Worth It?

Yes, a job in data science offers excellent opportunities for the future. Data science professions are among the most rapidly increasing and in-demand in technology.

According to the US Bureau of Labor Statistics, demand for data science skills will rise by 27.9% by 2026.

According to a McKinsey report, this will result in a shortage of between 140,000 and 190,000 analysts in the United States alone.

When I checked on Google Trends, I found that the search demand for the term ‘data science’ has increased by 95% worldwide in the last 5 years.

Glassdoor has referred to a Data Scientist job as the “greatest job in America” and LinkedIn has labelled this job title as “the most promising career” because of its high demand, attractive salary, and all other benefits that come with it.

The US Bureau of Labor Statistics predicts a whopping 36% growth in data scientist jobs by 2031, significantly outpacing the average job market. This translates to a wealth of opportunities for qualified data scientists.

The beauty lies in its flexibility. Even if your academic journey is on a different track, a data science minor can be the perfect bridge. Many universities now offer this interdisciplinary minor, allowing you to seamlessly integrate data science skills into your existing major.

Here’s why a data science minor is a strategic move:

• High Demand, High Value: The data science minor equips you with exactly what employers seek – practical data analysis skills, machine learning proficiency, and statistical modeling expertise. With this in-demand skillset, you’ll stand out in the job market.
• Diverse Career Paths: As data becomes the cornerstone of decision-making across industries, a background in data science opens doors to a wide range of exciting career paths.

– Sai Sathish, Marketing and tech product head at Consainsights, a market research and consulting firm.

Further resources:

• Understand what is data curation in data science.
• Looking for companies to apply as a data scientist? Look at the top data science companies In Bangalore.

Final Words

The demand for Data scientist jobs has increased with their salaries and Data Scientists can often expect to earn six figures. Demand in this field also allows you to relocate much more easily, both domestically and abroad.

Before Pursuing this career, make sure this field of data and numbers interests you or else it will be difficult for you to survive in this field in the future. This guide provides guidance that can get you started with your data science career and help you to avoid some costly mistakes.

Article by

Samruddhi Chaporkar

Samruddhi is a Technical Content Writer of science and technology with an academic qualification in Bachelors in Computer Applications (BCA). Her love for writing and knowledge of computer science helps her write about trends in the technology industry that help people understand the ins and outs of the world of technology.

LinkedIn | Twitter | Author page

Learn about data science career paths, their salary, job growth, and career opportunities.

Is Data science a good career path in 2023?

Yes, Data science is definitely a good career choice for people who have a great interest in maths and statistics.

Data science has diverse career opportunities and career paths— Data Scientists, Data Analysts, Data Engineers, Data Architects, Machine Learning specialists, Business Analysts, System Analysts, Database Administrators, Data and Analytics Managers, and many more.

The Search demand for the keyword ‘data scientists’ worldwide has increased by 78% in the past 5 years, according to Google Trends.

Also, I found that there are 164,182 data scientist jobs available on LinkedIn for US alone— which highlights a high demand for skilled data scientists all around the world.

According to a survey by IBM, the demand for data scientists will increase by 28% by 2023, making it one of the most promising occupations in the future. 

What is Data science? 

Data Science is a combination of math and statistics with expert programming skills, advanced analytics, artificial intelligence (AI), machine learning, and subject matter expertise in data to derive valuable business insights.

Data science is used in predictive modeling, complex data analysis, and data visualization. Businesses of all sizes, from Big business giants to rookie startups, use data science technology to turn large database into useful business insights about customers, audience, and products.

Data science integrates statistical and mathematical concepts, programming and data manipulation skills, and domain-specific knowledge, emphasizing the importance of a multidisciplinary approach to extract valuable insights from data.

What Do Data Scientists Do?

A data scientist is a professional who builds algorithms and prediction models to extract the data required by the business, as well as help evaluate the data and share findings with peers.  They are responsible for processing, collecting, cleaning, and munging data. Once data gets into shape they analyze the data for further operations.

The role of data scientist includes parts of various traditional and technical career paths, such as mathematician, statistician, scientist, and computer programmer. 

Day-to-Day Tasks:

• To get insights from data, and look for trends and patterns in datasets.
• Guide the team and Share your suggestions with the upper management and other teams.
• Work with the technical and product management teams to identify the needs of the business and work on possible solutions.
• Apply new statistical or mathematical techniques as necessary for particular models or analyses.
• Perform day-to-day tasks with data analytic tools like Python, R, SAS, or SQL. 
• Keep up-to-date on developments and trends in the field of data science.

Data science career paths in 2023 

1. Data Analyst 

A Data Analyst is an entry-level role responsible for finding relevant information from the data, informing conclusions, and supporting decision-making. They play a critical role in cleaning, analyzing, understanding, and displaying data using various techniques and business intelligence tools.

Years of experience required: 0-1 years

Role Type: Executive or  Intern 

Average Annual Salary: ₹2.0 Lakhs in India and $1,52,275 in the US.

Job Responsibilities:

• Study data, analyze results using statistical methods, and deliver regular updates.
• In complex data sets, find, evaluate, and analyze trends or patterns.
• Maintain databases and data systems while gathering data from primary or secondary data sources.
• Develop and implement databases, data collection methods, data analytics, and other strategies to improve statistical efficiency and quality.
• Discover and define innovative process improvement opportunities.

Key Skills Required:

• Technical knowledge in relevant fields, such as data reports, Data representations, and queries.
• Strong Excel or SQL skills and curiosity to learn about more analytical tools.
• Good knowledge of AI applications like Big Query, Python, Tableau, Power BI, Google Looker Studio, Excel Scripting, and projects.

Learn about writing a perfect data specialist job description.

2. Data Engineer

A Data Engineer is responsible for designing, building, and maintaining the infrastructure that enables efficient collection, storage, and processing of data. Data Engineers create pipelines to transform and transport data, ensuring its quality, reliability, and accessibility for analysis. They play a critical role in data-driven decision-making and support various data-related tasks within organizations.

Years of experience required: 3-7 years

Role Type: Senior or team lead

Average Annual Salary:  ₹ 7.3 LPA in India and $1,15,470/yr in the US.

Job Responsibilities:

• Work closely with the data science team to develop complex algorithms that give a variety of new insights into data.
• Utilize agile software development techniques to constantly enhance the back-end systems.
• To create a more complete picture of user flows throughout the system and to enable rich data analysis, model front-end, and back-end data sources.
• Create data channels that can be used to clean, transform, and integrate data from many sources.
• Create models that can be used to predict things and provide answers for the entire company.

Relevant: Data engineer career paths

Key Skills Required:

• Experience with Python, SQL, and data visualization/exploration tools (three or more years) is required.
• Knowledge of the AWS environment, especially Redshift and RDS
• Communication abilities, especially when conveying technical concepts to non-technical business leaders
• Being able to work on multiple projects with a research-oriented team

Learn about various in-demand Python career paths

3. Business Analyst

Business Analysts analyze the company’s processes, identify needs, and recommend solutions to enhance efficiency and productivity. Their insights help in decision-making and successful project outcomes. They gather and document the necessary business data and requirements, and streamline communication between stakeholders.

Years of experience required: 3-5 years

Role Type: Senior or team lead

Average Annual Salary: ₹ 6.6 LPA in India and $94,744/yr in the US.

Job Responsibilities:

• Collaborate with stakeholders across various business units (such as sales, finance, security, and compliance), and communicate effectively and efficiently with the production, managerial, and executive teams.
• Maintain systems processes, such as sending monthly status reports to all relevant parties.
• Continuously assess, analyze, and communicate system requirements to the team.
• Develop and update documentation both internally and externally, and formally initiate and deliver requirements and documentation.
• Evaluate systems daily to improve performance and address issues
• Develop long-term relationships with partners for effective system integration, and respond to queries and concerns from managers and executives with supporting research and proposals.

