Data Science & Big Data Analytics - Eduedge Pro

Program Duration

Duration – 11 months Training & Practice hours – 300+

Program Pedagogy

Online, Part-time Classroom intervention – once in 6 weeks

Batch Starts

Jan 8, 2022

Program Highlights

  • Industry-oriented


  • Applied learning on platforms

    Applied learning on platforms

  • Capston Project

    Capston Project

  • Industry recognized

    Industry recognized

  • Foreign collaboration

    Foreign collaboration

Certifying Bodies And Collaborations

About Our Program

The Post Graduate Program in Data Science and Big Data Analytics is being offered by Christ University, in collaboration with industry partners and collaborators such as EduEdge Pro, Google, Microsoft and SAS.

This Program is designed to provide deep rigorous training in Data Science, Business Analytics, Artificial Intelligence and Big Data Analytics, to those participants looking to upgrade themselves in the respective fields.



You will earn the prestigious degree from Christ University


You will gain hands-on experience in building Data Science models through platforms widely used in the industry


You will additionally earn certifications from HBS online, Tableau, Google, Microsoft & SAS


You will be provided 100% Placement assistance once you undergo our program


Algorithms, models and frameworks in Data Science and Analytics
Learn domain applications across business and industries to Data Science and Analytics
  • Predictive Modelling

    Predictive Modelling

  • Machine Learning

    Machine Learning

  • Big Data

    Big Data

  • Neural Network

    Neural Network

  • Statistical Modelling

    Statistical Modelling

  • Marketing Analytics

    Marketing Analytics

  • Financial Analytics

    Financial Analytics

  • Social Media Analytics

    Social Media Analytics

  • Data Mining

    Data Mining

  • NLP


  • Artificial Intelligence

    Artificial Intelligence

  • Sentiment  Analysis

    Sentiment Analysis

  • Data Visualization

    Data Visualization

  • Deep Learning

    Deep Learning

  • Consumer Analytics

    Consumer Analytics

  • Supply Chain Analytics

    Supply Chain Analytics


Certifications you could earn


Practical training in platforms in Data Science and Analytics
Participants can appappliedly domain concepts and frameworks in such platforms

Data Science - Platforms/Tools You Will Learn

Big Data - Platforms/Tools You Will Learn

Our Alumni at Work



Revenue and Margin Forecasting Demand Forecasting Cross-selling algorithms Predictive Modelling Customer Segmentation Social Media Analytics


Strategic Decision Making Portfolio Analytics Risk Analytics Fraud Detection Credit Card default forecasting


Performance and Defect Tracking Automation of Manufacturing units Energy and fuel Optimization Supply Chain Optimization Transport Monitoring system


Medical History Analysis Drug Discovery Virtual Assistant Medical Image Analysis Bioinformatics


  • Data Scientist
  • Data Analytics
  • Data Analyst
  • Business Analyst
  • System Analyst
  • Data Engineer
  • Machine Learning Engineer
  • Data Architect
  • Analytics Manager
  • Statistician
  • Business Intelligence Manager
  • Data Manager




Avg Salary


Work Experience

0-3 years


Python, Pandas, AWS, Maths and Statistics


Avg Salary


Work Experience

3-5 years


Python, Pandas, AWS, R, Data Science Modeling


Avg Salary


Work Experience

5-8 years


Python, Pandas, AWS, Tableau, Project Experience


Avg Salary


Work Experience

8-12 years


Python, Pandas, AWS, R, Google Cloud , Management Skills


Avg Salary


Work Experience

12-18 years


Python, Pandas, AWS, Tableau, Google Cloud , Project Experience, Management Skills


Avg Salary


Work Experience

18+ years


Python, Pandas, AWS, R , Tableau, Google Cloud , Management Skills, Data Visualization

