Post Graduate Program in Data Science- Henry Harvin® in Chandigarh

Post Graduate Program in Data Science in Chandigarh

Henry Harvin® Ranks in Top 30 most Trusted Companies by Insight Success

  • ★ ★ ★ ★ ★ 4.8/5 Ratings
  • 1.1 Million Learners
  • 3117 Learner’s Rating
  • 81% Report Career Benefits

Expedite your Career with Prestigious Post Graduate Program in Data Science | Learn Data Science with India’s #1 course and Get Ahead in Your Career | Learn Extensive use of Tools & Technologies and Analytics Techniques | Get a skill diploma from the prestigious Jain University

Starts In 1 day

26 Feb 2024

EMI Starting at

₹ 10944/month

Total Program Fee

₹ 98500

Learning Period

288 Hours

Placed Learners

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Accreditations & Affiliations

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World’s #1 Upskilling & Reskilling Institute

Key Features

Get Certified from a Prestigious University

Get the prestigious Skill Diploma from a prestigious university after the completion of the Post Graduate Program in Data Science 

10 in 1 Program

Training + Projects + Internship + Certification + Placement + E-Learning + Masterclass + Hackathons + Gold Membership

25+ Industry Graded Projects

Our Trainers are well versed in the subject matter with 19+ Experience. They Focus on giving Learner Industry Graded Projects, as per the Curriculum during the training for Practical and In-Depth Learning of the Subject Matter

100% Job Assistance and Internship

Weekly Job Support + E-Learning Access + Skill Enrichment Sessions for Interview + 52+ Masterclass Sessions

Capstone Projects

Capstone Projects based on the modules for the Specialization of Knowledge and Skills Gained. This course offers you Specific Capstone Projects for a cumulative understanding of the topic. After the completion of this project, the learner will be termed a Data Scientist

12-Month Program

6 Month Course + 6 Month Internship. The Curriculum is designed by the Industry Expert for Substantial Knowledge on the Subject matter. The Modules are allotted dedicated enough time frame for detailed knowledge of the Course

Upcoming Cohorts

Our Placement Stats

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Average salary hike


Access the best jobs in industry

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Maximum salary hike

About the Post Graduate Program in Data Science

Why Data Science?

According to a recent evaluation, more than 93% of firms use Artificial Intelligence for enhanced products and services

  • 43% of Professionals in Business Analytics have work experience of 3 years
  • 67% of Jobs are open for fresher or professionals with experience of fewer than 5 years
  •  It is estimated, that by 2023, jobs in  Data Science and analytics arena will have a void of 2.9+ Million jobs   
  • The Data Science Industry is estimated to be growing at a remarkable rate of 33.7% CAGR (Compound Annual Growth Rate)
  •  It will become one of the Top 5 Leading Markets in Big Data by the end of 2023

“Big Data is at the Foundation of all Mega Trends that are happening”   - Chris Lynch 


  • 12 months: Instructor-Led Live Online Classroom Sessions                  
  • Live Projects for improved understanding of the subject 
  • Capstone Project as a Culminating Assignment 
  • Masterclass sessions

Trainers at Henry Harvin®

  • Trainers with 23+ experience in the Industry with Global Certification 
  • Expertise on the Topics and Tools with expansive Teaching Experience of having trained 897+ Individual Globally                                              
  •  Aspirants are free to attend Multiple Sessions with Multiple Trainers

Become Job Ready

On the Successful Completion of the Post Graduation in Data Science form Henry Harvin® 

Be Exposed to the High Paying Jobs and Fill the Void of 3+ Million Jobs Globally

Alumni Status

Become a part of the Elite Analytics Academy of Henry Harvin® & join the 4,00,000+ strong alumni network worldwide