Key Skills Required:

• Over five years of expertise in analytics and system development.
• High level of SQL and database management proficiency.
• Proven analytical skills.
• Experience in creating reports and process documentation.
• Excellent communication skills and the ability to transform data into insights that can be used in work.

4. Data Architect

Data Architects design and structure the overall data framework, defining how data is stored, organized, and accessed. They create data models, choose appropriate technologies, and establish guidelines for data integration and governance. Their role ensures data systems align with business needs and enable efficient data management and analytics.

Years of experience required: 3-7 years

Role Type: Senior or team lead

Average Annual Salary: ₹ 25.0 LPA in India and $1,55,022/yr in the US.

Job Responsibilities:

• Develop and implement efficient database solutions and models for storing and retrieving corporate data.
• By assessing client processes, applications, and programming, you may examine and determine database structural requirements.
• Examine database implementation techniques for compliance with internal and external regulations.
• Install and organize information systems to ensure the smooth operation of the business.
• Prepare detailed reports on database design and architecture for management and executive teams.
• Supervise data migration from legacy systems to new solutions.
• Perform frequent tests, debugging, and incorporating new features to monitor system performance.
• Make suggestions on ways to improve new and current database systems.
• Team members and management should be educated through training and one-on-one support.
• Respond to system faults in a convenient way to assist.

Key Skills Required:

• Minimum three to five years in a similar position.
• Extensive exposure to data mining and database structure systems.
• Organizational and analytical skills.
• Excellent verbal and written communication abilities.

5.  Data Scientist   

Data Scientists study complex data to extract valuable insights, using statistical and machine-learning techniques. They play a critical role in cleaning, process, and modeling data, and identifying patterns and trends to influence business decisions. They develop predictive models for data visualizations and solve problems across various domains.

Years of experience required: 5-8 years

Role Type: Senior or Manager 

Average Annual Salary: ₹ 9.3 LPA in India and $1,52,279/yr in the US.

Job Responsibilities:

• Work as a lead data strategist to find and integrate new datasets that can be used to enhance the functionality of the company’s products and closely work with the engineering team to produce data products.
• Carry out analytical tests to assist in solving issues across many industries and domains
• Recognize relevant data sources and sets to mine, as well as large structured and unstructured datasets and variables, for client business needs.
• Create and use models and algorithms to mine massive data stores; analyze data and errors to enhance models; data cleaning and validation for uniformity and accuracy
• Analyze trends and patterns while evaluating data, and interpret it for specific goals.
• Work with software developers and machine learning engineers to put analytical models into production.

Key Skills Required:

• Five or more years of experience in the field of data science.
• Good knowledge and understanding in the fields of mathematics, statistics, and data mining.
• Advanced proficiency in pattern recognition and predictive modeling
• Experience in programming languages (such as SAS, Java/Python), Excel, PowerPoint, Tableau, and SQL
• The ability to operate effectively in a dynamic, research-focused team that is working on several projects at once

Job Outlook for Data Science

Demand for skilled Data Scientists, Data analysts, machine learning engineers, and AI experts is high across industries as companies depend more and more on data-driven insights. There are about 164,000+ jobs available in the USA (Linkedin) and 82,000+ in India (Linkedin). This industry is expected to grow by 32% by the end of 2027. 

You can start your career in data science by doing an internship in a field that interests you the most. For non-technical fields, you can work as a junior data analyst or junior business analyst. For technical fields, you can work as a fresher in a software development position.

With relevant experience and skill set, a person can advance to the top of the data science hierarchy.

Further resources on data science:

• Differences between data science and business analytics
• How to get your first data science internship

Skills and prerequisites to become a Data Scientist 

Here are some of the top data science skills and prerequisites to become a top data scientist in 2023:

1. Academic Requirements: For Data science academic prerequisites an undergraduate or postgraduate degree in Computer Science, Mathematics, Statistics, Business Information Systems, Information Management, or any other similar field is required.
2. Programming skills: A good understanding of various programming languages, such as Python, Perl, C/C++, SQL, and Java, with Python is required.
3. Mathematics and Statistics: To solve complex problems and execute the desired output a strong command of statistics and mathematics is required in this field.
4. Data Analysis & Visualization: A data Scientist must know the use of popular data analytics and visualization tools like Tableau, Power BI, Qlikview, and D3.js.
5. Web Scraping: One must know how to extract valuable data such as text, images, videos, customer reviews, surveys, polls, and other valuable information from the web. 
6. Data Extraction, Modification, and Loading: One must know how to collect, cleanse, and prepare the data for analysis.
7. Big Data: A Data Scientist must know how to use Big data to gather, store, extract, analyze, and evaluate practical data from various data sets. Knowledge of tools like  KNIME, RapidMiner, Hadoop, Spark, and Integrate.io is a must. 
8. ML with AI and DL with NLP: A data Scientist must know the fundamentals of machine learning (ML) with artificial intelligence (AI) and deep learning (DL) with natural language processing (NLP).
9. Problem-Solving Skill: When necessary, one must be able to recognize problems and come up with effective solutions.
10. Internships or Industry Projects (Optional): Internships or Industry Projects offer practical experience and can increase a candidate’s competitiveness in the job market.

Learn about the steps to start and grow your career in data science.

The Future of Data Science — Will AI Replace Data Scientists? 

Data science has a bright future worldwide. It is one of the most in-demand careers in the technology field. The data science platform market is evaluated to grow at a CAGR of 26.78% between 2022 and 2027. The size of the market is predicted to increase by USD 249,147.71 million.

If you’re wondering whether AI Replace Data Scientists…

The answer is no, AI will not replace data scientists. While AI can automate some data science tasks, such as cleaning and organizing data, it lacks the originality and creativity that only humans possess.

There is no doubt that AI technology will help in data science progress. However, it will not replace data scientists and analysts in the future. It might change the work process of data science making data scientists and engineers work at ease without replacing them.

With the help of AI, some repetitive tasks can be automated and many other components will be improved or optimized, helping analysts to do better work in less time..

Data scientist salary/ How Much Do Data scientists Make? 

The average annual salary of a Data Scientist in the US is $152,277/yr. Likewise, on average, a Data Scientist in India makes ₹9.4 Lakh annually.

The average salary in the field of Data science varies in different countries and cities depending on work demand.

Here are some average annual salary of data scientists in India by city:

Here are some average annual salary of data scientists in US by city:

How Difficult is Data Science?

At first, data science might seem a little challenging to start in this field. However, with dedication, concentration, and a solid learning roadmap you can master data science skills within a year.

Starting with Data science might be difficult for some students from non-technical fields because it requires a strong background in math, statistics, and computer programming. However, You can start your foundation with technical and programming basics like Python and R with online courses, YouTube tutorials, and Blogs.

Engaging with data science communities and forums can be really helpful as they are very helpful in getting online support, advice, and networking opportunities. You can ask questions and seek guidance through these communities. Attending data science meetups, conferences, and webinars can also help you get exposure to your real-life data science journey.

The following YouTube video about data science career roadmap will be useful you to get started:

Is data science career path a right fit for you?

Take the following quiz to self assess whether data science is the right career path for you or not based on your current skills, interest, and preferences.

NOTE: This process is not the most effective way to decide your career. However, you will get a quick understanding of your interests and current skills.

Data Science career path Reddit— What others are saying on Reddit?

I analyzed Reddit to find what data science experts and employees are saying on the possible career paths.

Here’s what I found:

Someone asked on Reddit that: What are the potential career paths for Data Scientists after 3 years?

In response, a data scientist outlined three main career paths for data scientists on Reddit: 

Advancing within Data Science, Switching to Engineering roles, and transitioning to “Business”-oriented positions. It emphasizes the flexibility of data science careers, allowing professionals to shape their roles based on their interests.

The answer also highlights specific roles within each path, underlining the interdisciplinary nature of data science and its potential for social impact, entrepreneurship, and consulting. Overall, it provides a concise and informative overview of the varied opportunities available to data scientists.

Here’s another response.

This Reddit thread on data science career progression, I found some interesting career roadmap to become a data scientist from high school degree.

