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PGP-Data Science & Analytics

PGP-Data Science & Analytics

Kickstart your career in Data Science | Learn In-demand Tools & Languages

Application closes 25th Nov 2024

  • Program Overview
  • Curriculum
  • Certificate
  • Tools
  • Success Stories
  • Faculty
  • Career Support
  • Fees

Key highlights of the Data science course

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    20 weekly online mentorship sessions

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    7 hands-on projects and 40+ case studies

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    Dedicated 1:1 Mentorship

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    Academic Learning Support

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    10 years of excellence

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    Dedicated Career Support

Skills you will learn

  • Python
  • Data Mining
  • Tableau
  • Machine Learning
  • SQL
  • ChatGPT

Leading global education platform

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Our alumni work at top companies

Curriculum

Course 01: Python for Data Science

This course teaches you how to read, explore, manipulate, and visualize data to tell stories, solve business problems, and deliver actionable insights and business recommendations by performing exploratory data analysis using some of the most widely used Python packages.

Python Programming

  • Variables and Datatypes
  • Data Structures
  • Conditional and Looping Statements
  • Functions

Python for Data Science

  • NumPy arrays and functions 
  • Accessing and modifying NumPy arrays 
  • Saving and loading NumPy arrays 
  • Pandas Series (Creating, Accessing, and Modifying Series) 
  • Pandas DataFrames (Creating, Accessing, Modifying, and Combining DataFrames) 
  • Pandas Functions 
  • Saving and loading datasets using Pandas

Exploratory Data Analysis

  • Data overview
  • Univariate analysis (Histogram, Boxplots, and Bar graphs)
  • Bivariate/Multivariate analysis (Line Plot, Scatterplot, Lmplot, Jointplot, Violin Plot, Striplot, Swarmplot, Catplot, Pairplot, Heatmap)
  • Customizing of Plots
  • Missing value treatment
  • Outlier detection and treatment

Course 02: Inferential Statistics

This course teaches you how to perform statistical analysis using Python to evaluate the reliability of a particular business estimate using confidence intervals and test hypotheses (assumptions) before putting them into action and committing resources by analyzing data distributions and performing hypothesis testing.

Inferential Statistics Foundations

  • Experiments, Events, and Definition of Probability
  • Introduction to Inferential Statistics
  • Introduction to Probability Distributions (Random Variable, Discrete and Continuous Random Variables, Probability Distributions)
  • Binomial Distribution
  • Normal Distribution

Estimation and Hypothesis Testing

  • Sampling
  • Central Limit Theorem
  • Estimation
  • Introduction to Hypothesis Testing
  • Hypothesis Formulation and Performing a Hypothesis Test
  • One-tailed and Two-tailed Tests
  • Confidence Intervals and Hypothesis Testing

Common Statistical Tests

  • Test for one mean
  • Test for equality of means
  • Chi-square Test of independence
  • One-way ANOVA

Course 03: Predictive Modeling

This course helps you explore linear models to capture the relationships between variables and a known outcome that is continuous, check the statistical validity of the models, and draw statistical inferences to gain business insights regarding key factors influencing decision-making.

Introduction to Supervised Learning - Linear Regression

  • Introduction to learning from data
  • Simple and Multiple Linear Regression
  • Evaluating a regression model
  • Pros and Cons of Linear Regression

Linear Regression Assumptions and Statistical Inference

  • Statistician vs ML Practitioner
  • Linear Regression Assumptions
  • Statistical Inferences from a Linear Regression Model

Course 04: Machine Learning - 1

In this course, you will explore classification models to capture the relationships between variables and a known categorical outcome and gain business insights by identifying the key factors influencing decision-making.

Logistic Regression

  • Introduction to Logistic Regression
  • Interpretation from a Logistic Regression model
  • Changing the threshold of a Logistic Regression model
  • Evaluation of a classification model, Pros and Cons

Decision Trees

  • Introduction to Decision Tree
  • Different impurity measures
  • Splitting criteria in a Decision Tree 
  • Methods of Pruning a Decision Tree
  • Regression Trees, Pros and Cons

Course 05: Machine Learning - 2

In this course, you will learn how to combine the decisions from multiple models using ensemble techniques to improve model performance and make better predictions, and employ feature engineering techniques and hyperparameter tuning to arrive at generalized, robust models to optimize associated business costs