Learning Benefits Of Data Science

  • Be well accomplished  in analytics tools and technologies such as Python, Tableau, SQL
  • Know about the Machine learning techniques such as Regression, Predictive Clustering, Time Series Forecasting, Classification, etc.
  • Create an analytics framework using statistics and data modeling to structure any Business problem 
  • Learn to use  data cleaning and data transformation operations using tools and Techniques
  • Learn Comprehensively about  Deep Learning, Natural Language Processing (NLP)
  • Be Job-ready for the post of Data Scientist, Data Engineer, or Analyst for top Analytics companies  
  • 9 in 1 Program: Training + Projects + Internship + Certification + Placement + E-Learning + Masterclass  + Hackathons + Gold Membership

Course Duration

288 Hours training.

Professional Perks From CMAP + CSSE-GB Course

  • Evolve and enhance your Marketing Analytics + Management + decision-making + problem-solving skills.
  • Achieve eligibility for better job opportunities and networks.
  • Upgrade your CV with certification credentials CMAP + CSSE-GB and get professionally developed.
  • Make yourself eligible for higher positions with a specialization in the Marketing Analytics and Six Sigma Green Belt techniques.
  • Distinguish your profile from peers during Job Interviews
  • Earn a Rewarding Certification- 'Certified CMAP + CSSE-GB’
  • Build a Startup in one of the most rewarding fields of today- Analytics!
  • Support a Startup with improved Process and Performance that leads to high-quality products and services.

Knowledge Gains From CMAP + CSSE-GB Course

  • Understand the DMAIC methodology improvement cycle.
  • Analyze and Control Business Problems using Analytics Tools like R, Python, SAS & others.
  • Learn to get invaluable insights into your business by structuring and interpreting data which is the basic tool of Marketing Analytics.
  • Master the skill of quality management to be able to improve the working with skill sets of Six Sigma Course.
  • Understand emerging technologies that can assist you to focus on marketing analytics for the growth of the industry.
  • Gain expertise in the real-time application of the learned concepts.
  • Learn to calculate brand value and customer value over time to build marketing strategies.
  • Learn to make predictions that can avoid spams and can make specific marketing decisions to improve sales and ROI.
  • Explore+Analyse+Solve Business Problems using Analytics Tools like Python & Advanced
  • Understand 'What' & 'How' aspects of Data Collection
  • Get insights from Industry Best Practices for Data Measurement & Analysis
  • Communicate Business Objectives in a focused manner with data analysts & help evaluate results of data analysis better.
  • Develop Goal-Oriented Business Strategies
  • Gain Full View of Customers across Different Channels by Accessing + Integrating + Analyzing Customer Data through Multiple Sources & Engage Customers in Real-Time
  • Understand the Affinity of Product by analyzing transactional data.
  • Understand Analytics-based Financial decision-making to drive company's ROI
  • Take Actionable Data-Driven Decisions to increase market share
  • Helps with Adjusting to Macro Changes in predictive models.
  • Cultivate capabilities in Fact-based decision-making & Data-driven problem-solving
  • Gain the skill to explore + analyze + solve management problems using 20+ Management Tools
  • Sharpens Business Acumen & commitment to improving processes
  • Derive useful Information from Data using analytic tools
  • Learn essential Project Management + Leadership skills
  • Gain the skill to make Customer-Centric Actions at every stage
  • Understand the science to develop high-quality products/services
  • Develop the capability to look beyond the present field with this domain and industry independent methodology

Course Facilitators

Henry Harvin’s Training is delivered by Seasoned Industry Experts with specialization. These talented practitioners have valuable exposure, experience and success across diverse industries and are also empanelled as subject matter specialists with Henry Harvin Management Academy.

Gold Membership Program

Get 1-Year Membership of Henry Harvin Analytics Academy and avail the following benefits:

  • E-Learning Access: Includes Recorded Videos, Games, Projects, Case Studies
  • Brushup Sessions: 12 Monthly Brushup Sessions for 1-Year Worth Rs.6000 for Free
  • Internship: Guaranteed Internship with Henry Harvin or partner firms
  • Job Opportunities: Job Opportunities regularly on mail
  • Interview Skills: Support in Clearing Interviews with Startups and Top Corporates

5+ Projects covered (Hands-on Practical Experience)