Program Pathway & outline


  • Duration

    40 hours

  • Includes

    Comprehensive notes Practical examples Use cases Detailed Codes

  • Certification

    Tableau Desktop Certified Specialist

Platform you will learn

Python Essentials
  • Introduction to Python IDE's
  • Installation
  • Pre-requisites
  • Concept of Packages
  • Data Types & Data objects
  • Basic Operations
  • Control flow & conditional statements
  • Python Built-in Functions
Data Cleaning
  • Sub Setting Filtering Slicing Data
  • Mutation of table
  • Binning data
  • Renaming columns or rows
  • Type conversions
  • Setting index
  • Handling duplicates/missing data /Outliers
  • Creating dummies from categorical data
  • Data manipulation tools
Data Science Operations
  • What is NumPy
  • Overview of functions & methods in NumPy
  • Data structures in NumPy
  • Creating arrays and initializing
  • Reading arrays
  • Slicing and indexing
  • Combining arrays
  • What are pandas
  • Pandas Data Structures (Series & Data Frames)
  • Functions & methods
Data Visualization
  • Introduction to Matplotlib
  • Basic Plotting with Matplotlib
  • Different Line & Bar Plots
  • Tree Maps and Heat Maps
  • Complex Visualizations
  • Advanced Visualization methods
Data Scrapping and Wrangling
  • Data Scrapping
  • Finding data across sources
  • Querying an API directly Stocks, Weathers etc.
  • Browser-based Scrapping
  • Scrapping tables such as Wikipedia/IMDB
  • Data Wrangling
  • Integrating data
Visualization using Tableau
  • Introduction to Tableau
  • Installation
  • Pre-requisites
  • Connect to Data
  • Data Visualizations
  • Filtering functionality
  • Hierarchy Navigations ? Drill up/down
  • Forecasting
  • Dashboarding
  • Story - integrating everything


  • Duration

    50 hours

  • Includes

    Comprehensive notes Practical examples Use cases Detailed Codes

  • Certification

    SAS Statistical Business Analyst Professional Certificate

Platform you will learn

Statistics Primer
  • Measures of central tendencies
  • Measures of variance
  • Measures of frequency
  • Measures of Rank
  • Basics of Probability
  • Important Distributions
  • Conditional Probability (Bayes Theorem)
Statistical Methods & Hypothesis Testing
  • Descriptive vs. Inferential Statistics
  • Discrete & Continuous distributions
  • Concept of Sampling & types of Sampling
  • Hypothesis Testing and Applications
  • Statistical Methods and tests
  • Correlation and intricacies
Statistical Data Analysis
  • Exploratory data analysis
  • Descriptive statistics
  • Frequency Tables and summarization
  • Uni-variate Analysis (Distribution of data & Graphical Analysis)
  • Bi-Variate Analysis (Cross Tabs, Distributions & Relationships, Graphical Analysis)
Applied Econometrics using Python
  • Working with Financial Datasets and Time series
  • Webscrapping financial information using Python
  • Performing regression
  • PCA application for multivariate datasets
  • Forecasting models using GARCH
  • Time Series models


  • Duration

    60 hours

  • Includes

    Comprehensive notes Practical examples Use cases Detailed Codes

  • Certification

    Microsoft Certified: Data Scientist Associate

Platform you will learn

Introduction to Predictive Modelling
  • Concept of model in analytics and how it is used
  • Common terminology used in modeling process
  • Types of Business problems - Mapping of Algorithms
  • Different Phases of Predictive Modeling, Data
  • Exploration for modeling
  • Exploring the data and identifying any problems with the data
  • Identify missing/Outliers in the data
  • Visualize the data trends and patterns
Supervised Learning ? Regression
  • Linear Regression
  • Logistic Regression
  • Multivariate Regression
  • Ridge Regression
  • Lasso Regression
  • Problems with Regression
Supervised Learning - Classification Algorithms
  • K-Nearest Neighbor
  • Na?ve Bayes Classifier
  • Decision Trees
  • Ensemble Learning ? Bagging
  • Random Forest
  • Adaboost, Gradient Boost, XGBoost
  • Support Vector Classifier
Unsupervised Learning Algorithms
  • K-means clustering.
  • Hierarchal clustering.
  • Anomaly detection.
  • Neural Networks.
  • Principle Component Analysis.
  • Independent Component Analysis.
  • Apriori algorithm.
Neural Networks
  • Multi Layer Perceptrons
  • Convolutional Neural Networks
  • Recurrent Neural Network
  • Auto Encoder
  • Generative Adversarial Network
  • Graph Neural Networks
  • Applications


