RnewYear2022 RnewYear2022

- Data Science Blogs -

Prerequisite for Data Scientist: First Step To Becoming Data Scientist


Businesses worldwide have always been gathering and analyzing data related to their customers to offer the best possible services and improve their revenue. They need advanced data processing techniques and software to manage this huge data. 

This data processing has led to a tremendous demand for data scientists, who have the desired prerequisites for data science. The popularity of data science has grown drastically over the years, and if you are planning to get your foot through the door of the Data Science field, have you ever come across the questions below? 

  • Can I learn data science if I love numbers but haven’t earned a graduate degree in math or statistics?
  • Will I be eligible to become a data scientist if I’m a quantitative problem solver but don’t have a master’s degree?
  • I’m excellent at writing macros in Excel and also have an affection for quantitative analysis. Will I be able to pursue my career as a data scientist?

These are the questions commonly get asked when counseling for data science courses

No doubt, Data science is currently the hottest career field, and if you’re interested in getting started on a data science career path, and want to know what prerequisites for data science are required, then do read this blog thoroughly and decide if the data science career is right for you.

What Does a Data Scientist Do?

Data science professionals are specialized in analyzing and interpreting data. Using their data science skills, they help businesses make wise decisions and enhance their processes. Data scientists need a solid maths, statistics, and CS (Computer Science) background. 

This knowledge is used to analyze huge data sets and identify trends. Moreover, data scientists could build new ways to collect and store data. You can learn more about what does a data scientist do to stay competitive in today’s world!

Data Science Training - Using R and Python

  • Detailed Coverage
  • Best-in-class Content
  • Prepared by Industry leaders
  • Latest Technology Covered

Why Data Science?

Since the world entered the age of big data, the need to store it has grown, and until 2010, this was the biggest concern for businesses. The main challenge was to build a framework that would store data. Now that Hadoop and other frameworks have successfully resolved this issue, the focus has transitioned to processing the data. Hence, it's essential to learn what data science is all about, what the prerequisites for data science are, and how it can add value to your organization using a data science tutorial

Here are a few reasons why data science could be your best career choice.

1. Rising Demand

Because the data-controlled decision-making process is becoming popular with almost all companies, whether small or big, it needs the expertise to analyze and interpret data and help businesses use it effectively.

As per a study, the demand for data scientists is rising year after year, and it's expected that the mathematician's and statistician's job roles, together with data scientists' jobs, will encounter a 36% increase between 2021 and 2031. 

This high demand and huge lack of experienced and skilled professionals have made it the best field to become an expert and make a career. If you have any queries about data science, a complete guide on data science career path, will help you find all the answers.

2. Well-paying Job

As stated by Glassdoor, the average salary of a data scientist is $1,21,247 in the United States, which is remarkably higher than other IT jobs. Also, according to Forbes, the demand for data scientists will grow by 26% by 2026, which signifies the durability of the profession. 

Moreover, the data scientist profession ranked second in the Best Jobs in America survey. Thus, if you’re looking for a stable and secure career, data science provides you with that opportunity.

3. Constantly Growing Field

As said earlier, Data Science is a fiercely growing field because of the ongoing amount of data around the world and the ever-expanding demand for data scientists. Suppose you aspire to make a career in Data Science; in that case, you’ll get an amazing opportunity to work on evolving technologies such as ML and AI, including Edge Computing, Blockchain, Serverless Computing, etc. If you’ve been convinced to make a career in Data Science due to its growing demand and high pay scale. You must be curious to know data scientist opportunities, including data science prerequisites.

Opportunities For Data Scientists

Future Opportunities

The majority of professionals and beginners equally aspire to enter the Data Science field. It's a mixed job that unites data analytics, science, and administrative tools and demands a job profile in the midst of numerous amounts of data that need excellent solutions for business profit. 

The increasing demand for data science has resulted in a rise in the Data Scientists' profile. According to a report, by 2026, there’ll be an intended 19% growth in data scientists' jobs.

Data science life cycle

As you know what data science is, next, we’ll focus on the life cycle, which contains five separate stages, each with its unique tasks - 

  1. Capturing - This step includes collecting raw structured and unstructured data, which can be simply Date Acquisition, Data Entry, Data Extraction, etc. 
  2. Maintenance - This step involves taking the raw data and converting it into a usable form. Data Warehousing, Data Cleansing, Data Staging, Data Processing, and Data Architecture. 
  3. Processing - At this stage, the data scientists take the processed data and analyze its patterns, biases, and ranges to evaluate its usefulness in predictive analytics.
  4. Analysis - This step consists of executing different data analyses. Predictive Analytics, Regression, Text Mining, Qualitative Analysis, Exploratory or Confirmatory. 
  5. Communication - This final stage involves preparing analyses in easily understandable formats like charts, graphs, and reports.