Ted Petrou’s journey into data science is a unique and eclectic one. Starting with a childhood fascination for math and statistics, they navigated through various career paths, including professional poker playing and teaching.

Their progression into data science, followed by a shift into teaching, reflects a passion for coding, analysis, and data visualization. This diverse background has enriched their data science career, despite an occasional longing for the excitement of trading.

Here’s another Reddit user sharing his data science career roadmap so far:

Data Science course certifications to start your career 

Find the top data science courses available with certification program in US.

1. IBM Data Science The writer Professional Certificate

This program includes Python, SQL, data analysis, machine learning, and data visualization skills and some real-world projects. This is a 5-month program that includes 10 course series that you can enroll in for Free.

2. Google’s Data Analytics Professional Certificate

This program includes critical analytical skills and tools where you will learn how to visualize data insights in dashboards, presentations, and popular visualization platforms. This is a 6-month program that includes an 8-course series that you can enroll in for Free.

3. Microsoft Certified: Azure Data Scientist Associate

This Microsoft certification focuses on the ability to apply machine learning to set up and run machine learning workloads on Azure. This exam will cost you around $165.

4. Harvard’s Professional Certificate in Data Science

This program provides Strong training in statistical modeling, machine learning, optimization, management and analysis of enormous data sets, and data collecting. This is a 1.5-years program that includes 9 skill-building courses. This curriculum includes practical research projects and costs around $991. 

5. Data Science: Machine Learning

This is the data science course by Harvard to learn about component analysis, regularization, and machine learning techniques. This is an 8-Weeks course that will cost you around ₹9,065.

Article by

Samruddhi Chaporkar

Samruddhi is a Technical Content Writer of science and technology with an academic qualification in Bachelors in Computer Applications (BCA). Her love for writing and knowledge of computer science helps her write about trends in the technology industry that help people understand the ins and outs of the world of technology.

LinkedIn | Twitter | Author page

Cybercrime is not an absurd threat but is being committed massively. Maintaining a healthy cyber security culture is essential for organizations in this case.

This guide will help you learn more about ethical hacking, Cyber security, and cyber ethics and their distinct differences in detail.

You will also learn about different career opportunities and the required skills for these fields.

What is ethical hacking? 

Ethical hacking is the process of testing the security of computer systems. This enables “Ethical Hackers” to detect security flaws in a company’s system and fix them before hackers find and take advantage of them. 

Let’s say we launch an app and install all the necessary security updates. We still need to confirm this application’s security. We will run specific tests to ensure the application is secure and no one can get around the security measures. This process is a simple example of ethical hacking.

Ethical hacking is a part of cyber security and a catchphrase for penetration testing. Ethical hackers, also called “white hats,” are security experts who legally carry out these security evaluations.

What is cyber security? 

Cybersecurity is the process of protecting computer networks, systems, and programs from hackers’ attacks. It is a collection of several skills and tools that combine to create a potential security environment for users.

We have seen two-factor authentication for account access or password changes in many applications and websites. With this, your account becomes more secure and prevents unauthorized access.

Now consider the malware detection software you set up on your computer. The software alerts you right away whenever it discovers any malicious activity. The goal in both cases is to make your device more secure.

There are primarily four distinct phases of cyber security:

1. Identify
2. Protect
3. Detect
4. React

Relevant read: Learn more about cybersecurity domains and their differences.

Differences between ethical hacking and cyber security 

Here are key differences between ethical hacking and cyber security based on various key factors:

1. Basic Concept

1.1 Cybersecurity

Cybersecurity uses technology, processes, and policies to protect systems, networks, programs, devices, and data against cyberattacks. It aims to reduce the risk of cyberattacks and protect against unauthorized systems, networks, and technology usage.

Find out important cybersecurity seminar topics 

1.2 Ethical Hacking

It is a technique where a professional hacker attempts to break an organization’s security systems and devices legally and consciously. Ethical hackers test the company’s security with different tools and methods to improve system security and find any flaws in the company’s systems and networks.

2. Methods Used

2.1 Cybersecurity

Network security, cloud security, IoT (Internet of Things), and application security apply to critical systems. Systems, networks, and other devices can be successfully secured using these techniques.

2.2 Ethical Hacking

Hacking, sniffing, social engineering, inserting SQL data, hijacking, leaving a digital footprint, enumeration, and cryptography

These hacking techniques help moral hackers more securely safeguard networks and systems. When used by a skilled hacker to protect systems, these techniques are more effective because they are more dependable and use the most recent upgrades to protect systems and networks.

3. Responsibilities

3.1 Cybersecurity

The responsibilities of a cyber security expert include safeguarding the system by creating access privileges, control mechanisms, and resources. Finding anomalies and noting breaches that help to discover problems. Enforcing security measures by evaluating the current environment, trends, and forecasting requirements.

3.2 Ethical Hacking

The responsibilities of an ethical hacker include visiting clients, checking the security measures in place, looking into the network structure and potential entry points of the company, conducting several penetration tests on the system, and identifying and documenting security flaws and compromises.

4. Process

4.1 Cybersecurity

There are five major steps in cyber security procedures. Each step is just as important as the next, and having a clear roadmap makes it easier for cybersecurity analysts to fulfill their duties. The following are the stages in the cyber security procedures:

Determine the Assets → Secure the Assets → System Monitoring → Resolve Incidents → Recovery

4.2 Ethical Hacking

Ethical hackers must put in a lot of effort and skill to find and take full advantage of every vulnerability. This simulated attack aims to find any weak points within the organization and work to strengthen them. The following are the steps in an ethical hacking process:

Reconnaissance → Scanning → Acquiring Access → Maintaining Access → Wrapping of Traces. 

5. Job roles

Here we have a breakdown of the differences between cybersecurity and ethical hacking job roles:

5.1 Cybersecurity Job Role

• Cybersecurity experts should identify security violations and inefficiencies by carrying out routine checks.
• Establish and maintain security measures. It also includes upgrading infrastructure and networks.
• A cybersecurity expert should give appropriate access rights to increase system security.
• Cybersecurity professionals must describe the effects of damaging attacks on the company.
• Should maintain quality service by following organizational standards.
• Keep up with market trends and Maintain technical knowledge by attending various educational workshops and certifications.
• Contribute to the team effort by completing crucial tasks as required.
• Professionals must examine hardware and software for flaws and hazards. They are in charge of controlling and keeping an eye on any invasions and assaults. The specialists must locate a potential threat or attempted breach and fix the security flaw.

5.2 Ethical HackingJob Role

• An ethical hacker evaluates the functionality of a system by testing it for various security flaws.
• The specialist must evaluate the company’s security system and suggest recommendations for enhancements.
• Ethical hackers should conduct regular pen tests on the system, online application, and network to check for security flaws.
• After identifying vulnerabilities, ethical hackers should create reports, and after the problems have been fixed, they should provide comments.
• Ethical hackers should conduct regular pen tests on the system, online application, and network to check for security flaws.
• After identifying vulnerabilities, ethical hackers should create reports, and after the problems have been fixed, they should provide comments.
• Experts in ethical hacking must inform the company about how the attack might hurt its customers and operations.
• Ethical hackers should use hacking techniques to suggest solutions to the system’s flaws.

As cyber threats evolve, the role of automation and artificial intelligence in security is also growing. A detailed comparison of how artificial intelligence is used in defense and how attackers adapt is covered in our guide on AI vs hackers.

Required skills to become an ethical hacker

1. Computer Skills

An ethical hacker must know about computer systems and networks. Managing databases, programming, and performing calculations in spreadsheets are examples of advanced computer skills.

Here are a few of the most crucial computer abilities an ethical hacker must have:

• MS Office
• Spreadsheets
• Database Management
• Email
• Social Media
• Web
• Enterprise Systems

2. Linux Skills

Linux is a secure operating system that offers customers a variety of tools and features. Linux skills are essential for ethical hacking because it enables you to access a system’s internal workings and spot possible security flaws. Additionally, having Linux skills enables you to write original scripts and programs that can be used to automate various tasks.