Bagging and Random Forest

  • Introduction to Ensemble Techniques
  • Introduction to Bagging
  • Sampling with Replacement
  • Introduction to Random Forest

Boosting

  • Introduction to Boosting
  • Boosting Algorithms (Adaboost, Gradient Boost, XGBoost)
  • Stacking

Model Tuning

  • Feature Engineering
  • Cross-validation
  • Oversampling and Undersampling
  • Model Tuning and Performance
  • Hyperparameter Tuning
  • Grid Search
  • Random Search
  • Regularization

Course 06: Unsupervised Learning

In this course, you will learn to use clustering algorithms to group data points based on their similarity, find hidden patterns or intrinsic structures in the data, and understand the importance of and how to perform dimensionality reduction.

K-means Clustering

  • Introduction to Clustering
  • Types of Clustering
  • K-means Clustering
  • Importance of Scaling
  • Silhouette Score
  • Visual Analysis of Clustering

Hierarchical Clustering and PCA

  • Hierarchical Clustering
  • Cophenetic Correlation
  • Introduction to Dimensionality Reduction
  • Principal Component Analysis

Course 07: Introduction to Generative AI

In this course, you will get an overview of Generative AI, understand the difference between generative and discriminative AI, design, implement, and evaluate tailored prompts for specific tasks for achieving desired outcomes, and integrate open-source LLMs and prompt engineering to solve business problems using generative AI.

Introduction to Generative AI

  • Supervised vs Unsupervised Machine Learning
  • Generative AI vs Discriminative AI
  • Brief Timeline of Generative AI
  • Overview of Generative Models
  • Generative AI Business Applications

Introduction to Prompt Engineering

  • Introduction to Prompts
  • The Need for Prompt Engineering
  • Different Types of Prompts (Conditional, Few-shot, Chain-of-thought, Returning Structured Output)
  • Limitations of Prompt Engineering

Course 08: Introduction to SQL

In this course, you will gain an understanding of the core concepts of databases and SQL, gain practical experience writing simple SQL queries to filter, manipulate, and retrieve data from relational databases, and utilize complex SQL queries with joins, window functions, and subqueries for data extraction and manipulation to solve real-world data problems and extract actionable business insights.

Querying Data with SQL

  • Introduction to Databases and SQL
  • Fetching data
  • Filtering data
  • Aggregating data

Advanced Querying

  • In-built functions (Numeric, Datetime, Strings)
  • Joins
  • Window functions

Enhancing Query Proficiency

  • Subqueries
  • Order of query execution

Additional Modules: Learn at your own pace

Course 1: Introduction to Data Science

This course provides you with an understanding of the evolution of  Data Science over time, its application in industries, the mathematics and statistics behind them, and an overview of the life cycle of building data-driven solutions.
  • The Fascinating History of Data Science
  • Transforming Industries through Data Science
  • The Math and Stats underlying the technology
  • Navigating the Data Science Lifecycle


Course 2: Pre-Work

This course provides you with a fundamental understanding of the basics of Python programming and builds a strong foundation of the basics of coding to build Data Science applications

  • Introduction to Python Programming
  • Data Science Application Case Study

Course 3: Data Visualization using Tableau

In this course, you will learn how to read, explore, and effectively visualize data using Tableau and tell stories by analyzing data using Tableau dashboards

  • Storytelling with Data
  • Creating Interactive Dashboards

Course 4: Time Series Forecasting

In this course, you will learn how to describe components of a time series data and analyze them using special techniques and methods for time series forecasting.
  • Introduction to Time Series Analysis
  • Introduction to Forecasting
  • ARIMA & SARIMA


Course 5: Model Deployment

In this course, you will learn the role of model deployment in realizing the value of an ML model and how to build and deploy an application using Python.

  • Introduction to Model Deployment
  • Serialization and Containerization

Upskill from Great Lakes

Earn a PG certificate in Data Science & Analytics

Ranked among India's top 10 business schools, Great Lakes is highly regarded for its analytics programs. A certification from Great Lakes Executive Learning ensures industry credibility and acceptance, providing a robust foundation for your career advancement.

Great lakes certificate

* Image for illustration only. Certificate subject to change.

  • Top Standalone Institution

    Top Standalone Institution

    By Outlook India

  • In One Year Programs

    In One Year Programs

    By Business World

  • Top B-Schools

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    By Business India

Industry relevant syllabus

Learn top in-demand tools

Delve deep into Data Science with our program, mastering significant skills and employing powerful tools to fortify digital defenses.

