Data Science Course Syllabus, Eligibility, Importance, In 2022

Data Science


In this blog you will read in detail about data science course syllabus, eligibity, importance, etc. So pay attention to this blog to know in detail about the essential skills and tools required to perform the functions of data science by data scientist.


What is Data Science

Data Science is a field of study that utilizes the data, algorithms, and tools to solve the problems faced by individuals, and companies in operating their businesses. It involves comprehending the fundamental ideas behind statistical techniques as well as how to apply them to practical issues. The course focuses on extensive data analysis, predictive modeling, and natural language processing using machine learning algorithms (NLP).

Eligibility For Data Science Course

  • Graduation in mathematics, computer science, engineering, statistics, data science, or related subjects is the standard eligibility requirement for enrolling in any data science course after the 12th grade; preferably a diploma, bachelor’s, or master’s degree. 
  • Candidates must receive a graduation grade of at least 50%.
  •  If they want to take admitted to data science programs at prestigious institutions like IIT Madras, then candidates must have earned at least 60% of their graduation marks.
  • Data science course eligibility is explained below with the help of a table for better understanding.
Course Eligibility Criteria
Data Science Certifications With a high school diploma from a reputable board and a foundational understanding of arithmetic, computer science, and statistics, Some universities and institutions may require a high school GPA of at least 50%.
Diploma in Data Science BE/BTech/MCA/MSc degree with a minimum cumulative GPA of 50% and statistics, computing, or programming as core subjects. Diploma in Data Science.
BSc/BTech/BCA Data Science Class 10+2 from a recognized board with PCM and Computer Science/Statistics
MSc/MTech/MCA Data Science A recognized university’s  BCA, BSc Statistics, BSc Mathematics, BSc Computer Science, BSc IT, or equivalent degree with a minimum GPA of 50% is required. Candidates with a background in statistics and mathematics are preferred.
Ph.D. in Data Science 55% in the postgraduate program in the desired discipline for a Ph.D. in data science. Higher GPA candidates will be given preference.

Data Science Syllabus 2022

Data Science Syllabus for Beginners

Online data science courses are available for those who are new to the field or who wish to check them out after finishing high school. Online data science courses are widely available for novices from udemy, Coursera, Google, Microsoft, and IBM. See the part below for a beginner’s data science syllabus:

  • Overview of Data Science
  • Data Analysis
  • Utilizing the Cloud
  • Analysis of Data
  • Visualization of data
  • Evaluation and Selection of Models
  • Learning Machines
  • Enterprise Intelligence
  • Database Management
  • Dashboard for data and storytelling
Data Science Subjects Data Science Course Content
Introduction to Data Science Data science definition, significance, and fundamental applications.
Machine Learning Algorithms Using mathematical models or algorithms to identify trends, categorize data, or make predictions.
Artificial Intelligence The development of algorithms enables the creation of a machine with human-like problem-solving abilities.
Data Visualization A chart, diagram, plot, or other visual representation of data.
Data Analysis Employing algorithms to format or model data to get insights.
Coding (Python, SQL, Java) Unstructured data organization using simple code.
Optimization Techniques Software used for data extraction is optimized to produce the most with the least amount of resources.
Statistics Data are analyzed using statistics to derive insights and apply the right mathematical models to variables.
Big Data Managing large data collections to facilitate data extraction and analysis.
Predictive Analysis Using data, algorithms, and models to make predictions about the future based on the past.

Importance Of Data Science

According to AIM Research, India, there were 137,870 data science jobs in India in June 2021, representing a 47.1% increase in the number of vacant positions compared to June 2020. India contributed to 9.4% of all analytics job vacancies worldwide, up from 7.2 in January 2020.

  • Data science helps companies to effectively analyze a large amount of data so that can gain helpful information for taking more improved and informed decisions.
  • Data science is extensively employed in many different business sectors including marketing, healthcare, banking, finance, and other areas.
  • It gives businesses the ability to monitor, manage, and record performance measures to support improved decision-making throughout the entire organization.
  • It helps in analyzing the trend of data which enables businesses to take important decisions that will improve consumer engagement, raise productivity levels, and boost profits.