  • HR: Analyze the Attrition rate of Employees
  • Sales: Predicting Department wise Sales
  • Multi-Domain: Business Analytics Optimization
  • Marketing: Website Trend Analysis
  • Financial Analysis: Stock Market Prediction
  • Finance: Analyze ETF Trends

Six Sigma:

  • Quality of Work Life in an Organization of Employees
  • Improve Total time & Rolled Throughput Yield
  • Optimization of Average call times in a BPO-Voice Process
  • Defect Reduction in Die Casting
  • Improving Internal SQR in XXM Activities

Know the complete offerings of our Post Graduate Program in Data Science in Chandigarh

You Get 10-In-1 Program

Two-way Live Training Course


Hallmark Certification


10+ Projects


Guaranteed Internship


Personalized Job Support


Student Engagement & Events




E-Learning Access


Gold Membership


Entrepreneurship Mentorship


Post Graduate Program in Data Science Curriculum

  • Module 1: Programming for Non Programmers

    Programming is an increasingly important skill. This course will establish your proficiency in handling basic programming concepts. This program will help you to gain basic programming concepts like data types, variables, strings, loops, functions, and software engineering concepts like multithreading and multitasking.

    • Introduction to Programming
    • What is Programming?
      Need to learn programming
      Front-end vs Backend developer

    • Understanding Different Programming Tools
    • Which Programming language High / low level programming Java, Python, Cpp, Ruby, C#, Machine Coding and JQuery PHP vs Ruby vs Python
    • HTML and CSS (web)
    • Sublime Introduction and Basics of HTML Introduction and Basics of CSS Basics of JavaScript
    • CPP
    • Introduction to C++ Installation and Basics of C++ Data Structures and Variables Operators in C++ Loop and If-Else Statements Pointers and its use
  • Module 2: Statistics for Data Science

    Statistics is the discipline of allocating a prospect through the classification, collection, and analysis of data. A substructure part of Data Science, this Course helps you in defining the statistical terms. The Course explains measures of central tendency and dispersion and comprehended skewness, correlation, regression, and distribution. It will enable you to make data-driven predictions through statistics and its essential applications of it.

    • Introduction to Statistics and Data Science
    • Introduction to Statistics Introduction to Data Science
    • Fundamentals of Descriptive Statistics
    • Measures of Central Tendency, Asymmetry, and Variability Practical Example: Descriptive Statistics Distributions
    • Advanced Studies
    • Estimators and Estimates Hypothesis Testing: Introduction Practical Examples for Hypothesis Testing
    • Regression Analysis
    • Fundamentals of Regression Analysis Assumptions of Regression Analysis Dealing with Categorical Data Practical Examples for Regression Analysis
  • Module 3: Data Science with R

    The next step to becoming a data scientist is learning R—the most in-demand open source technology. R is the most powerful Data Science and analytics language, which has a steep learning curve and vigorous community. Data Science with R is becoming the technology of choice for organizations that are adopting the power of analytics for competitive expedience.

    • Introduction to Business Analytics
    • Analytics Definition and Applications Data Science, Data Mining, Statistics Supervised vs Unsupervised Learning
    • Introduction to R Programming
    • About R: R Installation Data Import and Export Operators in R
    • Data Structures
    • Data Types Data Structures
    • Data Management in R
    • Apply Family in R Aggregate and Table Commands Data Manipulation in R Managing Missing values in R
    • Advanced Data Visualization
    • Introduction to basic graphs Introduction to GGPlot Library Plots: Scatter Plot, Histogram, Bar Plot, Box Plot, Heatmap, etc
    • Descriptive Statistics in R
    • Applying function of Statistics in R
    • Regression Analysis
    • Introduction to Regression analysis Building models for analysis Linear Regression Multi-Linear Regression Logistic Regression Assumptions of the model
    • Decision Tree: Classification
    • Introduction to Decision Tree Building models for analysis CART Approach
    • Clustering: K-means and Hierarchical
    • Introduction to Cluster Analysis Building models for analysis K-means Clustering Hierarchical Clustering
    • Association Rule Analysis
    • Introduction to Association Rule Analysis Understanding requirements for ARA: Support, Confidence, and Lift Building models for analysis
  • Module 4: Data Science with Python