3. Cryptography

Cryptography is the process of converting a normal text/message known as plain text to a non-readable form known as ciphertext during transfer to make it incomprehensible to hackers. An ethical hacker must ensure that information within the company is not compromised.

4. Reverse Engineering

It is one of the major skills required in this field. Reverse engineering is the process of determining a product’s design, functional requirements, and configuration from an analysis of its code. It creates a program database and generates data from it.

Reverse engineering aims to create documentation for a legacy system and speed up maintenance work by making a system easier to understand.

Reverse engineering is frequently used in software security to ensure the system is free of major security defects or vulnerabilities.

5. Problem-solving & Critical Thinking

You must be an expert critical thinker and problem solver to be a successful ethical hacker. You must be able to think of creative solutions once you have found a possible security vulnerability. Additionally, you must have excellent communication skills to convey your results to a nontechnical team. 

Required skills to become a cybersecurity expert

1. Understanding of Hacking

You must know how to “ethically hack” to protect your company from cyber-attacks. You must gain the same skills as a hacker to understand how a system might be attacked and find an effective solution.

2. Internet of Things (IoT) Attacks

The Internet of Things (IoT) allows us to stay connected with our devices in different places like home and office. The Internet of Things (IoT) is a network of interconnected devices that includes laptops, tablets, webcams, routers, smartwatches, home appliances, cars, home security systems, and more. The sensitive information could get stolen if the hackers gain control of these IoT environments.

As a result, it is important to ensure that the proper security measures are in place so that IoT devices can be left alone to carry out their tasks automatically without the need for cybersecurity specialists.

Relevant read: Lean about cybersecurity pros and cons.

3. Programming

Your potential opportunities in cybersecurity may be limited if you have no experience with coding.  A basic understanding of programming languages like Java, PHP, C, or C++ is essential for any cybersecurity professional. Understanding computer languages will enable you to identify future system attacks and create the right solution.

4. Cloud Security

Companies seek security experts who can work on public and hybrid cloud platforms like AWS and Azure. More companies are using cloud infrastructure to operate applications and store data. This involves the implementation of policies and technologies that protect cloud-based devices and systems.

5. Blockchain Security

Blockchain’s popularity is growing because of the level of security it provides. Therefore, cybersecurity experts should familiarise themselves with blockchain and how it handles security problems. Think of it as planning for the future.

6. Risk analysis

Cybersecurity experts must identify, control, and reduce risks. Risk management and mitigation skills will be in high demand in the future decades.

7. Information Security

Companies require skilled experts who can protect their electronic data from unauthorized access.

Relevant read: Does cybersecurity engineering require mathematic skills?

Ethical hacking and cyber security: Which one should you choose?

It depends on your interest and goals. Both of these careers required great interest in the field of computer science and information technology. Both Bachelor in Computer science and IT is preferred education for this career.

While cyber security focuses on defending systems from malicious attacks and potential threats, ethical hacking concentrates on using hacking techniques to test the security of computer systems. Ethical hacking might be a better choice if you want to learn more about computer networks and systems.

However, a career in cyber security is better if you’re more interested in defending and protecting computer systems. 

Is hacking under cyber security?

Yes, ethical hacking comes under cybersecurity. Cybersecurity is a vast topic of study.

Data security, digital forensics, ethical hacking, and many other IT and network security techniques are all included in the broad field of cybersecurity. Ethical hacking is also one of its many subjects.

What is the difference between cyber ethics and cyber security? 

Cyber ethics is a field of computer science behaviour that specifies the best practices that a user must follow while using the computer system. In simple words, cyber ethics means morally correct behaviour while using computer systems. Cyber ethics protects individuals’ financial, moral, and social behaviour.

 Whereas, Cybersecurity aims to protect computer networks from cyberattacks and unauthorized access. Cybersecurity is a defensive job that focuses on how to protect the system. To identify security flaws, cybersecurity professionals keep testing the system and upgrading it frequently.

Do ethical hackers need coding?

Yes, ethical hacking requires coding. Programming language knowledge is essential for ethical hackers because they need to analyze code and make changes as per the requirement. Programming is the key skill to keep as a professional ethical hacker.

It takes versatility to be an ethical hacker. Professionals can use the programs more effectively if they have coding skills. Starting with Java and Python can be a good option to become an expert in this field.

What is the salary of ethical hackers? 

According to Glassdoor, an average Ethical hacker Earns up to 5.2 LPA, and a fresher can earn up to 3 LPA in India.

However, it depends on where you work and the type of company you work for. If you work for a startup or small company, your monthly salary will be lower than in companies like Google or Microsoft.

The average Ethical Hacker’s salary in the United States is $90,459, depending on job location, company size, and years of experience.

What is the salary of cybersecurity experts?

In India, an average cybersecurity professional earns up to 12 Lakh annually. Cybersecurity expert can be a good career option in India as many companies provide additional bonuses and Incentives with a basic salary.

An Average cyber security executive can earn up to $94,967 /yr in the United States. 

The salary of a cybersecurity manager in the United States is$1,30,559, and it differs on job location, company size, and years of experience.

Can I learn about ethical hacking in cyber security? 

Yes, but you will only learn the basics of ethical hacking in the study of cybersecurity. That does not mean you can become a hacker with this. Cyber security is an extended topic; you will find subtopics like ethical hacking.

 Anyone with a computer science degree or a basic degree in science background can learn Ethical hacking. You need a basic understanding of digital communications, programming, and computer skills.

With these skills, you can join courses to learn the basics of ethical hacking, such as IoT hacking, cloud computing, SQL Skills, Linux Skills, cryptography and Basic Hardware Knowledge, wireless network hacking, and more.

Article by

Samruddhi Chaporkar

Samruddhi is a Technical Content Writer of science and technology with an academic qualification in Bachelors in Computer Applications (BCA). Her love for writing and knowledge of computer science helps her write about trends in the technology industry that help people understand the ins and outs of the world of technology.

LinkedIn | Twitter | Author page

In this guide, we will learn what these two career paths will look like, the difference between them, and what skills you need to succeed in this career path.

What is Data Science?

Data science is a multidisciplinary field of raw and unstructured data, where it comes from, what it represents, and how it can be transformed into valuable inputs and resources for the business.

Data science uses statistical methods, computer science, machine learning, and mathematics to pass through big data to estimate the unknown by asking questions.

What is Business Analytics? 

Business analytics is the use of data, statistical and quantitative analysis, explanatory and predictive models, and fact-based management to drive decisions and actions.

Data analysis focuses on processing and performing statistical analysis on existing datasets.

Data analytics uses statics in a much broader sense than data science, which helps combine diverse sources of data and find meaningful insight by simplifying the results. 

In this sense, data analytics doesn’t necessarily require in-depth knowledge of the subject and is mostly working on statistics and business intelligence. Still, it can be supplemented by learning the tools needed to analyze data like Power BI and Tableau. 

Differences between Data Science and Business Analytics

Here are key differences between data science and business analytics based on key factors such as:

• Definition
• Statistics
• Applications
• Challenges
• Coding skills
• Tools and languages
•  Data
•  Future trends

1. Definition

Data science: 

Data science is the study of data using technology, statistics, and algorithms.

Business analytics:

Business Analytics uses statistical concepts to extract insights from businesses to solve real-world complex problems.

2. Statistics

Data science: 

Mastering data science involves various techniques and methods such as general statistics, probability distributions, dimension reduction, Bayesian statistics, and more.

This includes tasks such as data cleaning, exploration, and visualization, as well as building models to make predictions or identify patterns.

Data science can be applied to a wide range of industries and domains, such as healthcare, finance, and technology.

Related guide: Data science lifecycle: six stages of data science 

Business analytics:

Business analytics is more focused on using data to inform and support business decision-making. This can include tasks such as forecasting, budgeting, and identifying areas for cost savings or process improvement.