Prerequisites For Data Scientist

Data Science Prerequites

As the name suggests, Data Science is all about data. Therefore, the most crucial requirements to be a data scientist are - to have an affection for data, understand the data, and have the capability to deal with data. 

Data scientists need to assess large data sets, including structures and unstructured ones. Combining computer science skills with maths and statistics, they process, analyze and model data to present meaningful outcomes. To do this, they require a variety of data science prerequisites that are mainly categorized into 2 types - 

  1. Technical Data Science Prerequisites
  2. Non-Technical Data Science Prerequisites
  3. Interpersonal and Analytical Skills 

1. Educational Requirement for Data Science

The minimum educational requirement to be a data scientist is a graduation degree in computer science, IT, maths, statistics, engineering, or any other relevant field. Furthermore, inclining statistical analysis and coding will be an added benefit for you to master the skills required for becoming a Data Scientist. 

Even though having a bachelor’s degree is the minimum prerequisite, most businesses prefer candidates with higher academic qualifications, i.e., a minimum master’s degree, as stated by the US Bureau of Labor Statistics. 

A solid educational background is crucial to acquire in-depth knowledge to pursue a successful data science career. You can also enroll in online data science training and certification courses to stay up to date with the latest technologies and skills.

2. Technical Data Science Prerequisites

1. SQL Databases: Analyzing thousands of job descriptions for data science posted on LinkedIn, one prerequisite for data science that topped the list was SQL, in around 57% of job postings. This language is used for managing and querying data stored in RDBMS and reading, retrieving and updating, inserting new data, or deleting existing data. SQL language also helps in data structure transformation and performing analytical tasks. 

Businesses expect candidates should be able to write complex SQL queries to get insights from data. 

2. Hadoop- Hadoop is another important prerequisite for data science cited in approximately 49% of data science job descriptions. However, it's not a compulsory requirement, but still one of the highly preferred skills by recruiters. 

When working as a data scientist, sometimes you might come across a situation where the quantity of data you’ve got exceeds your system's memory. At this time, you will be required to send that data to various servers; this is where Hadoop comes into play. This platform can quickly transfer data to different points in the system. 

3. Python- Python was the 3rd most in-demand data science skill, found in 39% of job listings. Currently, it is the most preferred programming language among data scientists. Since Python for data science is a very flexible and easy-to-use language inside almost all the data science processes, such as data mining, operating embedded systems, etc., it's highly popular. Python library has Pandas that can be used for data analysis and for carrying out any task, from plotting data using histograms to importing data. 

4. R Programming- Next important prerequisite for a data scientist, after Python, is R Programming, specially designed for data science and was cited in around 32% of job listings. It could be used to resolve any type of data science-related issue. R Programming is mostly used for solving statistical issues. 

But having said that, its learning curve is very steep and difficult to master, mainly if you already have mastery in another coding language. 

This language can install machine learning algorithms and give different statistical and graphical methodologies such as time-series analysis, clustering, standard statistical tests, etc. 

5. ML and AI- ML and AI are also essential prerequisites for data scientists. 

Machine learning help in assessing huge amounts of data with the help of algorithms, and this language could automate a major part of data scientists’ job. As just a small no. of data scientists are highly skilled with advanced ML techniques such as adversarial learning, reinforcement learning, the neural network in data science, etc.

Most proficient data scientists understand advanced ML techniques like recommendation engines and NLP (Natural Language Processing). Do you wish to stick out from the crowd and be at the top level? Knowledge of ML techniques is a must. 

6. Mathematics and Statistics- Another important prerequisite for data science is Mathematics and statistics because statistics and probability are required for data manipulation, visualization, transforming features, model evaluation, data processing, etc. Multivariable calculus is used for building machine learning models, and for data processing and transformation, model evaluation linear algebra is used. For representing a data set, the matrix is used. 

7. Apache Spark- Similar to Hadoop, Apache Spark is a big data computation framework; the only difference between the two is that Spark is relatively faster. Hadoop reads from a disk and writes to a disk; on the other hand, Spark grabs its numbers inside the memory of the device, making Spark faster than Hadoop. Spark is specifically designed for data science to execute complex algorithms quickly, helping you save time while processing data. Additionally, it can also help data scientists manage large, unstructured, and complicated data sets.

8. Data Visualization- Is also another very essential requirement for data science. Simply put, it involves representing data visually using graphs and charts. A data scientist should be able to present the data graphically with the help of charts, maps, etc. 

9. Excel and Tableau- Are the other 2 most important requirements for data scientists, as both are essential for understanding, manipulating, analyzing, and visualizing data. Excel is useful when a lot of manipulation and calculations must be done on the data.