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    Python

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    NumPy

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    Pandas

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    Seaborn

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    Matplotlib

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    Statsmodels

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    Scipy

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    Scikit-Learn

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    Hugging Face

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    ChatGPT

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    MySQL

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    Tableau

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    Docker

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    Flask

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    Gradio

Hands-On Case Studies & Projects

7 hands-on projects and 40+ case studies

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FoodHub Order Analysis

Industry - Food and Beverages
Perform an exploratory data analysis and provide actionable insights for a food aggregator company to get a fair idea about the demand of different restaurants and cuisines, which will help them enhance their customer experience and improve the business
Tools & Concepts - Python, Numpy, Pandas, Seaborn, Univariate Analysis, Bivariate Analysis, Exploratory Data Analysis, Business Recommendations
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E-news Express

Industry - News and Media
Explore the data provided and perform statistical analysis to decide whether the new landing page of an online news portal is effective enough to gather new subscribers as compared to the old one.
Tools & Concepts - Data Visualization, Exploratory Data Analysis, Hypothesis Testing, A/B Testing, Statistical Inference
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ReCell

Industry - Retail
Analyze the used devices dataset, build a model which will help develop a dynamic pricing strategy for used and refurbished devices, and identify factors that significantly influence the price.
Tools & Concepts - Exploratory Data Analysis, Data Preprocessing, Linear Regression, Linear Regression Assumptions, Multicollinearity, Statistical Inference
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INN Hotels

Industry - Hospitality
Analyze the data of INN Hotels to find which factors have a high influence on booking cancellations, build a predictive model that can predict which booking is going to be canceled in advance, and help in formulating profitable policies for cancellations and refunds.
Tools & Concepts - Exploratory Data Analysis, Data Preprocessing, Logistic regression, Multicollinearity, Decision trees, Pruning, Feature Importance
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EasyVisa

Industry - Immigration
Analyze the data of Visa applicants, build a predictive model to facilitate the process of visa approvals, and based on important factors that significantly influence the Visa status recommend a suitable profile for the applicants for whom the visa should be certified or denied.
Tools & Concepts - Exploratory Data Analysis, Data Preprocessing, Bagging, Random Forest, Boosting, AdaBoost, Gradient Boosting, XGBoost, GridSearchCV
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Trade&Ahead

Industry - Banking and Finance
Analyze the stock data, grouping the stocks based on the attributes provided, and sharing insights about the characteristics of each group.
Tools & Concepts - Exploratory Data Analysis, K-means Clustering, Elbow Method, Hierarchical Clustering, Principal Component Analysis, Cluster Profiling
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New Wheels Data Analysis

Industry - Retail
Analyze a vehicle resale company's listing and customer feedback data, answer business questions, and provide recommendations for the leadership to enable data-driven decision-making.
Tools & Concepts - Querying Data, SQL Functions, Data Aggregation, Joins, Subqueries, Window Functions
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Hotel Booking Cancellation Prediction

Industry - Hospitality
Build a Data Science solution for a chain of hotels that will help them predict the likelihood of a booking getting canceled so that they can take measures to fill in potential vacancies and reduce revenue loss
Tools & Concepts - Exploratory Data Analysis, Decision Trees, Random Forest, Scikit Learn, Pandas
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Restaurant Review Analysis

Industry - Food and Beverages
Analyze the customer reviews for different restaurants for a leading global food aggregator and use generative AI models to analyze the reviews and tag them, thereby enhancing the company's ability to understand customer sentiments at scale, enabling data-driven decision-making, and improving overall customer satisfaction.
Tools & Concepts - Generative AI, Large Language Models, Prompt Engineering, Hugging Face
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Machine Predictive Maintenance

Industry - Manufacturing
Analyze the data of an auto component manufacturing company and develop a predictive model to detect potential machine failures, determine the most influencing factors on machine health, and provide recommendations for cost optimization to the management
Tools & Concepts - Exploratory Data Analysis, Data Visualization, Decision Trees, Pruning, Scikit-Learn
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Rental Bike Count Prediction

Industry - Transportation
Analyze the customer data of a bike-sharing company and build a model to predict the count of bikes shared so that the company can make prior decisions for surge hours,
Tools & Concepts - Exploratory Data Analysis, Data Visualization, Decision Trees, AdaBoost, XGBoost, Scikit-Learn
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CredPay