    This Data Science with Python course will set up your mastery of Data Science and analytics techniques using Python. In this Python for Data Science course, you will learn the essential concepts of Python programming and gain in-depth knowledge in data analytics, Machine Learning, data visualization, web scraping, and natural language processing

    • Data Science Overview
    • Data Science, Data Mining, Statistics Supervised vs Unsupervised Learning
    • Data Analytics and Business Application
    • Analytics Definition and Applications Why Analytics and Roles (Application and Roles in various domains..) Tools and Techniques in Analytics
    • Python Environment Setup and Essentials
    • Anaconda - Download & Setup IDEs - Jupyter, Spyder, PyCharm Git - Setup and Configuration with IDEs Creating and Managing Analytics/ ML Projects
    • Mathematical Computing with Python
    • Understanding NumPy Library Managing and manipulating data
    • Scientific Computing with Python
    • Understanding SciPy Library Managing and manipulating data
    • Data Manipulation with Pandas
    • Group Summaries Crosstab, Pivot and Reshape data Managing Missing Values Outliers Detection Managing indexes in pandas
    • Data Visualization in Python using Matplotlib
    • Selection of Grap Libraries (matplotlib, seaborn, plotnine) Basic Graphs (histogram, barplot, boxplot, pie, etc) Managing plot parameters(size, title, axis, legend, etc) Advanced Graphs (correlation, heatmap, mosaic, etc) Exporting graphs
  • Module 5: Natural Language Processing

    This Natural Language Processing course will give you a comprehensive detail of the science behind applying Machine Learning algorithms to process large amounts of natural language data. Learn the concepts of statistical machine translation and neural models, deep semantic similarity model (DSSM), neural knowledge base embedding, deep reinforcement learning technique, neural models applied in image captioning, and visual question answering using Python’s Natural Language Toolkit (NLTK).

    • Introduction to NLP
    • Introduction to Natural Language Processing Components of NLP Applications of NLP Challenges and scope Data formats Text Processing Assisted Practice: Implement Text Processing Using Stemming and Regular Expression after Noise Removal and Convert It into List of Phrases Preprocessing in NLP-Tokenization, Lemmatization, Stemming, Normalisation, Stop Tweets Cleanup and Analysis Using Regular Expressions
    • Feature Engineering on Text Data
    • N-Gram Bag of Words Document Term Matrix TF-IDF Levenshtein Distance Word Embedding(Word2Vec) Doc2vec PCA Word Analogies Dense Encoding Topic Modelling Assisted Practice: Word2vec Model Creation Assisted Practice: Word Analogies Demo Assisted Practice: Identify Topics from News Items Build Your Own News Search Engine
    • Natural Language Understanding Techniques
    • Parts of Speech Tagging Dependency Parsing Constituency Parsing Morphological Parsing Named Entity Recognition Coreference Resolution Word Sense Disambiguation Fuzzy Search Document and Sentence Similarity Document Indexing Sentiment Analysis Assisted Practice: Analyzing the Disease and Instrument Name with the Action Performed Assisted Practice: Analyzing the Sentiments Assisted Practice: Extract City and Person Name from Text Identifying Top Product Feature from User Reviews
    • Natural Language Generation
    • Retrieval based model Generative based model AIML Language Modelling Sentence Correction Assisted Practice: Create AIML Patterns for QnA on Mental Wellness Assisted Practice: To Predict the Next Word in a Sentence Create your Own Spell Checker
    • NLP Libraries
    • Spacy NLTK Gensim TextBlob Stanford NLP LUIS Assisted Practice: Simplilearn Review Analysis Create your Own NLP Module
    • NLP with Machine Learning & Deep Learning
    • Neural Machine Translation Introduction to RNN, LSTM LSTM Forward Pass LSTM Backprop through time Applications of LSTM Advanced LSTM Structures Encoder Decoder Attention Text Classification and Summarization Document Clustering Attention Mechanism Question Answering Engine Assisted Practice: Target Spam Words and Patterns Assisted Practice: Summarization of News Document Clustering for BBC News
    • Speech Recognition Techniques
    • Basic concepts for voice/sound Sequential models Creating speech model Saving model Implementation/use cases Speech libraries Assisted Practice: Translation from Speech to Text Speech to Text: Extract Keywords from Audio Reviews
  • Module 6: Tableau