Entrance Exam For Data Science

The majority of the top universities for master’s in data science admissions have relied heavily on entrance exams. Some colleges accept the CUET, NIMSEE, and CUCET national-level exams, whereas others administer their own entry tests. Top M.Sc. Data Science entry exams include the following:

Pattern For Examination Of Data Science

The pattern for the data science exam is as follows:

  • There are 50 multiple-choice questions at 100 points each, which must be answered in three hours, or 180 minutes, and there is also a group discussion/interview-style assessment after this paper.
  • The three exam sections are Core Subjects, Logical Analysis, and General Aptitude.
  • Around 15% of the total marks will be allocated to the Core Subjects, 15% to the General Aptitude portion, and the remaining 70% will be divided among the Core Subjects.
  • The majority of these exams include both multiple-choice questions (MCQs) and questions with numerical answers.

Colleges For Data Science

The Following are the best colleges for data science in India as mentioned in tabular form:

Data Science Colleges
In India
Data Science Courses   Data Science Course
Data Science Course Fees  
1. IIT Delhi Program for Certificates in Data Science and Machine Learning   6 months ₹1,25,000 + GST  
2. IIT Roorkee Executive Postgraduate Certificate in Data Science   12 months ₹2,49,999/- 
(5% off for one-time payment)
3. IIT Madras 1. B.Sc. in Data Science and Programming

2. Diploma in Data Science
  1-3 years 
(with the option to finish in six years)

1-2 years

4. IIM Calcutta Advanced-Data Science Program   12 months  ₹4,40,000 +GST  
5. BITS Pilani (WILP)
1. M.Tech Data Science &

2. Postgraduate Program in AI & ML
  2 Years

11 Months

₹60,500/- per semester

₹16,500/- (one-time admission fee)

₹2,45,000/- (including GST)
6. Jain Online University 1. Certification- Python for Data Science 

2. MCA in Data Science

3. MBA in Data Science & Analytics
3 Months

2 Years

2 Years
₹12,000/- (National)

$250/- (International) 

₹2,00,000/- (National)

$4,200/- (International)
7. Manav Rachna Centre for
Distance and Online Education
1. BCA in Data Science & Big Data Analytics  
2. BCA in AI & ML 

3 Years

₹1,56,000/- (semester- wise payment)

₹1,46,250/- (year- wise payment)
₹1,36,500/- (one-time payment)
    1. Big Data Analytics and Data Science MCA
2. MCA in AI & ML
  2 Years ₹1,44,000/- (semester- wise payment)

₹1,35,250/- (year- wise payment)

₹1,26,500/- (one-time payment)

Online Course For Data Science

Following are the top 5 best data science courses for online in 2022

1. Data Science A-Z™: Real-Life Data Science Exercises Included- Udemy

2. Data Scientist – Simplilearn

3. Data Science Specialisation

4. Introduction to Data Science

5. Data Quest

Steps in Data Science

Step 1: Defining the Issue

  • The first step performed by a data scientist is to identify the nature of the problem faced by the companies.
  • If individuals frequently provide confusing feedback on their problems then you’ll need to develop the ability to translate those inputs into useful outputs in this initial step. 

You can get through this level by posing questions like the ones that follow:

  • Who are the clients?
  • How do you recognize them?
  • What is the process of sale right now?
  • What goods are of interest to them?

For numbers to become insights, much more context is required. You need to have as much information on hand as you can at the end of this phase.

 Step 2: Getting the raw data for the issue

  • After the identification of the problem, the process of data acquisition is performed which means collecting the required data for doing research to transform the company’s issue into a likely resolution
  • For finding the data a data scientist can scan the internal databases of the companies or can purchase the databases from external sources.
  • CRM (customer relationship management) systems are widely used by organizations to store their sales data.
  • Analysis of the data is made easier by connecting the CRM data via data pipelines to more complex applications.

 Step 3: Preparing data for analysis

  • After the second step of collecting the data, the processing of this data has to be done before doing further analysis of it.
  • If data is not properly stored, it can become disorganized, resulting in mistakes that can easily ruin an analysis.
  • You must evaluate the data and search for mistakes in order to derive more precise findings.

