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PG Program in Data Science and Engineering
Start a successful career in Data Science
  • 10 guaranteed interviews
  • 5 months program
  • Immersive in-person classroom program
  • No technical expertise required
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Course Details
225+ Hour
Immersive classroom learning
5+
Languages & Tools
5 Months
Duration
Class Timings
Monday to Thursday at 9:30 AM at Great Learning Centers
Batch Start Dates

Pune

22nd Jan, 2023

Hyderabad

22nd Jan, 2023

Bangalore

22nd Jan, 2023

Mumbai

22nd Jan, 2023

Gurgaon

22nd Jan, 2023

Chennai

22nd Jan, 2023
What will you get?
Guaranteed interviews from 10 companies
Dedicated Superset Placement Drives
Free Mock interviews with industry experts
Live Career Mentorship sessions
Personalized Resume building and LinkedIn Review
PG Certification from the Best Analytics School in the country
What sets us apart?
Average CTC
7 LPA
Maximum CTC
20 LPA
Students placed so far
1750+
Early Bird Special Price
Recover your entire investment in 6 months
Starting from ₹ 17,700/month
More than 50% students pay using the monthly payment plan
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Where are your fellow learners now?
  • Mahendra Shaji
    Analytics Consultant, Fractal Analytics
  • Salil Trehan
    Research Executive, Nielsen
  • Runav Desai
    Executive Data Scientist, NielsenIQ
  • Shawrya Sharma
    Advanced Insight Analyst, Expedia Group
  • Ankit Arora
    Associate Consultant (Data Scientist), Capgemini
  • SAI Ramya Machavarapu
    Business Analyst, Mercedes Benz
  • Ritesh Choudhury
    Application Development Analyst, Accenture
  • Suraj Suresh
    Data Scientist, DXC Technology
Course Curriculum
The curriculum has been designed by faculty from Great Lakes and highly skilled experts of data science.
225+ Hour
Immersive classroom learning
5+
Languages & Tools
Foundations
The Foundations block comprises four courses where we get our hands dirty with the introduction to Python, Exploratory Data Analysis, Statistics, SQL Programming, and some domain-specific knowledge head-on. These courses set our foundations so that we sail through the rest of the journey with minimal hindrance.

    This module will let us get comfortable with the Python programming language used for Data Science. We start with a high-level idea of Object-Oriented Programming and later learn the essential vocabulary(/keywords), grammar(/syntax) and sentence formation(/usable code) of this language.
  • Syntax and Semantics of Python programming
    Python is a widely used high-level programming language and has a simple, easy-to-learn syntax that highlights readability. This module will help you drive through all the fundamentals of programming in Python, such as syntax and semantics, and at the end, you will execute your first Python program.
  • Conditional statements
    Conditional Statements perform various operations based on a boolean condition that evaluates to true or false. In this module, you will learn to implement if-else statements in Python.
  • Loops
    A loop is used for executing one or more statements several times mentioned. It will repeat the sequence of information until it meets a specified condition. This module will teach you how to implement for and while loops.
  • Iterators
    Iterators are objects containing values, where you can traverse through them. This module will teach you how to implement Iterator methods like __iter__() and __next__().
  • User-defined functions
    User-defined functions are functions created by the programmer to perform any application-specific operations. In this module, you will implement user-defined functions in Python using the def keyword.
  • NumPy
    This module will give you a deep understanding of exploring data sets using NumPy. NumPy is one of the most widely used Python libraries. NumPy is a package for scientific computing like working with arrays.
  • Pandas
    Pandas is also one of the most widely used Python libraries. Pandas is used to analyse and manipulate data. This module will give you a deep understanding of exploring data sets using Pandas.

    This module of PG in Data Science courses will teach you all about Exploratory Data Analysis like Pandas, Seaborn, Matplotlib, and Summary Statistics.
  • Pandas
    Pandas is one of the most widely used Python libraries. Pandas is used to analyse and manipulate data. This module will give you a deep understanding of exploring data sets using Pandas.
  • Summary statistics (mean, median, mode, variance, standard deviation)
    In this module, you will learn about various statistical formulas and implement them using Python.
  • Seaborn
    Seaborn is also one of the most widely used Python libraries. Seaborn is a Matplotlib based data visualisation library in Python. This module will give you a deep understanding of exploring data sets using Seaborn.
  • matplotlib
    Matplotlib is another widely used Python libraries. Matplotlib is a library to create statically animated, interactive visualisations. This module will give you a deep understanding of exploring data sets using Matplotlib.

