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Data Science Course Syllabus by Top 4 Institutes

Are you looking for Data Science Courses but not sure which Institute to enroll in? Don’t worry, we have got you! Data Science in simple words is an observation of data to extract meaningful insights for business. These data play a crucial role for any organization to grow their business by making profits. In today’s time there is an ocean of data available online, but finding the right data is a task which can only be performed by Data Science experts. It is important as it combines tools and technologies to extract meaning from data. The word “Data Science” was introduced in the 60’s era and later modified in the 90’s by computer science professionals. Data Science is a rapidly growing field offering a wide range of job opportunities to individuals.

Data Science Course Syllabus (1)-compressed

Who is a Data Scientist?

A data scientist is a professional who uses different methods and techniques to extract meaningful data insights and knowledge from structured and unstructured data. Data Science experts are individuals having a deep knowledge of statistics, mathematics and computer science. Their job involves analyzing and identifying patterns, and trends and deriving important information to help organizations make data-related decisions. They often work as finance, healthcare and e-commerce experts in many organizations. They are very important for the organization as the reports prepared by them help in gaining a competitive advantage and improve the decision-making process.

The Key Responsibilities of Data Scientists Are:

  • Data Collection: Gathering relevant and important decisions for the organization is the core role of a Data Scientist.
  • Data Preparation: Preparing the right data through analysing them and for other consistencies.
  • Data Analysis: This is to analyse and understand the patterns and trends of data.
  • Communication: Preparing reports and presentations for the understanding of non-technical stakeholders.
  • Deployment: Converting models to real-world use systems and making them accessible.

Advantages of Data Science:

  • Decision-Making: With the help of Data Science reports, organizations can make the right and informed decisions. Through analysing the data, you get insights into customers’ behaviour, and market trends and make strategic decisions.
  • Predictive Analytics: Data Science allows forecasting future trends, and outcomes. Sectors such as healthcare, the financial market, and marketing predicting future events can provide a competitive advantage.
  • Cost Saving and Efficiency: Data Science contributes to increased efficiency and productivity resulting in cost-saving and better resource allocation.
  • Understanding Customers and their requirements: Analyzing customer data will help in tailoring products as per customers’ needs and preferences, which will help in understanding customers which will lead to loyal customers.
  • Fraud Detection and Security: Data Science plays a crucial role in preventing fraud and helps organizations in identifying fraud and taking security measures.
  • Healthcare Advancements: identifying disease patterns, developing effective treatment plans through research and analysing large data plans.
  • Optimized Marketing Strategies: Analysing customer behaviour, preferences and response helps businesses to optimize their marketing strategies and allocate resources through data-driven insights.
  • Innovation and Research: With the help of new tools that help in analyzing insights from the large data.
  • Competitive Advantage: Data Science helps to pull out insights that help in the better performance of businesses offering them a competitive advantage.
  • Continuous Improvement: There is a lot of scope for improvement for any organization with the help of Data Science through analyzing situations and adapting those to stay ahead of the competition.
  • Economic and Social Impact: Poverty, education and healthcare are some of the societal challenges that can be addressed by tackling complex global issues through right policymaking.
  • Process Optimization: Cost of organization’s internal processes such as supply chain management, logistics and inventory control through Data Science.

Some of the Job Profiles in Data Science:

  • Data Analyst: Collecting data to support the organization in decision-making.
  • Data Scientist: Analyzing data to support decision-making is an essential role of the Data Scientist.
  • Machine learning Engineer: Designing algorithms to solve business problems.
  • Data Engineer: Developing tools to help generate data and analyse them is the key responsibility of the Data Engineer.
  • Business Intelligence Analyst: With the help of tools and techniques, a Business Analyst helps companies in important decision-making.
  • Data Architect: Designing and creating data systems and architects for large-scale data processing.
  • Statistician: Using statistical techniques to interpret data for experiments is the job role of a statistician.
  • Quantitative Analyst: To use mathematical strategies to solve risk management problems.
  • Data Science Consultant: Offering guidance to organizations on how to use data science for business needs.
  • Data Product Manager: Developing a conceptualized product for the organization is a crucial job responsibility of a data product manager.
  • Data Science Trainer: Teaching data science concepts and techniques in any educational institute.
  • Healthcare Data Scientist: Applying data science techniques to healthcare data to take out patient details, medicine history and other medical information.

