Data Science Prerequisites: Skills, Roles & Responsibilities

data science prerequisites

Data science is one of the growing technology nowadays. Every business is starting to use data science to implement its projects. Data science plays an important role in data, and you can use it to grow your business.

So, in this article, we will be discussing all the data science prerequisites.

Before diving deep into the topic, let us understand what data science is.

Table of Contents

What is Data Science?

Data science is a field of study that works with huge amounts of data, utilizing contemporary technologies and methodologies to uncover hidden patterns, obtain valuable information, and make business decisions. 

To create prediction models, data scientists use sophisticated machine learning algorithms. Analytical data is available in many different formats and can come from a variety of sources.

Prerequisites for Data Science

The focus of data science, as the name says, is data. Therefore, a person’s love of data, comprehension, and capacity to work with it is the first and most crucial requirements for learning data science.

Data scientists might be thought of as “wranglers” of huge data. They perform extensive structured and unstructured data analysis. They mix Computer Science with mathematics and statistics to handle, analyze, and model data and understand relevant results.

They require expertise across a wide range of areas to accomplish this. These prerequisites primarily fall into two categories.

Relevant read: What mathematics does computer engineers use?

1. Non-Technical Prerequisites

Let us discuss a few non-technical prerequisites for data science.

1/ Business Strategy

Data scientists should have a thorough understanding of organizations, the issues they confront, and the capability to offer solutions through analysis. This enables them to use data in a way that benefits the company. Understanding the sector you work in and the issues your firm is trying to address is one of the essential prerequisites for Data Science. It should be clear to you how the issue might impact the company.

You need to understand how businesses function in order to tackle the issues and focus your efforts in the proper direction.

2/ Analytical and Interpersonal Skills 

Technical proficiency alone won’t get you far in the data science sector, as it won’t in any other field. Possessing analytical thinking, problem-solving skills, and social competence is crucial. Let’s go over some of the abilities required for a Data Science profession.

3/ Good in Teamwork

For data scientists, it’s crucial to be able to collaborate well in groups. They must collaborate with business leaders, product managers, designers, and software developers because they cannot work alone.

Better goods, data pipelines, and strategy development all require solutions to be developed. To develop better business solutions, they must collaborate with everyone, including clients and employees from all divisions.

4/ Ability to Communicate

Data scientists must have strong communication skills to readily, fluently, and effectively communicate technical findings to other non-technical teams, such as the Sales, Operations, or Marketing Departments. They must be able to offer valuable insights that will help the organization make more informed decisions. To make data easier to understand for everyone, it is also possible to develop a narrative around it.

2. Technical Prerequisites

Let us discuss a few technical prerequisites for data science.

1/ Knowledge of Machine Learning and Artificial Intelligence

ML aids in the analysis of massive volumes of data using algorithms. Data scientists can automate a large portion of their work using machine learning.

Data scientists that are skilled in advanced machine learning methods like adversarial learning, neural networks, reinforcement learning, outlier detection, time series, etc. constitute a very small minority.

The best data scientists have extensive knowledge of cutting-edge machine learning methods, including Natural Language Processing and recommendation engines.

Knowing machine learning techniques like logistic regression, supervised machine learning, decision trees, survival analysis, computer vision, and others is essential if you want to stand out from the crowd and be among the top tier.

2/ Learn Database in SQL

Data analysis and research are known as data science. We must take the data out of the database to study it. SQL can be used in this situation. An essential component of data science is relational database management.

SQL continues to be the best option for many CRM, business intelligence tools, and office processes, even if several contemporary sectors have focused their product management on NoSQL.

SQL is the basis for several database platforms. It has become a standard for many database systems, which is why. Modern big data systems like Hadoop and Spark actually use SQL to manage relational database systems and handle structured data.

3/ Programming in Python

Python, which was mentioned in 39% of job advertisements, was the second most important Data Science expertise behind Hadoop. These days, data scientists use this programming language the most frequently.

Python is incredibly flexible and can be used in practically all phases of data science. Python can perform any task, including running embedded devices and data mining, and as a result, 40% of respondents to an O’Reilly survey claimed that Python was their preferred programming language.

Data analysis is done using the Python module Pandas, which can do anything from displaying data with histograms to importing data from spreadsheets. Python can simply import SQL tables into your code from a variety of data sources. Numpy, Matplotlib, PyTorch, Pandas, Scikit-Learn, and Seaborn in Python are the python packages you need to master.

Relevant read: Learn whether you need coding skills to become a data scientist 

4/ Knowledge of Hadoop

In the same CrowdFlower survey, 49% of job descriptions listed Hadoop as the second most important Data Science ability. Although it is not usually a strict need, it is nonetheless one of the prerequisites for data science that companies really appreciate.

You will come across instances as a data scientist where your system’s RAM is exceeded by the amount of data you have.

In this situation, you would have to transfer the data to many servers. Here, Hadoop’s function is necessary. Hadoop may be used to send data fast to various system locations. Additionally, it can be applied to data exploration, filtering, sampling, and summarization.

5/ Learn R Programming

R is a language created especially for data science. Any Data Science-related issue you may run into can be resolved using it. Among data scientists, it is the most often used language.