You’ll need Tableau when you collect data at one location and show it with the help of powerful visualization on a dashboard. A blend of both could be used when all the important calculations can be performed on excel, and the resulting data set can be imported to Tableau for further analysis and to get a more clear vision.

Data Science Training - Using R and Python

  • No cost for a Demo Class
  • Industry Expert as your Trainer
  • Available as per your schedule
  • Customer Support Available

3. Interpersonal and Analytical Skills

Like every other field, technical skills aren’t sufficient to succeed in the data science industry. Therefore, having analytical, problem-solving, and interpersonal skills is essential. So, let’s look at a few skills required for pursuing a data science career. 

  • Communication skills: Along with the aforementioned prerequisites for data science, communication skills are also very important. Data scientists must be effective communicators to conveniently and efficiently interpret the technical findings to other team members such as Sales, Operations, Marketing, etc. They must be able to create a storyline around the data, to make it easy to understand for everyone.
  • Teamwork: Data scientists should be able to work in team settings and come up with solutions for creating better products, data pipelines, and developing strategies. They must work with everyone from all the departments to customers to produce excellent business results. 
  • Business Strategy: Data scientists must be able to understand businesses and their problems and should be able to provide solutions by carrying out analyses. This will facilitate them to utilize the data in such a way that it’ll be useful for the business. 

They must also understand how the problem could affect the respective business and solve those problems; the data scientists should know how companies work so that their efforts can be directed properly.

Tools used by Data Scientists

Data scientists are responsible for data extraction, manipulation, pre-processing, and forming predictions based on the data. So as to do that, a data scientist must work with different statistical tools and coding languages.

We’re sharing a list of top data science tools for your reference.

  1. SAS
  2. Apache Spark
  3. BigML
  4. D3.js
  6. Excel
  7. ggplot2
  8. Tableau
  9. Jupyter
  10. Matplotlib
  11. NLTK
  12. Scikit-learn
  13. TensorFlow
  14. Weka

Where Do You Fit in Data Science?

Data Scientist

Data science allows you to concentrate on and master one facet of this field. In this section, we’ll be introducing you to a few ways you can fit in this growing and thrilling field.

Data Scientist

  • Job Profile: To identify where the problem is, what questions need to be answered, and where to find the data and also to mine, clean, and represent the relevant data.
  • Skills Required: Programming skills, for example -SAS, R, Python, storytelling and data visualization, mathematical and statistical skills, and knowledge of Hadoop, SQL, and ML.

Data Analyst

  • Job Profile: Data analysts help bridge the gap between data scientists and business analysts, managing and analyzing data to answer an organization's questions. To perform technical analyses and convert them into qualitative action points.
  • Skills Required: Mathematical and Statistical skills, programming skills such as - SAS, R, and Python, including experience in data wrangling and visualization.

Data Engineer

  • Job Profile: Data engineers are responsible for developing, implementing, managing, and optimizing the businesses’ data infrastructure and pipelines and assist data scientists by helping them transfer and convert data for queries.
  • Skills Required: NoSQL, e.g., MongoDB, Cassandra DB, etc., programming languages like Java, Scala, and frameworks such as Apache Hadoop.

A comprehensive data science certification guide will give you an idea of different types of data science certifications, along with preparation tips. 

You can master data science skills, learn programming languages such as Python & SQL, data analysis & visualization, and develop ML models with advanced Data Science training courses by  JanBask Training. Enroll in and learn with our latest data science syllabus to land your dream job.


Data is going to be the lifeblood of the business sector for a reliable future. By integrating data science techniques into organizations, businesses can predict their future growth and potential issues and develop informed strategies for benefits. 

Data scientists are fully equipped with various skill sets; however, remember that it isn’t simple to become a jack of all trades because with price comes challenges! And with proper direction, training, experience, and courses, you can move ahead in the relevant field, which will slowly help you to add value to your professional career. 

Don’t wait; it's the right time to begin your career in data science! If you’re curious about where to start learning data science and get in front of fast-moving technological advancements, sign up for our data science courses today! If you’ve any queries regarding this article, put it in the comments below.


Q1. What’s the major difference between data science, AI, and ML?

Ans: The primary differences between data science and machine learning, and artificial intelligence are as follows -

Data science is a subset of Artificial Intelligence, which deals with structured and unstructured data, analysis, and statistics, everything for gaining more insights and meaning from data. AI makes a computer act or thinks like a human being. ML is also a subset of Artificial Intelligence that helps computers to learn things using provided data.

Q2. What is Data Science?

Ans: It is a subset of AI that deals with data processing methods, analysis, and statistics, to gain more insights and value from data.

Q3. What does a Data Scientist do?

Ans: A data scientist analyzes raw business data to draw useful insights.

Q4. Explain Data Science using an example?