Industry - BFSI
Analyze the data provided by a consultation firm that partners with banks, answer key questions provided, draw actionable insights, and help the company to improve the business by identifying the attributes of customers eligible for a credit card
Tools & Concepts - Exploratory Data Analysis, Data Visualization, Pandas, Seaborn
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Diabetes Risk Prediction

Industry - Healthcare
Analyze the historical patient data provided and build a predictive model to help identify whether a person is at risk of diabetes or not
Tools & Concepts - Exploratory Data Analysis, Data Visualization, Bagging, Random Forests, Scikit-Learn
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Music-Startup Data Analysis

Industry - Healthcare
Analyze the data from the database of a music-based startup that recently started selling music records, answer questions for a performance review to identify customer preferences by demographies, and generate recommendations to help business growth
Tools & Concepts - Data Filtering, SQL Functions, Data Aggregation, JoinsSQL
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Tourism Services Analysis

Industry - Tourism
Analyze the data comprising economic, social, and environmental & infrastructure indicators, and group countries based on them to help a tourism management organization identify key locations to invest to promote tourism services.
Tools & Concepts - Exploratory Data Analysis, Data Visualization, K-means Clustering, Hierarchical Clustering, Principal Component Analysis, Scikit-Learn
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Diet Plan Analysis

Industry - Health and Wellness
Analyze the data provided by a health company regarding a market test experiment to check the effectiveness of various diet plans for weight loss, and conduct hypothesis tests to find evidence of whether the different diet plans differ significantly
Tools & Concepts - Exploratory Data Analysis. Confidence Intervals, Hypothesis Testing, ANOVA, Statsmodels
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Online Course Provider Data Analysis

Industry - EdTech
Analyze the platform engagement data of a massive open online course provider and create an analytical report for a given academic year and enable informed decision-making regarding actions for the next academic year
Tools & Concepts - Exploratory Data Analysis, Data Visualization, Tableau

Watch stories of success of our learners

  • Aziz Elbahri - Learner

    Aziz Elbahri

    Post Graduate Program in Data Science

Our faculty

Meet our expert faculty - professionals who are passionate about deep Data Science knowledge

  • Dr. Abhinanda Sarkar - Faculty Director

    Dr. Abhinanda Sarkar

    Faculty Director, Great Learning

    Dr. Abhinanda Sarkar is the Academic Director at Great Learning for Data Science and Machine Learning Programs. Dr. Sarkar received his B.Stat. and M.Stat. degrees from the Indian Statistical Institute (ISI) and a Ph.D. in Statistics from Stanford University. He has taught applied mathematics at the Massachusetts Institute of Technology (MIT); been on the research staff at IBM; led Quality, Engineering Development, and Analytics functions at General Electric (GE); served as Associate Dean at the MYRA School of Business; and co-founded OmiX Labs. Dr. Sarkar has contributed to formulating the program curriculum and learning content.

    Read more

  • Dr. Jones  Mathew - Faculty Director

    Dr. Jones Mathew

    Principal and Head of Institution, Professor- Marketing, Great Lakes

    Dr. Mathew is a Ph.D. from Indian Institute of Foreign Trade (IIFT), MBA – Marketing from BIT-Mesra, Ranchi, BA Economics from Lucknow University and brings with him 18 years of industry experience followed by 7 years of B-School academic experience.

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  • Dr. P. K. Vishwanathan - Faculty Director

    Dr. P. K. Vishwanathan

    Professor of Analytics, Great Lakes Institute of Management

    Dr. P K Viswanathan, currently serves as a professor of analytics at Great Lakes Institute of Management. He teaches subjects such as business statistics, operations research, business analytics, predictive analytics, ML analytics, spreadsheet modeling and others. He has been teaching analytics for more than two decades but has been into active and intense teaching since analytics started witnessing a meteoric growth with the advent of R and Python. Ph.D. in the application of Operations Research from Madras University.