  • Duration

    90 hours

  • Includes

    Comprehensive notes Practical examples Use cases Detailed Codes

  • Certification

    HBS Online Certificate in Business Analytics

Platform you will learn

Applied Marketing Analytics
  • Analyze Marketing campaigns with Python
  • Analyze Social media data in Python
  • Perform advanced Marketing analytics such as Market Basket Analysis using Python
  • Customer Segmentation Analysis
  • Customer Analytics
  • Customer Churn Analytics.
Applied Financial Analytics
  • Working with open-source Financial Datasets and Time series
  • Web-scrapping historical time series
  • Fundamental data using Python
  • Portfolio Modeling with Python
  • Portfolio Optimization
  • Portfolio Analytics
  • Risk Analytics
  • Value at Risk (VaR) calculations
  • Monte Carlo Simulations and examples
Social Media Analytics
  • Analyse the unstructured textual data to derive meaningful insights
  • Text Mining and Natural Language Processing (NLP)
  • Word Clouds
  • Sentiment Analysis
  • Semantic network
  • Clustering
  • Extract user reviews of the product/services from Amazon
  • Extraction and text analytics in Python
  • LDA / Latent Dirichlet Allocation
  • Topic Modelling
  • Sentiment Extraction
  • Lexicons & Emotion Mining
Business Intelligence And Dashboarding
  • Introducing the Power BI Desktop
  • Extract data from various sources
  • Establish connections with Power BI Desktop
  • Perform transformation operations on data
  • Query Editor in Power BI.
  • Work with report elements
  • Build end-to-end industry styled Dashboards.


  • Duration

    60 hours

  • Includes

    Comprehensive notes Practical examples Use cases Detailed Codes

  • Certification

    Microsoft Certified: Data Scientist Associate

Platform you will learn

Big Data Primer
  • What is Big Data
  • Big Data in marketing, analytics, retail, hospitality, consumer good, defense etc.
  • Technologies for Handling Big Data
  • Introduction to Hadoop
  • Functioning of Hadoop
  • Cloud computing (features, advantages, applications)
Big Data Storage and Processing
  • Big Data storage systems
  • Relational Databases
  • NoSQL Databases: HBase, Graph DB
  • Distributed File Systems/HDFS
  • Cloud storage
  • Introduction to Big Data processing platforms
  • Data Volume: Hadoop, Spark
  • Data Velocity: Storm
  • Complex Event Processing
  • Cloud platforms
Data Engineering
  • Hadoop And MapReduce Programming
  • Data Management And Relational Database Modelling
  • NOSQL Databases And Apache Hbase
  • Data Warehousing
  • Data Ingestion With Apache Sqoop And Apache Flume
  • Building And Querying Data Warehouse With Apache Hive
Business Use-cases in Big Data

    Applied Case Studies to different domains and areas such as Social Media, Marketing Analytics, Financial Analytics, Supply Chain, HR Analytics, Google Analytics

Practicle Learning Through The Capstone Project


The Capstone Project gives you the opportunity to apply what you've learned about how to make data-driven decisions to a real business challenge faced by businesses.


At the end of this Capstone, you'll be able to ask the right questions of the data and know how to use different models and algorithms effectively to address optimization challenges.

  • Live Project
  • Designed With Analytics companies
  • Work With An Industry Mentor
  • Choose Domain Of Choice
  • Inter-disciplinary
  • Project Guidance
  • Hand-holding
  • Data Support
  • Domain Expertise Support
  • 2-month Duration

Designed with Analytics companies to give you invaluable experience in evaluating and creating data-driven decisions, the Capstone Project provides the chance for you to devise a plan of action for analyzing data and modeling algorithms itself to provide key insights and analysis. Once you complete your analysis, you'll be better prepared to make better data-driven models and algorithms.

Put Data Science and Big Data Analytics to practice
  • Predictive Modelling

    Predictive Modelling

  • Artificial Intelligence

    Artificial Intelligence

  • Neural  

    Neural Network

  • Data Mining

    Data Mining

  • Big Data Analytics

    Big Data Analytics

  • Machine Learning

    Machine Learning

Capstone Project examples


Market Basket Analysis

What could be sold together to enhance Marketing impact and Sales

Capital Markets

Asset Price Prediction

Application of Predictive Modelling over asset price prediction


Healthcare Analytics

Apply Data Science frameworks to predict disease outbreaks and run healthcare analytics


Credit default Prediction

Machine Learning and Predictive Analytics to understand credit defaults

Web and Social Media

Sentiment Analysis

Perform Text Mining and Sentiment Analysis to understand next steps in your Digital strategy

Supply Chain

Demand Forecasting

Apply Data Science algorithms to predict future Demand and hence optimize Supply Chain

Admission Process


    We have a dedicated admission counsellor who are here to help guide you in applying to the program. They are available to:

    • Address questions related to application.
    • Assist with Financial Aid (if Required)
    • Guide career role and opportunities after certified.
    • Help you to understand the program detail and pedagogy.
    • Complete your application to kick start the admission process.
    • Rate your various skills of OOPs language, quantitative and logical ability.
    • Submit application fee: ? 500/-
    • Submit the form successfully and scheduled your interview with us.
    • Interview is with admission committee, who will review the candidate profile.
    • Selection will be determined on the basis of academic records, work experience, test scores and interview.
    • Upon qualifying a confirmation letter for admission to the PG Diploma in Data Science will handover to the candidate.
  • Documentation

    After interview on the basis of confirmation letter , the required papers mentioned in the mandatory list of documents as per eligibility criteria. You would be required to submit your marksheets, education certificates, work experience proofs amongst other necessary documents.