In fact, most data scientists say that R is their preferred tool for handling statistical issues. One of the most significant Data Science Prerequisites is this. But there is a significant learning curve. It’s challenging to master, especially if you’re already proficient in another programming language.

6/ Learn Calculus and Statistics

One of the extremely common requirements for data science is math. For data imputation, feature transformation, model evaluation, dimensionality reduction, feature engineering, and data pretreatment, probability and statistics are applied.

To create machine learning models, multivariable calculus is used. We employ linear algebra to evaluate models, preprocess data, and transform data. Data sets are represented by matrices.

7/ Learn Both Excel and Tableau

Tableau and Excel are two other crucial Data Science Prerequisites. To comprehend, work with, analyze, and visualize data, both of these Data Science tools are crucial.

When there are numerous data manipulations and calculations that need to be made, Excel is used. When all the data needs to be collected in one location and displayed on the dashboard with strong visualizations, Tableau is utilized.

Both can be used in tandem, with the final data set being transferred to Tableau for additional processing, analysis, and gaining more insights once all the key calculations have been completed on Excel.

Why choose Data Science?

Let us understand the following reasons for choosing data science.

1. Demand is Rising

Every firm, large or small, requires specialists who can analyze and comprehend raw data and assist the company in making effective use of it as data-driven decision-making becomes more and more popular over time.

The need for data scientists is rising and will continue to grow, according to Searchbusinessanalytics.techtarget.com. The average rise in demand for data scientists since 2013 has been a 29% to 344% increase.

2. Exceptional Pay

Glassdoor.com reports that the average salary for data scientists in India is rupees 100K per year

According to Indeed.com, the typical compensation for a data scientist in the US is $119,353 per year, while similar salaries are paid in the United Kingdom, Canada, France, and Australia, with average yearly salaries of £52,137, C$79,313, €44,730, and AU$92,157, respectively.

3. Growing Technology

Due to the enormous and continuing increase in the amount of data in the world as well as the rising demand for data scientists, data science is a field that is actively developing.

If you decide to pursue a career in data science, you will have fascinating possibilities to work on cutting-edge technologies like artificial intelligence and machine learning as well as those that are fast developing, such as serverless computing, blockchain, and edge computing.

Roles and responsibilities of a data scientist

The roles and responsibilities of data scientists include the following:

  • The extraction of useful data from valuable data sources is known as data mining, and data mining is one of the responsibilities of a data scientist.
  • Using machine learning tools for optimizing, choosing features, and building classifiers.
  • Performing structured and unstructured data preparation Improving data gathering processes to capture all pertinent data for creating analytical systems
  • Preparation of data, cleaning of data and ensuring the accuracy of data for its analysis.
  • Pattern finding and solution finding by analyzing a lot of data
  • Creating machine learning algorithms and prediction systems
  • Clearly presenting the results
  • Also helps to offer tactics and different ways to deal with the company’s difficulties.
  • Communicate with the IT department and with the other businesses.

How to start learning data science?

To grow your career in data science, you should follow the below-mentioned steps:

Step 1. Obtain a Bachelor’s Degree

Obtaining a bachelor’s degree in a related subject, such as computer science, statistics, or data science, is an excellent approach to getting started in data science. It is one of the most prevalent considerations for recruiting data scientists.

Step 2. Acquire Knowledge of Programming Language 

Even while a Bachelor’s degree may give you a theoretical understanding of the topic, it is crucial to brush up on programming languages like Python, R, SQL, and SAS. When it comes to interacting with huge datasets, these languages are crucial.

Step 3. Learning Related Skills

A Data Scientist should be proficient in using a few technologies for Big Data, Machine Learning, and Data Visualization in addition to several different languages. Knowing how to handle large datasets and perform cleaning, sorting, and analysis on them is essential when working with large datasets.

Step 4. Acquire Certifications

Certifications for particular tools and skills are an excellent method to demonstrate your knowledge and proficiency in those areas. A few excellent certifications to get you started include the following:

  • Training Program for Tableau Certification
  • Certification Program for Power BI

The two tools listed above are the most frequently used by data scientist professionals and are a great place to start your career.

Step 5. Apply for Internships

In your employment search, look for positions with keywords like “data analyst,” “business intelligence analyst,” “statistician,” or “data engineer.” Additionally, internships are a terrific method to see firsthand what a job will actually entail. You can find internships in data science on LinkedIn Jobs more easily. Also, there are a lot of platforms you can search for internships in data science, such as internshala, internships.com, glassdoor, etc. 

Relevant read: Types of data science jobs (with job responsibilities) 

Step 6. Apply for Data Science Entry-Level Jobs

You have two options after your internship is over: you can either join the same business (if they’re hiring) or you may start looking for entry-level jobs as a data scientist, data analyst, or data engineer. As your knowledge and abilities grow, you can advance from there by gaining experience and moving up the ladder.

Further resource on data science:

Final Words

For the foreseeable future, data will be the industry’s lifeblood. Data is actionable knowledge, and knowledge may be the difference between a company succeeding and failing.

Companies may now foresee future growth, identify potential issues, and create well-informed success strategies by integrating data science techniques into their operations.

The time is here for you to begin a career in data science.

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