Some of the Institutes offering Data Science Courses through Online and Offline methods:


IIM SKILLS is a growing online institute offering world-class education at an affordable price. It is headquartered in New Delhi, India with a global presence in 23 countries in Asia. IIM SKILLS is known for its courses that are designed by skilled trainers as per industry standards for its learners. At IIM SKILLS they provide an MBA Program in Data Science. They have the best-designed data science course syllabus that covers all the topics and offers 100% tool-oriented training. The course includes 11 months of live lectures and rigorous training with practical assignments.

Course Name: Data Science Course

Highlights Of The Course:

  • Live Data Analyst Course
  • Live Lectures
  • Sessions dedicated to solving doubts.
  • Interest-Free EMI
  • 100% Assured Internship | Interview Guarantee
  • Preparation for Mater Certifications in Alignment with IIM Skills (Govt. Of India) Microsoft and Google
  • 24*7 Online Support
  • Practical Learning
  • Lifetime Access To The Recorded Study Materials
  • Resume Building
  • Optimization of Linkedin Profile
  • Hassle-Free Access from Any device anytime.
  • Mock interview Sessions
  • Knowledge of Tools and software
  • Interview Preparations
  • Capstone Projects
  • Industry experts as Data Science mentors

Data Science Course Syllabus:

  • SQL
  • Python
  • Jupyter
  • Chatgpt
  • Flask
  • Linear and nonlinear models
  • Inferential Statistics
  • Regression
  • Model Selection
  • Web-based Interface Analysis
  • Dashboards With Python, and More

With this Data Science Course at IIM SKILLS, learners can extract, refine and use their skills for a better future.

You learn advanced concepts in data science and implement them in practical assignments thereby gaining proficiency in the subject. You learn to craft data models that forecast trends and help to make better  business decisions.


Basic knowledge of Data and Analytics

It would be an added advantage oif you had computer science in graduation

Other Professional Courses from IIM SKILLS

Contact Number: +91 9580 740 740

Contact Email: [email protected]

2. Upgrad-

Upgrad is one of the well-known online platforms that offer several courses including a Data Science Course. Their data science course syllabus is best designed and upgraded on a regular basis. It is known to be one of the leading online institutes for designing and delivering courses that are as per market standards and are approved by top skilled trainers across the globe.

UPGRAD’s Post Graduate Program in Data Science & AI is considered to be the #1 online program. With the help of this program, you can master 20 programming languages and tools in Data Science & AI and graduate with a recognized degree equivalent to 1-year Postgraduate diploma in Canada.

Key Highlights of the Course:

  • Modules integrated with AI
  • Career essential soft skills program
  • IIT Bangalore Alumni Status
  • 60+ Industry projects
  • Student support available
  • Job opportunities portal
  • Interviews preparation
  • Personalised industry session
  • No-cost EMI option
  • Complimentary Python programming bootcamp
  • Designed for working professionals
  • 14+ programming tools and languages
  • Career Mentorships Session 1:1
  • High-Performance Coaching
  • AI-Powered Profile Builder
  • Career Bootcamp
  • Daily Doubt Resolution Support

Top Skills You Will Learn:

  • Predictive Analysis Using Python
  • Machine Learning
  • Data Visualisation
  • Big Data
  • Natural Language Processing

Who Is This Programme for?

  • Engineers
  • Marketing and Sales Professionals
  • Freshers
  • Domain Experts
  • Software & IT Professionals

Job Opportunities Post this Course on Data Science:

  • Data Scientist
  • Data Analyst
  • Data Engineer
  • Product Analyst
  • Machine Learning Engineer
  • Decision Scientist

Eligibility for the Course at Upgrade:

Minimum 50% in graduation. No coding experience is required.