  • Payment Processing

    Block your seat with the initial amount of fees and begin with your prep course and start your Data Science journey.

    Full or annual program fee to be deposited within 1 week of offer letter / program start ? whichever is earlier.

  • Confirmation

    Your admission will be confirmed basis the selection procedure, document authentication and fee payment.

    A welcome letter, ID card, student number and portal access will be shared upon successful completion of the admission process.

Investment for the Program

Full Program Fees

Indian Participants

15% off

International Participants

Add to cart
  • Scholarships

    Existing Christ students and Alumnus: Available Other participants: Available on outstanding merit record

  • Financing Options

    0% Interest EMI option available with partner banks Easy procedure with partner banks EMI as low as INR 14,000 per month

  • Corporate Discounts

    Available on nominations of 2+ participants Kindly contact us for further details

Individual Program Fees

Frequently asked question's about the course

Data scientists extract and interpret data to strengthen or align with a business’s overall goals. Data scientists work in big data, machine learning, or AI companies. However, experience in these types of organizations is not required as far as what you need to become a data scientist.

Data scientists work in tandem with data analysts, data engineers, business intelligence specialists, and data architects to create and maintain databases, analyze data, and communicate business insights. Their job is to identify the data analytics problems that offer the greatest opportunities to the organization.

Data science is an extremely broad field, entailing everything from cleaning data to deploying predictive models. It s rare for a data scientist to do it all. Most data scientists specialize in a specific function within the data processing cycle.

Data Scientist Role and Responsibilities

Data scientists work closely with business stakeholders to understand their goals and determine how data can be used to achieve those goals. They design data modeling processes, create algorithms and predictive models to extract the data the business needs, and help analyze the data and share insights with peers.

Some common data scientist responsibilities include:

Problem definition

The problem statement is the most crucial step towards solving any data analytics problem. Data scientists need to think of the problem statement in mathematical terms. For example, Why is product X underperforming and What data on user behavior might help explain it

Data collection and wrangling

Data wrangling is the process of cleaning, restructuring, and enriching raw data to make it analyzable. The primary goal of data wrangling is to reveal a deeper intelligence by gathering data from multiple sources and organizing the data for a broader analysis.

Exploratory analysis

Exploratory data analysis postpones any initial assumptions, hypotheses, or data models; instead, data scientists seek to uncover the underlying structure of the data, extract important variables, and detect outliers and anomalies.

Data processing

The data processing cycle refers to the set of operations used to transform the data into useful information. In this stage, the data is entered into a system, such as a CRM like Salesforce or a data warehouse like Redshift so that a data processing cycle can be established. Next, this process is deployed as a repeatable data model to enable long-term data analytics projects.

Model training and deployment

Data modeling represents the way data flows through a software application or the data architecture within an enterprise. It s almost like a blueprint that establishes relationships between different business entities to show how data is collected and stored.

Documentation, visualization, and presentation

Data scientists are expected to document their processes, providing sufficient descriptive information about their data for their own use as well as their colleagues and other data scientists in the future. Visualization is perhaps the most crucial aspect of the data function since computational statistics are only meaningful if they can be understood and acted upon by the organization.

There are several important Job Roles in Data Science most important one are :

  • Data Analyst.
  • Data Engineers.
  • Database Administrator.
  • Machine Learning Engineer.
  • Data Scientist.
  • Data Architect.
  • Statistician.
  • Business Analyst.

Most data scientists use the following core skills in their daily work:

Statistical analysis

Identify patterns in data. This includes having a keen sense of pattern detection and anomaly detection.

Machine learning

Implement algorithms and statistical models to enable a computer to automatically learn from data.

Computer science

Apply the principles of artificial intelligence, database systems, human/computer interaction, numerical analysis, and software engineering.


Write computer programs and analyze large datasets to uncover answers to complex problems. Data scientists need to be comfortable writing code working in a variety of languages such as R, Python, and SQL.