Business analytics is typically applied within the context of a specific organization or industry, such as retail or manufacturing.

Related guide: Data science lifecycle: six stages of data science 

3. Applications

Data science: 

Used in industries like technology, finance, internet-based, and academic.

For example, a data scientist may use machine learning to develop a predictive model that can identify patients at high risk of developing a certain disease or use natural language processing to analyze customer feedback and identify key themes.

Business analytics:

Used in industries like technology, fintech, technology, CRM/marketing, and retail. 

This can include tasks such as forecasting, budgeting, and identifying areas for cost savings or process improvement.

In short: Business analytics is typically applied within the context of a specific organization or industry, such as retail or manufacturing.

4. Challenges

Data science challenges: 

• Not being able to use the Results in organizational decision-making
• Decision-makers in business do not use the results of data science
• There is a lack of clarity about the questions that must be answered using the given data set
• Data not being available and could be difficult to access
• Coordination with IT is required

Business analytics challenges:

• Lack of strong domain experts’ input.
• soiled data
• Data not available or difficult to access
• Privacy concerns about not having enough money to purchase useful data sets from outside sources

• fail to integrate findings into organizational decision-making
• Uncertainty over the questions that the available data set should be used to address.
• Tool limitations
• Coordination with IT is required

5. Coding skills

Data science: If you want to become a data scientist, then having a solid grip on coding skills is necessary. This includes learning programming languages, libraries, and frameworks to perform data cleaning, exploration, visualization, and modeling.

For example, a data scientist may use Python to perform data cleaning and exploration, libraries such as pandas, NumPy, and scikit-learn, or use R to perform statistical modeling.

Related guide: Required skills, roles & responsibilities of data science

Business analytics: Coding skills are also required in the business analytics field. However, the primary focus here is tools and technologies used for business applications.

For example, a business analyst may use Excel and VBA to perform data analysis projects, create reports, or use SQL to retrieve and manipulate data from databases.

Related guide: Required skills, roles & responsibilities of data science

6. Tools and languages

Required tools and languages for data science: 

• Python
• R
• Matlab
• Pandas
• Matplotlib
• Numpy
• Keras
• Tensorflow
• PyTorch
• Sci-kit-learn
• GraphQL)
• Apache Spark
• SQL (ex-MySQL, PostgreSQL, Oracle)
• NoSQL (MongoDB, Cassandra DB, 
• Jupyter/Spyder Notebook
• Natural Language Toolkit

Required tools and languages for business analytics :

• Excel
• Tableau
• Power BI
• SQL
• Python
• Dash
• SAS
• Pencil
• JIRA
• Oracle Analytics Cloud

7. Data

Data science: Both structured and unstructured data is required in this field.

Business analytics: Mostly structured data is required and used.

8. Future trends

Data science: Artificial Intelligence and machine learning are future trends in this field.

Business analytics: Cognitive analytics and tax analytics are future trends in this field.

Related read: Discover the latest technology trends and their career opportunities

Key required skills to become a data scientist

The following are some of the most crucial technical and non-technical data scientist skills:

1. Mathematics and Statistics

You don’t need any prior knowledge of mathematics to begin learning data science, but without understanding some mathematical and statistical ideas, you won’t improve in your career.

Building solid data models, selecting and utilizing the various data techniques available, and properly understanding the data you are working with all require a solid understanding of statistics.

You should spend some time learning the fundamentals of calculus, probability, statistics, and linear algebra in addition to the very fundamentals of math given in a normal school curriculum. If you deal with AI and machine learning approaches, knowledge of Bayesian theory is also helpful.

2. Programming

It’s all meaningless if you don’t know how to Code. Therefore, in order to modify the data and apply various sets of algorithms as and when necessary, working as a data science expert would demand you to be familiar with a number of programming languages. However, there are a few important languages that data scientists frequently use, and more importantly, the recruiter will demand that you know these languages. 

The list of programming languages is as follows:

• Python
• R Programming
• SQL
• Scala

3. Big Data

Relying simply on Python or R may not be adequate when it comes to processing large amounts of complicated data quickly. The rapidly expanding tools and technology that make up the big data ecosystem enable more efficient, scalable, and reliable big data analysis. These activities include everything from ETL procedures and database maintenance to real-time data analysis and activity scheduling.

4. Machine Learning and Deep Learning

One of the hottest subjects in data science is machine learning. A subfield of artificial intelligence called machine learning is concerned with creating algorithms that can learn to carry out tasks without being explicitly programmed.

When hiring or promoting data scientists, having knowledge of neural network theory and practice is a game changer. Deep learning, however, is a challenging field that requires highly developed math and computer skills. Deep learning specialists are among the highest paid in the data science sector because of this.

5. Data Visualization

A key requirement of being a data scientist is being able to effectively communicate important ideas and gain support for suggested solutions. One ability that every data scientist will need to be adept at in order to grow professionally is understanding how to break complex data down into smaller portions as well as utilizing a number of visual tools (charts, graphs, and more).

6. Great Data Intuition

Large data sets may not necessarily provide valuable data insights; a skilled data scientist has intuition and knows when to dig further for useful data insights. This increases the productivity of data scientists, and it can be learned with practice and appropriate education. But experience is necessary to develop this data scientist skill, and boot camps are a terrific way to do that.

7. Strong Communication Skills

Communication is the next skill on the list of important data scientist skills. Data scientists are skilled in extracting, comprehending, and analyzing data.

However, you must be able to effectively explain your results to team members that come from different professional backgrounds if you want to succeed in your position and help your organization.

8. Strong sense of business

Technical skills can be most effectively applied when combined with a strong business sense. Without it, an aspiring data scientist might not be able to identify the issues and the difficulties that need to be overcome for a company’s growth.

This is important for helping the company you work for in exploring different business opportunities.

Responsibilities of a data scientist

• Identify useful data sources and automate data collection procedures
• Preprocess data, both structured and unstructured, and analyze a lot of data to find patterns and trends
• Create machine learning algorithms and predictive models
• By using ensemble modeling, combine models
• Use data visualization tools to present information
• Bring up strategies and solutions for corporate problems
• Work together with the product development and engineering teams
• Data collection and source identification
• Analyse massive amounts of data, both structured and unstructured
• Create strategies and solutions for corporate issues
• Use a variety of algorithms and modules to identify trends and patterns

Want to start your data science journey? Check out these courses ranked by placement assistance, reviews, and pricing.

Required skills of a business analyst

1. Domain Knowledge

The first stage in a business analyst’s career is understanding the business Domain, which is a unique skill of its own. Business analysts should have domain knowledge of the company they are working for. This will help them in completing the required targets. Business analysts typically strive to bring about a change with the aim of boosting sales, accelerating manufacturing, raising revenue streams, etc.

2. Analytical Skills

Business analysts focus on learning about and addressing the needs of the client. They are able to prioritize business requirements thanks to critical thinking. Before choosing the desired solution, a business analyst should evaluate a number of options with the use of critical thinking and Analytical Skills.

A good analytical bent of mind will allow a business analyst in achieving the stated goals even when resources are limited and conditions are less than ideal.

3. Microsoft Excel

Business analysts execute several calculations, data analysis, and budget analysis using Excel, one of the most established and efficient analytics and reporting tools.

Excel is used to organize an editorial calendar, list expenses for items, produce charts to indicate how near the product is to budget across each category, and create revenue growth models for new products based on current consumer estimates.

4. SQL

Business analysts use structured data the most frequently. They should be well-versed in relational databases like Microsoft SQL Server, MySQL, and Oracle DB, as well as NoSQL databases in order to store and process this large amount of data.

Every business analyst needs to have practical SQL experience. They can access, retrieve, manipulate, and analyze data with the use of this.

They must create, update, insert, delete, select, and other data definition and data manipulation commands.