Tools Covered in this Course for Data Science :

  • Python
  • Excel
  • MySQL
  • Tableau
  • Apache Spark
  • Flume
  • Hadoop
  • Hive
  • Power BI
  • Mongo DB
  • Shiny
  • Keras
  • Tensor Flow
  • Apache HBase
  • Amazon Redshift
  • Apache Airflow
  • Open CV
  • AWS
  • Map reduce
  • Amazon EC2
  • Kafka

Data Science Course Syllabus:

The Course at Upgrad is divided into two parts:

Course 1- Data Toolkit (13 Weeks)

  • Introduction to Python
  • Programming in Python
  • Python for Data Science
  • Data Visualization in Python
  • Exploratory Data Analysis
  • Credit EDA Assignments
  • Inferential Statistics
  • Hypothesis testing
  • Basics of SQL
  • Advanced SQL
  • SQL-RSVP Movies Case Study

Course 2- Machine Learning (10 Weeks)

  • Linear Regression-1
  • Linear Regression-2
  • Linear Regression Assignments
  • Logistic Regression-1
  • Logistic Regression-2
  • Classification using Decision trees
  • Unsupervised Learning- Clustering
  • Basics Of NLP and Lexical Processing
  • Business Problem Solving + Intro to GIT and GitHub
  • Case Study-Lead Scoring

Post Course 2 (Machine Learning) students can choose any of the below 5 specialisations as per their background and career aspirations:

  • Data Analytics
  • Business Analytics
  • Deep Learning
  • Natural Language Processing
  • Data Engineering

Industry Projects in the Data Science and AI Course:

Learn from India’s leading Data Analytics Data Science & AI instructors:

  • Engage in collaborative projects with student mentor interaction
  • Benefit in learning with expert mentors
  • Personalised subjective feedback on the submissions to felicitate improvements

Advantages of the Course at Upgrade:

  • Learning Support
  • Career Assistance
  • Practical Learning and Networking
  • Job Opportunities

Course Fees: INR 3,25,000 (Inclusive Taxes)

Contact Number: 1800 210 2020 (Indian Nationals)

+9180 4560 4032 (Foreign Nationals)

3. Coursera-

Coursera is another leading online platform for providing various courses including Data Science. It prepares you for a career as a Data Analyst. Coursera in association with IBM offers a Data Analyst Professional Certificate. Learners don’t need prior experience to do this course. Also, their data science course syllabus offers detailed practical & professional training as per the latest needs of the market trends.

Learnings from this Course:

  • Up-to-date practical skills and tools that data analysts use in daily roles
  • Develop a working knowledge of Python language for analyzing data using Python libraries like Panda, and NumPy, and invoking APIs and web services
  • How to visualize and data findings using various charts in Excel spreadsheets, BI tools like IBM Cognos, analytics & Tableau
  • Gain technical experience through hands-on labs and projects to build a portfolio to showcase your work

Skills you’ll Learn:

  • Data Science
  • Spreadsheet
  • Data Analysis
  • Python Programming
  • Microsoft Excel
  • IBM Cognos Analytics
  • Dashboards
  • SQL
  • NumPy
  • Pandas
  • Data visualization
  • Pivot Table

Highlights Of the Course:

  • Soft Skills Training
  • Resume Review
  • Interview prep
  • Career Support

Data Science Course Syllabus:

  • Introduction to Data Analytics
  • Excel Basics for Data Analysis
  • Data Visualization
  • Python for Data Science, AI and Development
  • Python project for Data Science
  • SQL for Data Science
  • Data Analysis with Python
  • Data Visualisation with Python
  • IBM Data Analyst Capstone Project

4. Imarticus Learning-

Imarticus Learning offers a range of Data Science, Finance, Analytics, Technology, Fintech and Business Analytics with placement assistance to help students secure a better career for themselves. The programs are designed by experts to equip them with the skills.

Highlights of the Data Science Course:

  • Job Assurance
  • Job Specific Curriculum
  • Live Learning Module
  • Real World Projects
  • Dedicated Career Services
  • KPMG India KOE Organised

Data Science Course Syllabus:

Basics Programming-

The basics of programming for those who are non-programmers will be taught. Learn programming concepts and implement these concepts to get code.

What will you achieve?