    This Tableau training will help you master the various aspects of the program and gain skills such as building visualization, organizing data, and designing dashboards. You will also learn concepts of statistics, mapping, and data connection. Tableau is an essential asset to those wishing to succeed in Data Science.

    • Introduction to Data Visualization and Power of Tableau
    • Comparison and benefits against reading raw numbers Real use cases from various business domains Some quick and powerful examples using Tableau without going into the technical details of Tableau Installing Tableau Tableau interface Connecting to Data Source Tableau data types Data preparation
    • Architecture of Tableau
    • Installation of Tableau Desktop Architecture of Tableau Interface of Tableau (Layout, Toolbars, Data Pane, Analytics Pane, etc.) How to start with Tableau The ways to share and export the work done in Tableau
    • Working with Metadata and Data Blending
    • Connection to Excel Cubes and PDFs Management of metadata and extracts Data preparation Joins (Left, Right, Inner, and Outer) and Union Dealing with NULL values, cross-database joining, data extraction, data blending, refresh extraction, incremental extraction, how to build extract, etc.
    • Creation of Sets
    • Mark, highlight, sort, group, and use sets (creating and editing sets, IN/OUT, sets in hierarchies) Constant sets Computed sets, bins, etc
    • Working with Filters
    • Filters (Addition and removal) Filtering continuous dates, dimensions, and measures Interactive Filters, marks card, and hierarchies How to create folders in Tableau Sorting in Tableau Types of sorting Filtering in Tableau Types of filters Filtering the order of operations
    • Organizing Data and Visual Analytics
    • Using Formatting Pane to work with menu, fonts, alignments, settings, and copy-paste Formatting data using labels and tooltips Edit axes and annotations K-means cluster analysis Trend and reference lines Visual analytics in Tableau Forecasting, confidence interval, reference lines, and bands
    • Working with Mapping Preview
    • Working on coordinate points Plotting longitude and latitude Editing unrecognized locations Customizing geocoding, polygon maps, WMS: web mapping services Working on the background image, including add image Plotting points on images and generating coordinates from them Map visualization, custom territories, map box, WMS map How to create map projects in Tableau Creating dual axes maps and editing locations
    • Working with Calculations and Expressions
    • Calculation syntax and functions in Tableau Various types of calculations, including Table, String, Date, Aggregate, Logic, and Number LOD expressions, including concept and syntax Aggregation and replication with LOD expressions Nested LOD expressions Levels of details: fixed level, lower level, and higher level Quick table calculations The creation of calculated fields Predefined calculations How to validate
    • Working with Parameters Preview
    • Creating parameters Parameters in calculations Using parameters with filters Column selection parameters Chart selection parameters How to use parameters in the filter session How to use parameters in calculated fields How to use parameters in the reference line
    • Charts and Graphs
    • Dual axes graphs Histograms Single and dual axes Box plot Charts: motion, Pareto, funnel, pie, bar, line, bubble, bullet, scatter, and waterfall charts Maps: tree and heat maps Market basket analysis (MBA) Using Show me
    • Dashboards and Stories
    • Building and formatting a dashboard using size, objects, views, filters, and legends Best practices for making creative as well as interactive dashboards using the actions Creating stories, including the intro of story points Creating as well as updating the story points Adding catchy visuals in stories Adding annotations with descriptions; dashboards and stories What is a dashboard? Highlight actions, URL actions, and filter actions Selecting and clearing values Best practices to create dashboards Dashboard examples; using Tableau workspace and Tableau interface Learning about Tableau joins Types of joins Tableau field types Saving as well as publishing data source Live vs extract connection Various file types
    • Tableau Prep
    • Introduction to Tableau Prep How Tableau Prep helps quickly combine join, shape, and clean data for analysis Creation of smart examples with Tableau Prep Getting deeper insights into the data with great visual experience Making data preparation simpler and accessible Integrating Tableau Prep with Tableau analytical workflow Understanding the seamless process from data preparation to analysis with Tableau Prep
    • Integration of Tableau with R & Hadoop
    • Introduction to R language Applications and use cases of R applications and use cases of R Learning R functions in Tableau The integration of Tableau with Hadoop
  • Module 7: Power BI