Data storytelling

Communicate actionable insights using data, often for a non-technical audience.

Tableau Desktop Certified Specialist

This certificate is for those who have foundational skills and understanding of Tableau Desktop and at least three months of applying this understanding in the product.

Tableau Certification Will Be Relevant in Business Intelligence and Visualization. All-Inclusive Bundle To Help You Amplify Your Skills and Get Certified.

Become an expert in Data Analysis. Business Analytics. Business Dashboards. Data Visualization. Drag & Drop Reporting. Data Discovery.

SAS Statistical Business Analyst Professional Certificate

For individuals who want to analyze big data with a variety of statistical analysis and predictive modeling techniques.

Tableau Certification Will Be Relevant in Business Intelligence and Visualization. All-Inclusive Bundle To Help You Amplify Your Skills and Get Certified.

Successful candidates should have experience in the following areas: Machine learning and predictive modeling techniques. Application of machine learning and predictive modeling techniques to big, distributed and in-memory data sets.

  • Pattern detection.
  • Experimentation in business.
  • Optimization techniques.

Microsoft Certified: Data Scientist Associate

This course is designed for data scientists with existing knowledge of Python and machine learning frameworks like Scikit-Learn, PyTorch, and Tensorflow, who want to build and operate machine learning solutions in the cloud.

Learn how to operate machine learning solutions at cloud scale using Azure Machine Learning. This course teaches you to leverage your existing knowledge of Python and machine learning to manage data ingestion and preparation, model training and deployment, and machine learning solution monitoring in Microsoft Azure.

HBS Online Certificate in Business Analytics

Develop a data mindset and the analytical skills to interpret and communicate date while applying the concepts to real business problems.

You will learn how to apply Business Analytics tools and methods across different domains as well learn the inter-disciplinary approach to Analytics problem-solving across various industries.

Google Data Analytics Professional Certificate

Get started in the high-growth field of data analytics with a professional certificate from Google. Learn job-ready skills that are in demand, like how to analyze and process data to gain key business insights.

Data analysts prepare, process, and analyze data to help inform business decisions. They create visualizations to share their findings with stakeholders and provide recommendations driven by data.

Get a job in data analytics, with help from Google,Learn the foundations of data analytics, and get the job-ready skills you need to kick-start your career in a fast-growing field.

While we do recommend having some knowledge of at least one programming/scripting language or computing environment, many of our attendees come to us with little to no experience with programming.

Our pre-program coursework includes

  • Tutorials on Programming basics
  • Tutorials on Statistics

This would help you get ramped up for the program!

The pre-programming experience required by our course is great to have, but not necessary.

Our pre-program coursework includes tutorials on Introduction to R Programming to get you ramped up for the program! There you will learn to use basic programming constructs like how to assign variables, call functions, work with loops and decision tree logic.

In the program, we will assume that you do not come with any programming expertise and hence, will build your skills from scratch.

We work hard to ensure that no prior statistics knowledge is required. We will teach you all the basics you need to know before and during the bootcamps. We cover Statistical Finance concepts, correlations, hypothesis testing, and Bayes rule in the pre course bootcamp (online), all at a level appropriate for someone with no/little statistics experience.

Peer networking

In addition to the training program, you will have ample opportunity to network with the peers in your cohort. Prior to coming to the program, you will have access to your cohort where you can meet and engage with your peers before, during, and after the program.

Industry networking

Throughout the program, you will be taught and mentored by Data Scientists working/having worked with leading companies.

Industry networking

Communicate actionable insights using data, often for a non-technical audience.

Placement Leads

We have networks with leading industry participants including banks, financial institutions, consulting firms and analytics firms; you can leverage that network for connecting with the industry and finding suitable opportunities in Data Science. Once you are trained and ready, we will help you with placement leads that can help you land your dream role in Data Science.

Having said that, its entirely your performance and skill-sets that will define your journey in landing your dream job. We cannot guarantee that you will get a job after completing the program. Obtaining a job is strictly based off one s own skill sets. Upon completion of the program, we will provide you with a certificate that you can print and/or add to your LinkedIn profile.

Networking opportunities

You will have great networking opportunities at the program. Our industry faculty work in the industry in leading positions and that will provide a great opportunity for you to network. Besides, EduEdgePro, through its vast industry network, will help you reach out to the leading financial firms for placements.

Yes, this is an online program.

There are 2 versions

  • Self-paced online
  • Live synchronous training