  • Build a solid foundation of programming
  • Practice coding skills with 20+ coding skills


  • Introduction to Programming
  • Variables and Arithmetic Expressions
  • Functions
  • Data Types
  • Conditions and conditional statements
  • Lists
  • OOPS


The modules cover Excel for the Data Science course syllabus. Learn the importance of Excel for using data analysis, and have a strong hold.

What will you achieve?

  • Excel for Data analysis
  • Summarise Data pivots, and charts


  • Basics of Excel
  • Importing data
  • Formatting in Excel
  • Excel formulae
  • Data Validation
  • Calculation

Reporting using Excel:

  • Lookup and reference
  • Pivot Tables
  • Charts
  • What-if Analysis
  • Intro to Macros


Learners are introduced to SQL programming and data from the database.

What will you achieve?

  • Build a strong SQL foundation for data query
  • Create datasets for the database


Basics of SQL

  • Introduction to SQL
  • DDL Statements
  • DML Statements
  • DQL Statements

Advanced SQL: Part 1

  • Aggregate functions
  • Date functions
  • Union and all introspect
  • Joins

Advanced SQL: Part 2

  • Views and Indexes
  • Sub-Queries
  • Exercise on SQL

Python Programming:

The module covers Python programming which is included in the data science course syllabus. How to work with multiple Python data science libraries to execute essential tasks like mathematical calculations, data manipulation, etc.

What will you achieve?

  • Master Python Program
  • Data Analysis using Python Program Libraries
  • Create useful charts for data visualisation


Introduction to Python

  • Python Introduction
  • Variables
  • Functions
  • Python Operators
  • Python Flow Controls
  • Conditional Statements
  • Loops

Python Objects + List Comprehension

  • Python collection objects
  • Strings
  • List
  • Tuple
  • Dictionary
  • List Comprehension

User-defined and Lambda Functions:

  • User-defined function
  • Functions Arguments
  • Lambda functions


  • Introduction to NumPy
  • NumPy Array
  • Creating NumPy Array
  • Array Attributes
  • Array Methods
  • Array Indexing
  • Slicing Array
  • Array Operations
  • Iterations through Array


  • Introduction to Pandas
  • Pandas Series
  • Creating Pandas Series
  • Accessing Series Elements
  • Filtering a Series
  • Arithmetic Operations
  • Series Ranking and Sorting
  • Checking Null Values
  • Concatenate a Series

Data Frame Manipulation:

  • Pandas Dataframe- Introduction
  • Dataframe Creation
  • Reading Data from various files
  • Understanding data
  • Accessing Dataframe elements using indexing Dataframe sorting
  • Ranking in Dataframe
  • Dataframe Concatenation
  • Dataframe Joins
  • Dataframe Merge
  • Reshaping Dataframe
  • Pivot tables
  • Cross tables
  • Dataframe Operations
  • Checking duplicates
  • Checking rows and columns
  • Replacing values
  • Grouping Dataframe
  • Missing value and analysis treatment

Visualisation Part-1

  • Visualisation using Matplotlib
  • Plot styles and settings
  • Histogram
  • Boxplot
  • Pie chart
  • Scatter Plot

Visualisation Part-2

  • Visualisation using seaborn
  • Strip Plot
  • Distribution Plot
  • Joint Plot
  • Violin Plot
  • Pair Plot
  • Count Plot
  • Heatmap


  • Summary Statistics
  • Missing value treatment
  • Dataframe Analysis using Groupby
  • Advanced data explorations

Statistics for Data Science:

Review, analyse, and draw conclusions from data are introduced under this module. Quantified mathematical model variables for data analysis.

What will you achieve?

