Does Data Science Require Coding?

coding skills for data science

Data science is one of the most demanding and competitive career options. Students, who want to start their career in data science often get confused when it comes to questions like:

‘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.

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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 best data modeling books and best computer science podcasts.

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.