    The Business Intelligence course curriculum is composed of modules which impart the essential knowledge along with developing interest and curiosity. You will earn a Henry Harvin certificate when you have put your learnings to the test and come out a winner. Once you have completed all requirements about the Business Intelligence course, our trainers would be honored to hand over the Course completion certificate to you, in person!

    • Business Intelligence (BI) Concepts
    • Introduction to Business Intelligence The importance of Business Intelligence The relation between Business Intelligence and Data Warehouse Tools and Technologies in Business Intelligence area
    • Microsoft Power BI (MSPBI) Introduction
    • Power BI Architecture Power BI introduction and overview Introduction Power BI desktop and Power BI in Excel
    • Connecting Power BI with Different Data Sources
    • Connect to CSV files in Power BI Desktop Connect to Excel in Power BI Desktop Connect to text in Power BI Desktop Connect to SQL Server in Power BI Desktop Connect to a Web page from Power BI Desktop Connect to Direct SQL Query in Power BI Desktop
    • Power Query for Data Transformation
    • Power Query Introduction Query Editor Query Editor Interface Column Transformations Data Type Adding Column Text Transformations Number Column Calculations Date and Time Calculations M built-in functions
    • Data Modeling in Power BI
    • Introduction to DAX Calculated Columns Measures Calculated Tables Row Context vs Set Context Advanced calculations using Calculate functions Time Intelligence Functions
    • Reports in Power BI
    • Create a new Power BI report The report editor in Power BI Add a page to a Power BI report Add a filter to a report in Power BI Save a report in Power BI About filters and highlighting in Power BI reports How to use report filters Analyze in Excel Change how visuals interact in a report Open a Power BI report in Reading View Go from Reading View to Editing View in Power BI Interact with a report in Editing View in Power BI Aggregates (sum, average, maximum, etc.) in Power BI Rename a report in Power BI Page display settings in a Power BI report Duplicate a report page in Power BI Delete a page from a Power BI report Rename a report page
    • Reports & Visualization Types in Power BI
    • Types of visualization in a Power BI report Custom visualization to a Power BI report Download a custom visual from the gallery Getting started with color formatting and axis properties Change how a chart is sorted in a Power BI report Move, resize, and pop out a visualization in a Power BI report Drill down in a visualization in Power BI Histograms Basic Area chart Combo Chart in Power BI Customize visualization title, background, and legend Customize X-axis and Y-axis properties Doughnut charts in Power BI Enhanced Scatter charts in Power BI Funnel charts in Power BI KPI Visuals Radial Gauge charts in Power BI Scatter Charts in Power BI Slicers in Power BI Tree Maps in Power BI Waterfall charts in Power BI
    • Dashboards in Power BI
    • Create a Power BI dashboard Dashboard tiles in Power BI Pin a tile to a Power BI dashboard from a report Power BI publisher for Excel Pin an entire report page to a Power BI dashboard Data alerts in Power BI service Add an image, text box, video, or web code to your dashboard Edit a tile — resize, move, rename, pin, delete, add hyperlink Tips for designing a great Power BI dashboard Print a dashboard, print a dashboard tile, print a report page Display dashboards and reports in Full Screen mode (TV mode) Display a dashboard tile in Focus mode Featured dashboards in Power BI Create a phone view of a dashboard Add an image to a dashboard
    • Data Refresh in Power BI
    • Configure scheduled refresh Refresh a dataset created from a Power BI Desktop file – local Refresh a dataset created from a Power BI Desktop file – cloud Refresh a dataset created from an Excel workbook – local Disable privacy settings
    • Projects — End to End Data Modeling & Visualization
    • Project 1 Project 2
  • Module 8: SQL Developer