  • Build a strong foundation for statistics
  • Implement mathematical models for data analysis

Topics For the Data Science Course Syllabus:

Introduction to Statistics

  • Random Variables
  • Descriptive Statistics
  • The measure of Central Tendency
  • Measure of dispersion
  • Skewness and Kurtosis
  • Covariance and Correlation

Probability Theory:

  • What is Probability?
  • Events and Types of Events
  • Sets in Probability
  • Probability basics using Python
  • Conditional probability
  • Expectation and Variance

Probability Distributions:

  • Discrete Distributions
  • Uniform
  • Bernoulli
  • Binomial
  • Poisson
  • Contribution Distributions
  • Uniform
  • Normal
  • Probability distributions using Python

Hypothesis Testing:

  • Introduction to Hypothesis testing
  • Terminologies used in Hypothesis testing
  • Procedure for Testing a Hypothesis
  • Test for the population mean
  • Small Sample Tests
  • Large Sample Tests
  • Test For Normality

Statistical Tests:

  • One-Way ANOVA
  • Assumptions
  • Anova Hypothesis
  • Post Hoc Test
  • Chi-Square Test
  • Chi-Square Test steps
  • Chi-Square Example

Machine Learning:

This module explains how machine learning and different algorithms of machine learning work. It also covers the optimisation of models and model tuning works. Learn how machine learning is applied to solve problems.

What will you achieve?

  • Learn machine language algorithms and their application
  • Analyse data and its predictions
  • Solve real-world business problems using Machine Learning

Topics Of Data Science Course Syllabus:

Introduction to machine learning:

  • Introduction to machine learning
  • Machine learning modelling flow
  • Parametric and Parametric Algorithms
  • Types of Machine Learning

Linear Regression using OLS:

  • Types of Linear Regression
  • OLS Model
  • The math behind linear Regression decomposition variability
  • Metrics to evaluate model
  • Feature scaling
  • Feature Selection
  • Regularisation Techniques

Project on Linear Regression:

  • Project- Property Price Predictions
  • Class Assessment on Linear Regression

Logistic Regression:

  • Intro to Logistic Regression
  • Maximum Likelihood Estimation
  • Performance metrics

Model Tuning Techniques:

  • Performance Measures
  • Bias Variance Trade-off
  • Overfitting and Underfitting Problems
  • Cross Validation

Project on Logistic Regression:

  • Project- Vaccine Usage Prediction
  • Home Assignments On Logistic Regression

Decision Trees:

  • Introduction to decision trees
  • Entropy
  • Information Gain
  • Greedy Algorithm
  • Decision Tree: Regression
  • Gini Index
  • Tunning of decision tree-Pruning

Specialisation Track:

At the end of the core module, learners’ performance is evaluated and based on that they are assigned a specialisation track with a dedicated mentor for optimal learning.

After completing the course, the following are the Job Roles:

  • Data Scientist
  • Data Analyst
  • Business Analyst
  • Business Intelligence Specialist
  • Business Intelligence Specialist
  • Business Analytical Professional
  • Analytics Manager
  • Data Science Consultant
  • Machine Learning Engineer

Course Tenure: 6 Months

Course Name: Executive Post Graduate Programme

Course Fees: INR 6736/ Month

FAQs About Data Science Course Syllabus:

Qns 1. What is the learning in the Data Science Course?

While doing a Data Science Course, learners will learn to collect and manage data responsibly and then use it to make crucial decisions.

Qns 2. Why should I choose the Data Science Course?

The Data Science Course syllabus offers opportunities for career advancement and it’s a booming industry across the globe.

As a Data Scientist, one can help companies in making informed decisions, improve their operations and drive growth.

Qns 3. What are the biggest challenges in Data Science faced by Data Scientists?

Some of the common Data Science challenges faced by Data Scientist are:

  • Data Security
  • Identifying business issues
  • Generating data from multiple sources
  • Efficient Collaboration


Data Science is booming and one of the recognised and well-paid industries too. Due to high demand and technical skill set jobs in data science tend to pay well. It has become essential for almost every sector. Many institutes offering courses in data science has upgraded data science course syllabus which is designed as per industry expectations. We have got you names of some known and renowned institutes hence, It is advisable to do your research as well before you enrol on any Data Science Course Institutes

I am skilled at researching any topic requested and values creating unique yet compelling articles for my clients. My passion for writing content and blogs has motivated me to pursue a career in content writing from IIM Skills. Before this, I have 6+ years of experience in the Banking and Media Industry with EiPi Media, Hindustan Times(Shine.com), Royal Bank of Scotland and Citibank.

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