    In the next module, you will learn everything you need to know about all the statistical methods used for decision making in this Data Science PG course.
  • Probability distribution
    A statistical function reporting all the probable values that a random variable takes within a specific range is known as a Probability Distribution. This module will teach you about Probability Distributions and various types like Binomial, Poisson, and Normal Distribution in Python.
  • Normal distribution
    Normal Distribution is the most critical Probability Distribution in Statistics, which describes the distribution of values of a variable.
  • Poisson's distribution
    Poisson's Distribution is a Probability Distribution in Statistics, which determines an event’s occurrence within a specified time interval.
  • Bayes’ theorem
    Baye’s Theorem is a mathematical formula named after Thomas Bayes, which determines conditional probability. Conditional Probability is the probability of an outcome occurring predicated on the previously occurred outcome.
  • Central limit theorem
    This module will teach you how to estimate a normal distribution using the Central Limit Theorem (CLT).
  • Hypothesis testing
    This module will teach you about Hypothesis Testing in Statistics. Hypothesis Testing is a necessary procedure in Applied Statistics for doing experiments based on the observed/surveyed data.
  • One Sample T-Test
    One-Sample T-Test is a Hypothesis testing method used in Statistics. In this module, you will learn to check whether an unknown population mean is different from a specific value using the One-Sample T-Test procedure.
  • Anova and Chi-Square
    Analysis of Variance, also known as ANOVA, is a statistical technique used in Data Science, which is used to split observed variance data into various components for additional analysis and tests.
    Chi-Square is a Hypothesis testing method used in Statistics, which is used to measure how a model compares to actual observed data.
    This module will teach you how to identify the significant differences between the means of two or more groups.

    Here, we will cover everything you need to know about SQL programming, such as DBMS, Normalization, Joins, etc.
  • Introduction to DBMS
    Database Management Systems (DBMS) is a software tool where you can store, edit, and organise data in your database.
    This module will teach you everything you need to know about DBMS.
  • ER diagram
    An Entity-Relationship (ER) diagram is a blueprint that portrays the relationship among entities and their attributes.
    This module will teach you how to make an ER diagram using several entities and their attributes.
  • Schema design
    Schema design is a schema diagram that specifies the name of record type, data type, and other constraints like primary key, foreign key, etc. It is a logical view of the entire database.
  • Key constraints and basics of normalization
    Key Constraints are used for uniquely identifying an entity within its entity set, in which you have a primary key, foreign key, etc. Normalization is one of the essential concepts in DBMS, which is used for organising data to avoid data redundancy.
    In this module, you will learn how and where to use all key constraints and normalization basics.
  • Joins
    As the name implies, a join is an operation that combines or joins data or rows from other tables based on the common fields amongst them.
    In this module, you will go through the types of joins and learn how to combine data.
  • Subqueries involving joins and aggregations
    This module will teach you how to work with subqueries/commands that involve joins and aggregations.
  • Sorting
    As the name suggests, Sorting is a technique to arrange the records in a specific order for a clear understanding of reported data. This module will teach you how to sort data in any hierarchy like ascending or descending, etc.
  • Independent subqueries
    The inner query that is independent of the outer query is known as an independent subquery. This module will teach you how to work with independent subqueries.
  • Correlated subqueries
    The inner query that is dependent on the outer query is known as a correlated subquery. This module will teach you how to work with correlated subqueries.
  • Analytic functions
    A function that determines values in a group of rows and generates a single result for every row is known as an Analytic Function.
  • Set operations
    The operation that combines two or more queries into a single result is called a Set Operation. In this module, you will implement various set operators like UNION, INTERSECT, etc.
  • Grouping and filtering
    Grouping is a feature in SQL that arranges the same values into groups using some functions like SUM, AVG, etc.
    Filtering is a powerful SQL technique, which is used for filtering or specifying a subset of data that matches specific criteria.
Machine Learning Techniques
The next module is Machine Learning that will teach us all the Machine Learning techniques from scratch, and the popularly used Classical ML algorithms that fall in each of the categories.

    This module will get us comfortable with all the techniques used in Linear and Logistic Regression.
  • Multiple linear regression
    Multiple Linear Regression is a supervised machine learning algorithm involving multiple data variables for analysis. It is used for predicting one dependent variable using various independent variables.
    This module will drive you through all the concepts of Multiple Linear Regression used in Machine Learning.
  • Fitted regression lines
    A fitted regression line is a mathematical regression equation on a graph for your data. This model can be used to identify the relationship between a predictor variable (x-scale) and a response variable (y-scale) so that it can assess whether the model fits your data.
  • AIC, BIC, Model Fitting, Training and Test Data
    In this module, you will go through everything you need to know about several models such as AIC, BIC, Model Fitting, Training, and Test Data.
  • Introduction to Logistic regression, interpretation, odds ratio
    Logistic Regression is one of the most popular ML algorithms, like Linear Regression. It is a simple classification algorithm to predict the categorical dependent variables with the assistance of independent variables.
    This module will drive you through all the Logistic Regression concepts used in Machine Learning, interpret Machine Learning models, and find the odds ratio relationship.
  • Misclassification, Probability, AUC, R-Square
    This module will teach everyone how to work with Misclassification, Probability, AUC, and R-Square.