    Read more

  • Prof. Prashant Koparkar  - Faculty Director

    Prof. Prashant Koparkar

    Corporate Trainer and Consultant - Machine Learning

  • Dr. Srabashi Basu - Faculty Director

    Dr. Srabashi Basu

    Professor, PhD Statistics

    Srabashi is an analytics expert and an academic administrator. She does extensive work in statistical applications in different areas like health analytics, complex data analytics, aviation industry, CRM etc. She has published research papers in many areas of statistics and is always eager to apply her skills in challenging areas including finance, retail, marketing and other areas. She also works as a Corporate Trainer in Business Analytics, Predictive Modeling, R and SAS and leverages her work experience to prepare business case studies. She has developed online course materials in Statistics, Business Analytics, R and SAS. She is involved in professional training and corporate coaching for more than a decade now. She is adept in developing course for short-term, long-term and regular classes as well as online learning. She is also an efficient academic administrator. Specialties: Big data analytics, Statistics, Marketing Research, Market Survey, Business Intelligence, R, SAS, Minitab, Professional training, Academic administration.

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  • Prof. Snehamoy Mukherjee - Faculty Director

    Prof. Snehamoy Mukherjee

    Adjunct Faculty

    Snehamoy Mukherjee has 18+ years of experience and primarily in Analytics. He is part of the strategic leadership team at Axtria, a New Jersey based analytics firm, where he is responsible for business development, solution development, delivery leadership and strategy formulation. Prior to this, he used to head the analytics practice at Technopak Advisors. He has worked in multiple domains like retail consulting, FMCG/CPG, insurance and market research. He has a Bachelors and Masters in Mathematics and Scientific Computing from IIT Kanpur and a Minor in Engineering Management from the same institute.

    Read more

  • Dr. C P Gupta - Faculty Director

    Dr. C P Gupta

    Finance

  • Prof. Saurabh Aggarwal - Faculty Director

    Prof. Saurabh Aggarwal

    Professor, HBTI Kanpur

    Prof. Saurabh Agarwal is a professor at HBTI Kanpur and also a faculty at Great Learning. He has conducted workshops in premier institutes of India & govt departments on IBM SPSS Statistics, Amos, Modeler, Financial Modelling, Decision making using AHP, Advanced Excel, Simulation Techniques, Forecasting Analysis, etc. Prominent trainings include IIT Kanpur, Vinod Gupta School of Management IIT Kharagpur, IIIT Allahabad, Symbiosis University, Anna University, Ernst & Young, Airport Authority of India, Ministry of Science & Technology, New Delhi, Jamia Milia University, New Delhi, Jaipuria Institute of Management, ICSSR, New Delhi, Guwahati University, Aligarh Muslim University(AMU), etc.

    Read more

Industry experts

Introducing our dedicated mentors and experienced industry insiders devoted to guiding learners on their Data Science career journey.

  •  Satish Raghavendran  - Mentor

    Satish Raghavendran

    Vice President, Deloitte
  •  Manish Gupta  - Mentor

    Manish Gupta

    Senior Applied Scientist,Microsoft
  •  Sreevasan P S - Mentor

    Sreevasan P S

    Data Science Practitioner, AI/ML Mentor, Ex - Cognizant
  •  Balaji Sundararaman - Mentor

    Balaji Sundararaman

    Mentor - Data Science, ML, AI and Analytics at Great Learning
  •  Udayakumar Devaraj - Mentor

    Udayakumar Devaraj

    Senior Data Scientist, WNS

Advanced Career Support

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    1:1 CAREER SESSIONS

    Engage one-on-one with industry experts for valuable insights and guidance.

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    INTERVIEW PREPARATION

    Gain insights into Recruiter Expectations.

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    RESUME & LINKEDIN PROFILE REVIEW

    Showcase your Strengths Impressively

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    E-PORTFOLIO

    Create a Professional Portfolio Demonstrating Skills and Expertise

Program Fees

Program Fees: 1,800 USD

Flexible payment options available

Apply Now

Benefits of learning from us

  • 7 hands-on projects and 40+ case studies
  • Personalised mentorship sessions
  • Dedicated career support
  • 15+ Languages & Tools
  • Doubt-Solving with Expert Industry mentors
  • Proactive Program Support
  • Certificate of completion from Great Lakes

Application process

Our admissions close once the requisite number of participants enroll for the upcoming batch . Apply early to secure your seats.

  • steps icon

    1. Fill the application form

    Apply by filling a simple online application form.

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    2. Interview Process

    Go through a screening call with the Admission Director’s office.

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    3. Join program

    An offer letter will be rolled out to the select few candidates. Secure your seat by paying the admission fee.

Still have queries? Let’s Connect

Get in touch with our Program Advisors & get your queries clarified.

Speak with our expert +91 93193 68768 or email to pgpdsa@mygreatlearning.com

career guidance