    SQL is the Leading Programming Language for relational databases. Manage and Code relational databases and database-driven applications. Create MySql database and database design. Master the Core Concepts. American National Standard Institute considers SQL as the most standard database

    • SQL Overview
    • Introduction to SQL Installation and Getting Started
    • SQL Manipulation
    • Relational Databases SQL Statements SQL commands
    • JOIN
    • Introduction to JOIN Inner JOIN Left JOIN Right JOIN Full Outer JOIN Cross JOIN
    • String Functions
    • Length Upper Lower Replace Trim, L Trim, R Trim Concatenation Substring List Aggregation
    • Mathematical Functions
    • Ceil Random Setseed Round Power
    • Data-Time Functions
    • Current Date and Time Age Extract
    • Tuning Tips
    • Soft Delete vs Hard Delete Update vs Case String Functions Joins Schema
  • Module 9: Simulated Data Science Projects

    • Retail
    • E-commerce
    • Web & Social Media
    • Banking
    • Supply Chain
    • Healthcare
    • Insurance
    • Entrepreneurship /Start-Ups
    • Finance & Accounts
  • Module 10: Projects — End to End Data Modelling & Visualization

    The candidate will be provided with two projects which will check the candidate’s understanding of the topics practically

    • Project 1
    • Project 2
  • Module 11: Projects Covered

    • HR: Analyze the Attrition rate of Employees
    • Sales: Predicting Department wise Sales
    • Multi-Domain: Business Analytics Optimization
    • Marketing: Website Trend Analysis
    • Financial Analysis: Stock Market Prediction
    • Finance: Analyze ETF Trends
  • Module 12: Electives 1: Artificial Intelligence

    • Neural Network
    • This module will equip the candidate with the knowledge of Nural Networks. Gain Comprehensive knowledge about the Activation Functions and feedforward neural network. Learn about backpropagation and gradient descent. Know about the full connected layer forward and backward pass. Get acquainted with Data Preprocessing, Data Augmentation, weight initialization, working with google collab, and more
    • Computer Vision
    • This module will help the candidate to gain knowledge of Computer Vision. Learn to work with images. Gain knowledge about Convolutions 2D for images. Know about the CNN architectures. Get acquainted with the knowledge of Semantic segmentation using UNet, and more
    • Natural Language Programming (NLP)
    • This module will equip the candidate with the knowledge of Natural Language Processing. Learn the Preprocessing in NLP-Tokenization, Lemmatization, Stemming, Normalisation, Stop words, BOW, TF-IDF. Know about Word embedding, POS Tagging, LSTM application, Encoder-Decoder attention, and more
  • Module 13: Electives 2: Machine Learning

    The Machine Learning course will make you a Master in Machine Learning, a form of Artificial Intelligence that automates data analysis to enable computers to learn and adapt through experience to do specific tasks without explicit programming. You will learn concepts and techniques, including supervised and unsupervised learning, mathematical and heuristic aspects, and hands-on modeling to develop algorithms and prepare you for your role with advanced Machine Learning knowledge.


    - Master the concepts of supervised and unsupervised learning, recommendation engine, and time series modeling

    - Acquire practical mastery over principles, algorithms, and applications of Machine Learning through a hands-on approach that includes working on four

    major end-to-end projects and 25+ hands-on exercises

    - Acquire thorough knowledge of the statistical and heuristic aspects of Machine Learning

    - Implement models such as support vector machines, kernel SVM, naive Bayes, decision tree classifier, random forest classifier, logistic regression, K-n

    means clustering, and more in Python

    - Validate Machine Learning models and decode various accuracy metrics. Improve the final models using another set of optimization algorithms, which

    include Boosting & Bagging techniques

    - Comprehend the theoretical concepts and how they relate to the practical aspects of Machine Learning

    - Gain expertise in Machine Learning using the Scikit-Learn package

    - Use the Scikit-Learn package for natural language processing

  • Module 14: Electives 3: Deep Learning with KERA & Tensorflow

    Keras is an API designed for human beings, not machines. Keras follows best practices for reducing cognitive load: it offers consistent & simple APIs, it minimizes the number of user actions required for common use cases, and it provides clear & actionable error messages. It also has extensive documentation and developer guides.