The most frequent errors people make and should be on the lookout for are:

  • Missing values
  • corrupt values, such as incorrect entries
  • discrepancies in time zones
  • Date range mistakes, such as a sale that was recorded before the sales began

 Step 4: Investigating the Data

  • This step requires you to develop ideas that can be applied to uncover underlying patterns and new information.
  •  You’ll need to look for more attractive patterns in the data, such as the reasons why sales of a specific good or service increased or decreased.
  • This type of material requires further investigation or attention.
  • One of the most crucial steps in data science is this one.

Step 5:  Executing a Comprehensive Analysis

  • Your aptitude in arithmetic, statistics, and technology will be put to the test in this step
  • You must employ all of the data science tools accessible in order to successfully crunch the data and obtain every insight imaginable.
  •  You might need to develop a predictive model that contrasts average clients with underachievers.
  • In your research, you may come across a variety of factors that are crucial predictors of who will purchase a service or good, such as age or social media usage.
  •  Once you’ve completed this stage, you may integrate your quantitative and qualitative data and put it to use.

  Step 6: Results of this Analysis Communication

  • It is critical to communicate your ideas and conclusions to the sales head after finishing each of these steps in order to make them understand their importance.
  • It will be beneficial if you can effectively communicate in order to meet the task you have been given.
  • A successful dialogue will result in action. On the other hand, an inappropriate interaction might lead to inaction.
  • To help the sales head comprehend it better, you must connect the facts you have gathered, your insights, and their knowledge
  • Start by describing why a product was performing poorly and why a certain demographic was not drawn to the sales pitch.
  • You can discuss the issue and then its resolution after stating the issue and need to create a powerful narrative with distinct objectives.

Skills required in Data Science

Following are the data science required skills:

  1. The basics of data science:

 The first and most important skill you’ll need is knowledge of the fundamentals of data science, machine learning, and artificial intelligence. comprehend subjects like –

  • What distinguishes machine learning from deep learning?
  • The distinctions between data science, business analytics, and data engineering.
  • Tools and jargon that are frequently used
  • What distinguishes supervised from unsupervised learning?
  • Classification and regression issues

2. Broad knowledge of statistical and probabilistic concepts

  • When learning to construct sentences, you must comprehend grammar in order to construct suitable sentences.
  • Similar to this, you must know statistics in order to produce high-quality models. Machine learning develops from statistics
  • Even the concept of linear regression has been applied in statistical analysis for quite some time.
  • It is crucial to understand concepts in descriptive statistics including mean, median, mode, variance, and standard deviation.
  •  Along with inferential statistics like hypothesis testing and confidence intervals, probability distributions also include sample and population, CLT, skewness, and kurtosis.

3. Knowledge of Programming Languages:

In addition to having a solid background in math and statistics, data scientists also need to be adept in complex statistical modeling software and have a solid understanding of programming.

Several computer languages are preferred for the profession of a data scientist. Here are a few of them:


  •  Python is a general-purpose programming language that may be used to build websites, run embedded systems, and perform data mining.
  •  Pandas is a Python data analysis tool that can import data from Excel spreadsheets and plot data using histograms and box plots, among other things.
  •  With the help of this library, data processing, reading, aggregation, and visualization are all made simple.

R Programming:

  • A set of tools for computing, manipulating, and displaying graphics is called R.
  •  It is more frequently used in academic settings than Python. 
  • The software provides a variety of statistical and graphical methodologies, such as time-series analysis, classification, clustering, and linear and non-linear modeling, in addition to machine learning algorithms that may be quickly and readily deployed.

4. Understanding of data exploration and data wrangling

  • Data wrangling is the process of organizing and cleaning up large and jumbled data sets so they are simpler to access and analyze.
  • The initial step in your data analysis process is exploratory data analysis (EDA).
  •  Here, you’ll learn how to interpret the information you have, and how to formulate the questions you want to ask.
  •  The most effective way to change your data sources in order to obtain the solutions to the issue you are presently thinking about.
  •  This is accomplished by examining patterns, trends, outliers, unexpected results, etc. On the other side, data wrangling and processing might be time-consuming yet ultimately lead to superior data-driven decisions.
  • Some of the frequently employed data wrangling and manipulation techniques include scaling, transformation, correcting data types, outlier treatment, and missing value imputation.