5. Programming

To execute data analysis more quickly and effectively, business analysts should have practical programming skills. Learning R and Python is quite helpful. Writing effective codes can be used to resolve complex issues. Several tools and packages for data wrangling, data manipulation, data visualization, and analytics are available in R and Python. Additionally, proficiency with statistical programs like SAS and SPSS is recommended.

6. Negotiation Skills

Business analysts bargain during every stage of a project. Negotiation skills are used in the early stages of a project to determine what must be included in the project’s vision.

After that, business analysts utilize their negotiation skills to decide which requests become requirements and what priority they should be given. Negotiation abilities are essential when determining the functional design that meets the criteria as the project moves forward.

Technical decisions are also made through negotiation techniques.

7. Data Visualization

For the purpose of developing reports and dashboards, business analysts need to be skilled users of a variety of business intelligence and data visualization tools. To help with decision-making, business analysts create general reports and dashboard reports.

To create various reports based on business needs, solid knowledge of Tableau, QlikView, and Power BI is required.

8. Strong Communication Skills

Understanding is important, but so is being understood.  A data analyst should be able to explain the needs to clients and stakeholders in a clear and simple manner.

At various stages of a project, such as when it is being launched, while collecting and analyzing data, when working with stakeholders, while validating the final solution, and so on a business analyst needs communication and interpersonal skills.

Responsibilities of a business analyst

• Monitoring corporate processes, witnessing needs, creating, and implementing solutions
• Keeping up with new technology to automate tasks and boost productivity
• Examining corporate procedures and giving management and other team members feedback
• Forecasting and planning
• Coordinating initiatives while keeping a continuous eye on needs in charge of pricing
• Carrying out comprehensive market research and competitive analysis
• Analyses and Reporting regularly
• Managing overlapping resources and priorities while maintaining frequent delivery checks
• Overall project management
• managing and leading teams

Data Science and Business Analytics: Which one should you choose?

Both Data Science and Business Analytics are equally hot in the market. It depends on what you want to pursue soon after receiving your degree in the near future. 

If you are from a management background business analytics is a suitable career option as it requires less coding. Getting a master’s in  Business Analytics will be the best option to start your career in this field.

Technical background students can go for data science as this field requires lots of coding. You can go for a master’s in data analytics. This will help you in your career as a Data Scientist. 

Can a business analyst become a data scientist? 

Yes, a business analyst can become a data scientist. It is an ideal role for someone who wants to become a data scientist.  For this, you need to Brush up the skills like statistics and maths. Learn to code in languages like Python and R. 

If you have some coding knowledge under your belt, try taking them to the next level.  Taking Tests and quizzes and joining groups and the data science community would certainly help you in this case.

Can I change from business analyst to data analyst? 

Yes, with proper training in business, analysts can switch to data analysis. For a smooth transition into these professions, individuals must be comfortable with coding and programming.

Business analysts and data analysts are two different sides of the same coin. Business analysts are more involved in addressing business issues and making recommendations with business intelligence, whereas data analysts work more directly with the data itself. 

Final Words 

Data analysts and data scientists have job titles that are deceptively similar given the many differences in educational requirements, roles and responsibilities, and career trajectories.

Both Data science and data analysis have great job opportunities. Still, when it comes to career growth, data science is the best option as it has different types of Data Science Jobs options including data engineer and Database Manager. On the other hand, career growth options are yet to be discovered in the field of business analytics.

Article by

Samruddhi Chaporkar

Samruddhi is a Technical Content Writer of science and technology with an academic qualification in Bachelors in Computer Applications (BCA). Her love for writing and knowledge of computer science helps her write about trends in the technology industry that help people understand the ins and outs of the world of technology.

LinkedIn | Twitter | Author page

‘Does data science require coding?’

Or,  ‘Do I need to master Mathematics to be a data analyst?’

In this blog, we will answer all the questions and queries of a beginner who wants to start or planning to start a career in data science and machine learning.

Quick Navigation

• What is data science?
• Does Data Science require Coding?
• Which Job Profiles in Data Science require Coding?
• Which Programming Languages are required in Data Science?
• Data science career path
• Skills Required To become a data scientist
• Final Words

What is Data Science? 

Data science is the field of study and analysis of Data and Datasets. It’s a process where so many tools and algorithms are applied to collect and analyze data, detect the pattern and find out the insights or results from the data.

Anyone can learn about the various tools, algorithms, and methods of machine learning used in the field of data science to find new and useful patterns from the raw data.

Does Data Science require Coding?

Yes, data science requires coding and experience with technical tools and technologies. A data scientist doesn’t need to be a master in programming but basic knowledge of programming is required to understand functions and algorithms on the project they are working on.

Most of the time machine learning logic has already been implemented by programmers who are experts in that area. Data scientists are experts at finding patterns in data using machine learning algorithms. They must deeply understand the operations of the functions and how to implement them into a script or application.

Which Job Profiles in Data Science require Coding?

There are different levels of coding needed for each position, depending on the role you choose. However, learning the principles of one coding language and one querying language would be an excellent place to start. Understand that Google is your best buddy, Google is here to assist you in every possible way when it comes to real-world coding.

Let’s discuss some of the jobs and the amount of coding needed in this section:

1. Business Analyst 

This is a role that requires less coding, depending on the company you are applying for. learning the foundations of SQL and data visualization like Power BI or Tableau will be required in this field. Any bachelor’s degree is the initial requirement to be an entry-level data analyst.

The business Analyst role requires strong analytical skills to examine documents, data, user surveys, and workflows to provide problem-solving solutions.

2. Data Engineer

Data Engineer’s Job is to prepare data for analytical or operational uses. To improve data as required, a data engineer would need to be an expert in SQL or a data query language and have a solid understanding of Python and R. Bachelor’s degree in computer science or a related field is the initial requirement to be a  Data Engineer.

A data engineer will become a specialist in a cloud platform like Microsoft Azure, Google Cloud Platform, or Amazon Web Service (AWS) Over time. Getting certified on these cloud platforms can help you get started and move along the career path for data engineering more quickly.      

3. Machine Learning Engineer

A machine learning engineer needs to be skilled in coding languages like Python and R. they also need to have a solid understanding of querying languages like SQL. The Bachelor’s degree in computer science or a related field is a valuable addition to this role.

4. Data Scientist

A data scientist needs to know everything from programming and technical skills to all non-technical skills in this field. Data scientists must continue to learn throughout their careers, regardless of platform, coding language, tools, or technology. A Bachelor’s and Master’s in the field of Computer science, Maths, or a related field is necessary to be a data scientist.

 If you are attempting to go into data science, this can be intimidating. However, what companies are looking for is someone who has a basic understanding of a language and is motivated to learn.

5. Technical writer for data science software

This job profile does not require any coding knowledge. A technical writer for data science software needs to have a good understanding of the software, data science techniques, processes, applications, user psychology, and behavior.

Technical writers have challenging jobs. It is not only the content that must be developed and correct, but also the design, and legal criteria that must be met. They must understand data science and software and collaborate with users, developers, and marketing.

6. Data science project manager

Not all data scientists make good project managers. So, people hire project managers to look after large projects and coordinate with all involved stakeholders. You do not need programming knowledge to be a project manager.

A project manager for data science plans, designs, and implements data science solutions. They monitor the project’s development and evaluate the risks. A project manager must escalate problems as they occur and find solutions.

As a Data Science project manager, you must possess business domain expertise, end-to-end data science abilities, a structured project management approach, and the capacity to manage people.

Which Programming Languages are required in Data Science?

There are many languages you can master to start your data science career bellow are some most common and important languages that you should learn with your bachelor’s:

1. Python

It is one of the most widely used and open-source languages. It is object-oriented and supports multiple paradigms, from functional to structured and procedural programming. 

It is a faster and better option for data manipulations with less than 1000 iterations. With the help of the Python packages, natural data processing, and data learning become simple tasks. Additionally, Python creates a CSV output that makes it simpler for programmers to read the data in a spreadsheet.

2. R

R is a high-level programming language typically used for statistical computing and graphics. R is useful for exploring data sets and performing ad hoc analysis, the loops contain more than 1000 iterations. Although it is more difficult to understand than Python.