Know the complete offerings of our Post Graduate Program in Post Graduate Program in Data Science

Skills Covered

Descriptive Statistics

Model building and Fine Tuning

Hypothesis Testing

Exploratory Data Analysis

Supervised and Unsupervised Learning

Inferential Statistics

Deep Learning

Machine Learning

Neural Networking

Tools Covered


Know more about 50+ tools covered in this Data Science with Python

Our Projects


Face Recognition

Face recognition comes in the artificial intelligence category, but nowadays, many gadgets come with a face recognition feature. It is mainly useful for security purposes. Data science solves the data collection problem for face recognition.



Churn Rate Prediction

The churn rate is the annual total number of customers unsubscribing from a product or a service of a company. With the use of data science technology, a company can develop a system to know about customers' behavior. A company may use this data to make an ad for those leaving customers.



Short-Term Memory Forecast

It is an artificial neural network with memory sets at each nodal. It acts as a feedback system to forward feedback received earlier based on regular activities. A professional will have hands-on data science technology to develop short-term memory forecast applications.


Career Services By Henry Harvin®

Career Services

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Exclusive access to our dedicated job portal and apply for jobs. More than 2100+ hiring partners’ including top start–ups and product companies hiring our learners. Mentored support on job search and relevant jobs for your career growth.


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Get Ahead with Henry Harvin® Post Graduate Program in Data Science in Chandigarh Certification

  • Earn your Certificate

    Our Post Graduate Program in Data Science Course is the Proof of our Procurement in the Field Of Digital Content Writing and Content Strategy

  • Differentiate Yourself with Henry Harvin® Certification

    The Practical Knowledge Gained from the Industry Best Trainer will keep you a Step Ahead of Your Competition

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    Talk about it on Linkedin, Whatsapp, Instagram, Facebook, Twitter boost your resume or frame it - tell your friends and colleagues about it

  • Training Certification

    Get a prestigious Hallmark Certification in Post Graduate Program in Data Science (PGDS) and get 8 essential Certification with 3 Elective Certificates:

    1. Certified Programming for Non-programmers (CPN)

    2. Certified Data Scientist Statistics (CDSS)

    3. Certified Data Scientist-R (CDS-R)

    4. Certified Data Science with python (CDSP)

    5. Certified Tableau Training (CTT)

    6. Certified Natural Language Processing (CNLP)

    7. Certified Power BI (CPBI)

    8. Certified SQL Developer (CSQLD)


    1. Certified Artificial Intelligence Practitioner (CAIP)

    2. Certified Machine Learning Practitioner (CMLP)

    3. Deep Learning with KERA & TensorFlow Training Course 


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Post Graduate Program in Data Science in Chandigarh Training Certification Process

Counselling and Registration

Consult one of the Counselors and get into the Right Batch. Register yourself for the Post Graduate Program in Data Science

Attend the Training for Post Graduate Program In Data Science

Attend the Instructor-Led Sessions of the Post Graduate Program In Data Science. Go Through the Recorded Sessions, in case you missed any topic or training.

Submit Projects Assigned and Commence your 6 Months Internship

Submit the Hands-on Project and Capstone Projects assigned during the training for assessment and get into the Assured 6 Months Internship

Get Certified as a Post Graduate in Data Science

Post Completion of the training and Internship, get a Certified as Post Graduate in Data Science from Henry Harvin® Analytics Academy. Post it on Social Media and apply for Internship and Freelancing Projects

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There's a reason that 95% of our customers undertake 3+ courses as a minimum with Henry Harvin®

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