5. Knowledge of Data Visualisation:

  • Data visualization is one of the most important parts of data analysis.
  •  It has always been important to communicate information in a way that is both clear and aesthetically appealing.
  •  Data visualization is one of the abilities that data scientists need to have in order to communicate with end users more effectively.
  • Tools with user-friendly interfaces are available, including Tableau, Power BI, Qlik Sense, and many others.

Tools Require for Data Science

The top 5 tools required for data science are as follows:

1. Statistical Analysis System (SAS)

  • The SAS Institute created SAS, a statistical and sophisticated analytics tool.
  • It is one of the earliest tools for data analysis and was created primarily for statistical operations.
  •  Professionals and businesses that rely largely on sophisticated analytics and intricate statistical processes frequently utilize SAS.
  •  Various statistical libraries and tools are provided by this dependable commercial program, which can be utilized for modeling and arranging the provided data.

 2. Apache Hadoop

  • Apache Hadoop is an open-source framework that aids in the distributed processing and computation of massive datasets over a cluster of thousands of machines.
  • It can store and manage a lot of data.
  •  It is the perfect tool for handling complex calculations and handling enormous amounts of data.     

 3. Tableau

  •   Software for creating interactive visualizations of data is known as Tableau, and it has strong visuals.
  •  Businesses involved in business intelligence and analytics are the main users of it.
  • The ability of Tableau to communicate with various spreadsheets, databases, online analytical processing (OLAP) cubes, etc. is its most important feature.
  •  Along with these capabilities, Tableau can also display geographic data by mapping out longitudes and latitudes.

   4. TensorFlow

  •  TensorFlow is a potent library that revolves around deep learning, machine learning, and artificial intelligence algorithms and aids in developing and training models before deploying them on various platforms.
  •  Including servers, computers, and smartphones, to carry out the functions specified for each model.
  • One of the most adaptable, quick, scalable, and open-source machine learning libraries is TensorFlow, which is widely used in both production and research.
  •  TensorFlow is preferred by data scientists because it does numerical computations using data flow graphs.


 5. Excel

  •  Excel is a sophisticated analytical tool that is widely used in the field of data science because it makes it possible to create spreadsheets and data visualizations that are excellent for thorough data analysis.
  •  In addition to its extensive library of formulas, tables, filters, slicers, and other features, Excel lets users build their own unique formulas and functions.
  •  It can also be linked to SQL and utilized for additional data manipulation and analysis. Due to its user-friendly GUI environment, which makes data pretreatment simple, data scientists also utilize Excel for data cleaning.     

Jobs in Data Science

There are many data science jobs opportunity available as there is a huge increase in data science companies in India. There are many fresher jobs in data science for the following job roles: 

1. Data Scientist Job in India


  • Finding data sources for business requirements
  • Automated data collecting and management process data processing, cleaning, and integration
  • Process improvement using data science approaches and technologies
  • Analyzing a lot of data to identify trends and produce reports with advice
  • Working together with teams from business, engineering, and product

2. Data Analyst


  •  Employ automated techniques to extract data from primary and secondary sources
  • the creation and upkeep of databases
  • analyzing data, producing reports, and making recommendations
  • data analysis and trend predictions for the organization or project
  • improving data collecting and quality processes in collaboration with the rest of the team

3. Data Engineers


  • Create and keep up with data management systems
  • Gathering, acquiring, and managing data 
  • Researching both primary and secondary sources
  • Make analytics-based reports and updates for stakeholders.
  • Use data to identify hidden patterns and predict trends
  • Partnering with other teams to understand organizational objectives

Salary in Data Science

Data Scientist salaries in India range from 4.5 lakhs to 25.2 lakhs for those with 0 to 8 years of experience, with an average yearly pay of 10.6 lakhs based on 17.9k salaries.

Leave a Comment

Your email address will not be published. Required fields are marked *

Scroll to Top