3. JavaScript

JavaScript is the most popular and easy-to-learn programming language. This flexible language can handle numerous tasks at once. Everything from electronics to desktop and web applications can be embedded with this language.

Java is used by popular processing systems like Hadoop. Furthermore, it is one of those data science languages that is quick and simple to scale up for large applications.

4. Julia

Julia is a rising star in the field of data science. Julia has made an impression on the field of numerical computing despite being one of the more recent languages on this list ( launched in 2011). When compared to other languages for data analysis, Julia sometimes referred to as the inheritor of Python, is a powerful data analysis tool when compared to other programming languages.

Despite its popularity as a result of its early adoption by several major organizations, including several in the financial industry, Julia lacks the maturity to compete with top data science languages. It still has a limited community and fewer libraries than its primary rivals, Python or R.

Data science career path

If you are thinking of becoming a data scientist or planning to start your career in data science. You would have always wondered how long does it take to learn data science?

These are some steps that will help you to start your journey from the beginning.

Data science is a field that required a strong background in mathematics and computer science, you need to understand that data science required strong knowledge and skills.

1. Get a Bachelor’s Degree     

For any beginner who wants to start their career in data science, a bachelor’s in computer science or a related field is necessary. You can go for any bachelor’s that includes maths, stats, and programming.

2. Skill yourself 

Data science is a competitive field and requires skilled professionals to be in this job role. Sharpening your skill and being updated is all you need to have a successful career in this field.

Programming knowledge of important languages, machine learning techniques, data visualization, and reporting, effective communication, and risk analysis are some skills you need to master.

Books and Podcasts are the easiest way for any beginner to understand any subject. Reading books about data modeling created by professionals is the best course of action if you want to learn more about it.  Check out these guides where we’ve curated the best data modeling books, computer science podcasts, and the top data science courses in th US.

3. Earn a certification

Getting certifications in  Microsoft Azure or Google Cloud Platform can help you a lot to begin your career in data science. All these certifications are free and available on the respective sites of these big tech giants.

For the start, you can also get free certification in data science tools like Tableau and Power bi.

4. Get a job or Internship 

You can easily apply for an internship as a data engineer or business analyst based on the above certification and skills. The certifications and internship can add extra wattage to your resume in the data science career

Getting an entry-level job is easy if you have knowledge and skills. With preferred skills and experience can get you a high-profile job in your career.

5. Take Further education

Though you have completed your Graduation and initiated your career in this field, there is no end to learning. In data science or any technology career, you constantly need to update yourself and learn new things to be in the race.

You can take further education in these:

• Master of Computer Science in Data Science.
• Master of Data and Network Analysis.
• Master of Science in Data Science.
• Master of Applied Data Science.

Skills Required To become a data scientist

Apart from programming languages, there are some technical and non-technical skills you need to master before starting your data science career:

1. Probability & Statistics

Probability & Statistics are the major part of a study of data science. Probability & Statistics helps to Explore and understand more about the data and understand patterns or motives of the data.

Probability, when combined with statistical methods, aids in the development of estimates for further analysis. The theory of probability serves as the foundation for most statistics. Simply said, the two are interconnected.

2. Database Management

In data science, 80% of work goes into collecting all the information suitable for processing in a business environment. Database management is generally made up of a collection of programs that can modify, index, and manipulate databases. The DBMS takes a data request from an application and informs the OS to supply the precise data that is requested. 

Database Management helps Data Science by defining guidelines for data entry, validation, and testing, operating on the database’s record level. Manage, define, and retrieve data in a database. MySQL, SQL Server, Oracle, IBM DB2, PostgreSQL, and NoSQL databases (MongoDB, CouchDB, DynamoDB, HBase, Neo4j, Cassandra, Redis) are some popular DBMSs.

3. Data Visualization

Data sets are available in large amounts and understanding and organizing different types of data is more difficult. Data Visualization helps in sorting and understanding large useful data in a way that even a non-expert person can understand.

You may display your data using a wide range of tools, including histograms, bar charts, pie charts, scatter plots, line plots, time series, relationship maps, heat maps, geo maps, 3-D plots, and many more.

4. Machine Learning / Deep Learning

Machine learning is an essential skill for a data scientist. Predictive modeling is done using machine learning. For example, if you want to calculate the number of clients you will have in the upcoming month based on the previous month’s data, you will need to apply machine learning techniques.

Practicing problem statements is the most effective technique to learn machine learning. There are many websites that offer a variety of practice problems that you can work on at any time.

5. A Strong Data Intuition

This is possibly one of the most important non-technical data scientist skills. Large data sets may not necessarily provide valuable data insights; a skilled data scientist has intuition and knows when to search further for useful data insights. This increases the productivity of data scientists, and it can be learned with experience and the appropriate education.

6. Strong Communication Skills

Data scientist knows how to work with data, but apart from that they data scientist should have excellent communication skills. You should be able to effectively convey your insights to team members who do not share your professional experience.

Final Words

Data science is a field that requires mathematical and programming knowledge. In this article, we have discovered all the job profile that requires coding from beginner to expert level. We have also found some jobs that do not require coding language expertise.

This career is difficult for any non-technical person as it requires a lot of hard work, technical knowledge,  and determination.

We suggest you go for the data science field only if you have an interest in subjects like maths, statistics, and programming.

Article by

Samruddhi Chaporkar

Samruddhi is a Technical Content Writer of science and technology with an academic qualification in Bachelors in Computer Applications (BCA). Her love for writing and knowledge of computer science helps her write about trends in the technology industry that help people understand the ins and outs of the world of technology.

LinkedIn | Twitter | Author page

But what is Artificial intelligence and how important is this thing? 

In this article, we will get a closer look at Artificial intelligence, and its types. There are various career opportunities in this field which we will also discuss in this article. 

What is Artificial Intelligence?

Artificial Intelligence or AI is the science of building machine intelligence that is capable of thinking like humans. It uses Algorithms and methods to build machines that can perform human-like tasks.

Who is the father of Artificial Intelligence?

The father of artificial intelligence is John McCarthy, who organized the mythic Dartmouth conference In 1956, where he first used the term “artificial intelligence”, defined as the science and engineering of making intelligent machines.

Types of Artificial Intelligence

Artificial intelligence is majorly divided into two categories, which are based on capabilities and based on functionality.

Artificial Intelligence based on capabilities:

1. Weak AI or narrow AI
2. General AI
3. Super AI

Artificial intelligence based on functionality:

1. Reactive machines
2. limited theory
3. Theory of mind
4. Self-awareness

Related article: Handpicked Artificial Intelligence seminar topics for your presentation

Branches of Artificial Intelligence

Artificial intelligence is an expanding field with many branches and sub-branches. Let’s take a deep dive into them.

1. Machine learning

Machine learning is a branch of artificial intelligence (AI) and Computer Science, which is defined as the ability of machines to learn automatically from data and algorithms, Improve their performance from past experiences and make decisions without being explicitly programmed.

The machine-learning process begins with a collection of historical data such as direct experience or instructions. With this data, it builds logical models for future inference. Whenever it receives new data It predicts the outcomes with the help of this model.

 The accuracy of the output is based on the amount of data, as a large amount of data helps to build a better model which increases the accuracy of production.

Machine Learning algorithms can be further classified into three types:-

• Supervised learning is a method in which we use Labelled data to train machines and on that basis, it predicts the output. Labeled data is the data where output is already known to you. The goal of this model is to map the input to the output. An example of Supervised learning is the algorithm used to train the machine that identifies images of animals.

• Unsupervised learning is a machine learning technique in which we use data that is not Labeled. Unlabelled data means there is no fixed output variable. The model extracts data from the input, identifies patterns and features, and then generates the output.

•  Reinforcement learning is a technique that uses a model of agent and environment to produce actions and rewards. There is no predefined target variable in this learning model.