Ans: Data science deals with large volumes of data with the help of advanced tools and techniques to find hidden patterns, derive useful insights, and make corporate decisions. For instance, finance firms can make use of a customer’s banking and bill-paying history to analyze trustworthiness and loan risk.

Q5. What types of issues do the data scientists resolve?

Ans: Data science combines different disciplines like statistics, computer science, and mathematics, and they solve issues such as:

  1. Mitigate loan risk
  2. Pandemic curve and contamination patterns
  3. The efficiency of different types of online promotion
  4. Resource allotment

Q6. Why is it important to pursue Data science online training program?

Ans: Data science courses are important to pursue because:

  • They verify a candidate's knowledge in the Data science field.
  • They improve job interview invitations dynamically.
  • They push your career prospects when job hiring preferences and salary discussions occur.
  • They imbibe confidence when approaching any job or project.

Q7. What is the eligibility for data science online training courses?

Ans: Our data Science courses are suitable for candidates planning to pursue a career in Data Science, and the minimum criteria is a Bachelor’s Degree. Candidates from a STEM background (Mathematics, Computer Science, and Statistics) can apply for this course.

Q8. Is it possible to learn Data Science on my own?

Ans: Remember that Data science is a complex field with several difficult technical needs. Therefore, it’s not advisable to try learning data science without the help of a  proper learning program.

Q9. What are the business applications of data science?

Ans: The finance department at your organization might make use of data science to create reports, and predictions, and determine financial patterns. Data science can also be used to improve the company's security and safeguarding of sensitive data. Finding out the incompetencies in manufacturing operations is another way to integrate data science in business. 

Q10. Why is communication skill is of the most important data scientist requirements?

Ans: All the excellent research and insights could get washed out if a data scientist doesn’t have good communication skills. Strong communication skills will help to fill out the data scientists' qualifications and make their work accessible to others in the company. 

Q11. Explain how data science is a dynamic field?

Ans: Data Science combines several disciplines, such as statistics, computer science, and mathematics. It’s possible to become an expert in all fields and be equally knowledgeable in all of them. A candidate with a background in statistics might not be able to instantly learn Computer Science and become a successful Data Scientist. Because of it’s dynamic and ever-changing nature, you need to study continuously to learn the various aspects of data science.



    With fact-finding market research & solicitous words, Nandita helps our digital learners globally navigate their way to profound career possibilities in IT and Management.


Related Courses

Trending Courses



  • AWS & Fundamentals of Linux
  • Amazon Simple Storage Service
  • Elastic Compute Cloud
  • Databases Overview & Amazon Route 53

Upcoming Class

0 day 29 Sep 2023



  • Intro to DevOps
  • GIT and Maven
  • Jenkins & Ansible
  • Docker and Cloud Computing

Upcoming Class

7 days 06 Oct 2023


Data Science

  • Data Science Introduction
  • Hadoop and Spark Overview
  • Python & Intro to R Programming
  • Machine Learning

Upcoming Class

0 day 29 Sep 2023



  • Architecture, HDFS & MapReduce
  • Unix Shell & Apache Pig Installation
  • HIVE Installation & User-Defined Functions
  • SQOOP & Hbase Installation

Upcoming Class

0 day 29 Sep 2023



  • Salesforce Configuration Introduction
  • Security & Automation Process
  • Sales & Service Cloud
  • Apex Programming, SOQL & SOSL

Upcoming Class

1 day 30 Sep 2023



  • Introduction and Software Testing
  • Software Test Life Cycle
  • Automation Testing and API Testing
  • Selenium framework development using Testing

Upcoming Class

0 day 29 Sep 2023


Business Analyst

  • BA & Stakeholders Overview
  • BPMN, Requirement Elicitation
  • BA Tools & Design Documents
  • Enterprise Analysis, Agile & Scrum

Upcoming Class

0 day 29 Sep 2023


MS SQL Server

  • Introduction & Database Query
  • Programming, Indexes & System Functions
  • SSIS Package Development Procedures
  • SSRS Report Design

Upcoming Class

7 days 06 Oct 2023



  • Features of Python
  • Python Editors and IDEs
  • Data types and Variables
  • Python File Operation

Upcoming Class

0 day 29 Sep 2023


Artificial Intelligence

  • Components of AI
  • Categories of Machine Learning
  • Recurrent Neural Networks
  • Recurrent Neural Networks

Upcoming Class

0 day 29 Sep 2023


Machine Learning

  • Introduction to Machine Learning & Python
  • Machine Learning: Supervised Learning
  • Machine Learning: Unsupervised Learning

Upcoming Class

35 days 03 Nov 2023



  • Introduction to Tableau Desktop
  • Data Transformation Methods
  • Configuring tableau server
  • Integration with R & Hadoop

Upcoming Class

0 day 29 Sep 2023