2. Neural network or deep learning

Artificial Neural network, also known as deep learning, is a branch of artificial intelligence (AI) and sub-branch of Machine learning (ML) that can be used to solve more advanced problems. The algorithms of  Neural networks are inspired by the structure of the human brain.

This network works similarly to the human brain. The Neural networks take in data and train themselves to recognize patterns and then predict the output for a new set of similar data. These networks are trained to detect the patterns and predict the most accurate output. 

An artificial Neural network is a complex, integrated system that can perform analytics much deeper and faster than human capability. There are different types of neural networks, often best suited to different purposes and target outputs. 

Face verification and Recognition algorithm are one of the most common and well-known Examples of  Artificial Neural network and deep learning which is commonly used in Meta, self-driving cars, and virtual assistants like Siri, Alexa, and so on.

3. Expert system 

An expert system is a computer system that uses artificial intelligence (AI) to mimic the decision-making and behavior of a human or an organization who have knowledge and experience in a certain field. This system is not designed to replace human professionals instead, it is used to assist experts in making complex decisions.

Like a human expert, the system aids in decision-making for complex issues by using both facts and heuristics. It possesses in-depth knowledge of a certain field and is capable of resolving any challenging issue in that field. These systems are created for certain fields like science, medicine, etc.

The whole process of an expert system mainly revolves around three components: User Interface, Inference Engine, and Knowledge Base. 

The user Interface (UI)  takes a query from the User and passes it on to the Inference engine.

Inference engine work as the brain of the system. Which concludes or infers new information by using the inference rules to the knowledge base. It helps to determine an error-free solution to user requests.

The knowledge base is similar to a database that holds data and instructions for a specific field or subject. The Expert System will be more accurate if the knowledge base is more extensive.

4. Robotics 

Robotics is the branch of artificial intelligence and engineering. The main aim of robotics is to design and develop robots.

The main goal of AI in the field of robotics is to develop Expert Systems and  Implementing Human Intelligence in Machines which means creating systems that understand, learn, think, demonstrate, exhibit intelligent behavior and behave like humans.

The aim of deploying robots is to assist humans with tedious and bulky tasks. Robots are programmed in a way in which they can perform a series of actions automatically or semi-automatically. NASA uses this technology to move heavy objects in space. 

Humanoid robots are one of the most known examples of Robotics and engineering marvels. Sophia from Hanson Robotics’  is one of the most advanced humanoid robots. With neural networks and artificial intelligence, Sophia can recognize human faces and understand their gestures and emotions, and can interact with different people. 

Industrial robots, Medical robots, Mobile robots, Robotic Process Automation (RPA), Chatbots, Autonomous robots, Domestic robots Etc. are other mainstream examples of Robotics applications in everyday life.

Related read: Learn how can DevOps teams take advantage of AI

5. Fuzzy logic 

Fuzzy logic is a branch of artificial intelligence, defined as the technique to solve a problem or statement that is either true or false. The Fuzzy logic method mimics how humans make decisions by considering all possibilities between digital values YES and NO.

Fuzzy logic’s inventor, Lotfi Zadeh, noticed that human decision-making has a variety of options between YES and NO, such as CERTAINLY YES, POSSIBLY YES, CANNOT SAY,  POSSIBLY NO, CERTAINLY NO, and the fuzzy logic works on these levels of possibilities of output.

Fuzzy logic architecture is made of four components:-

• Rule Base
• Fuzzification
• Inference Engine
• Defuzzification

6. Natural language processing

Natural language processing or NLP is a branch of artificial intelligence, Computer Science, and Human language that helps to communicate between computers and humans through natural language.

Natural language is the way we humans are expressing with each other through speech and text. Natural language processing is the process that helps computers understand, analyze, translate and manipulate human language.  

Question Answering, Sentiment Analysis, Spam Detection, Machine Translation, Spelling correction, and Speech Recognition are common examples of NLP applications. 

Natural language processing is further divided into two parts:

1.  Natural Language Understanding (NLU)
2.  Natural Language Generation (NLG)

7. Pattern recognition

It is the process of recognizing patterns using machine learning algorithms. This process can be defined as the classification of data based on previously acquired knowledge or statistical information derived from patterns and/or their representation. The data inputs in this process can be words or texts, images, or audio.

Speech recognition, multimedia document recognition (MDR), speaker identification, and automatic medical diagnosis are some common examples of Pattern recognition technology.

8. Statistical AI

Anyone working in the field of artificial intelligence is familiar with the ideas of probability and statistics as they play a significant role in the field.

Statistical Relational Artificial Intelligence (StarAI) is a combination of both  Logical  (or relational) and probabilistic (or statics) AI.

Relational AI excels at handling complicated domains with a large number of items connected by complex relationships, while the uncertainty that derives from incomplete and noisy descriptions of the fields is managed well by 

statistical AI.

9. Data mining

Data mining is the process of extracting information out of massive data sets to find patterns, trends, and relevant data that would allow the business to make data-driven decisions.

Data mining is also known as Knowledge Discovery of Data (KDD). It mainly transforms unprocessed data into useful information.

Data mining can be done on the following types of databases:

• Relational Database
• Data warehouses
• Data Repositories
• Object-Relational Database
• Transactional Database

Career Opportunities in Artificial Intelligence branches

AI and ML are becoming mainstream and career opportunities have increased in this field in the past few years. There has been a shortage of employees with skills in this field.

According to Statista, 82% of organizations need machine learning skills and only 12% of enterprises state the supply of ML skills is at an adequate level.

So, What AI Career options are available and what Careers Can You Pursue?

Let us look at the top AI & ML career options individually.

1. Machine Learning Engineer

Machine Learning Engineer plays a vital role in the data science team. They do research, develop, and create the artificial intelligence that powers machine learning, as well as maintain and develop already-existing artificial intelligence systems.

An average Machine Learning Engineer can earn between 35,00$ – 50,000$ and $1,80,000 in tech giants like Google and Facebook. 

2. Data Scientist  

A data scientist’s job is to gather data, look at data, and shape data, but in many different ways and for many different reasons. An average data scientist spends 2 weeks analyzing a data set and delivering the results to a business audience.

A data scientist makes $105,000 on average. A manager of a data science role can earn up to $1,80,000 /yr with experience.

3. Big Data Engineer/Architect

Big Data architects construct data infrastructure to extract, organize and maintain 

data for authorized individuals to access. Data architects/engineers work with database administrators and analysts to provide easy access to the company’s big data.

Big data engineers are among the best-paid job roles and Can earn up to $151,300 on average.

4. Data Analyst

The Data Analyst job includes reviewing data to identify key insights into a business’s customers and ways the data can be used to solve problems. A data analyst can earn up $65,000 or more.

5. Robotics Engineer

An average robotics engineer works on building robotic prototypes, configuring, examining, and debugging robots and robotic systems.  They can earn an average salary of $151,300.

6. NLP Engineer

Natural Language Processing (NPA) Engineer’s daily task is to transform natural language data into useful elements using NLP techniques to feed classification algorithms. NPL engineers can earn up to  $78,000, going up to over $100,000 with experience.

7. Business Intelligence Developer

The task of creating, managing, and maintaining business interfaces falls under the responsibility of a business intelligence developer. The average salary of a Business Intelligence Developer is $86,500, going up to $130,000 with experience.

Related article: Best AI documentaries to watch this year

Final Words

In this guide, we have discussed different types of Artificial Intelligence. We also discussed various branches of AI which will give us more clarity on the flow of development.

AI and Ml is a vast field that still needs to be researched. Artificial intelligence has opened the gate to new opportunities in the field of science and engineering. Career opportunities across various industries are expanding with captivating, and high-paying jobs in the field of  AI.

Article by

Samruddhi Chaporkar

Samruddhi is a Technical Content Writer of science and technology with an academic qualification in Bachelors in Computer Applications (BCA). Her love for writing and knowledge of computer science helps her write about trends in the technology industry that help people understand the ins and outs of the world of technology.

LinkedIn | Twitter | Author page