    In the next module, you will learn all the Supervised Learning techniques used in Machine Learning.
  • CART
    CART, also known as Classification And Regression Tree, is a predictive machine learning model that describes the prediction of outcome variable's values predicated on other values. You will learn about the usage of this predictive model in this module.
  • KNN (classifier, distance metrics, KNN regression)
    KNN or k-Nearest Neighbours algorithm is one of the most straightforward machine learning algorithms for solving regression and classification problems.
    You will learn about using this algorithm like classification, distance metrics, and KNN regression through this module.
  • Decision Trees (hyper parameter, depth, number of leaves)
    Decision Tree is a Supervised Machine Learning algorithm used for both classification and regression problems. It is a hierarchical structure where internal nodes indicate the dataset features, branches represent the decision rules, and each leaf node indicates the result.
    You will learn about hyperparameter, depth, and the number of leaves in this module.
  • Naive Bayes
    Naive Bayes Algorithm is used to solve classification problems using Baye’s Theorem. This module will teach you about the theorem and solving the problems using it.

    In the next module, you will learn all the Unsupervised Learning techniques used in Machine Learning.
  • Clustering - K-Means & Hierarchical
    Clustering - K-Means & Hierarchical Clustering is an unsupervised learning technique involving the grouping of data. In this module, you will learn everything you need to know about the method and its types, like K-means clustering and hierarchical clustering.
    K-means clustering is a popular unsupervised learning algorithm to resolve the clustering problems in Machine Learning or Data Science.
    Hierarchical Clustering is an ML technique or algorithm to build a hierarchy or tree-like structure of clusters. For example, it is used to combine a list of unlabeled datasets into a cluster in the hierarchical structure.
  • Distance methods - Euclidean, Manhattan, Cosine, Mahalanobis
    This module will teach you how to work with all the distance methods or measures such as Euclidean, Manhattan, Cosine, and Mahalanobis.
  • Features of a Cluster - Labels, Centroids, Inertia
    This module will drive you through all the features of a Cluster like Labels, Centroids, and Inertia.
  • Eigen vectors and Eigen values
    In this module, you will learn how to implement Eigenvectors and Eigenvalues in a matrix.
  • Principal component analysis
    Principal Component Analysis is a technique to reduce the complexity of a model, like eliminating the number of input variables for a predictive model to avoid overfitting.

    In this Machine Learning, we discuss supervised standalone models’ shortcomings and learn a few techniques, such as Ensemble techniques, to overcome these shortcomings.
  • Bagging & Boosting
    Bagging, also known as Bootstrap Aggregation, is a meta-algorithm in machine learning used for enhancing the stability and accuracy of machine learning algorithms, which are used in statistical classification and regression.
    As the name suggests, Boosting is a meta-algorithm in machine learning that converts robust classifiers from several weak classifiers. Boosting can be further classified as Gradient boosting and ADA boosting or Adaptive boosting.
  • Random Forest
    Random Forest is a popular supervised learning algorithm in machine learning. As the name indicates, it comprises several decision trees on the provided dataset’s several subsets. Then, it calculates the average for enhancing the dataset’s predictive accuracy.
  • AdaBoost & Gradient boosting
    Boosting can be further classified as Gradient boosting and ADA boosting or Adaptive boosting. This module will teach you about Gradient boosting and ADA boosting.
  • Hackathon
    Hackathon is an event usually hosted by a tech organisation, where computer programmers gather for a short period to collaborate on a software project.
Applications*
This module concentrates on the applications of Data Science.

    This block will teach you all the techniques involved in Time Series.
  • Trend and seasonality
     Trend is a systematic linear or non-linear component in Time Series metrics, which changes over a while and does not repeat.
    Seasonality is a systematic linear or non-linear component in Time Series metrics, which changes over a while and repeats.
  • Decomposition
     This module will teach you how to decompose the time series data into Trend and Seasonality.
  • Smoothing (moving average)
     This module will teach you how to use this method for univariate data.
  • SES, Holt & Holt-Winter Model
     SES, Holt, and Holt-Winter Models are various Smoothing models, and you will learn everything you need to know about these models in this module.
  • AR, Lag Series, ACF, PACF
     In this module, you will learn about AR, Lag Series, ACF, and PACF models used in Time Series.
  • ADF, Random walk and Auto Arima
     In this module, you will learn about ADF, Random walk, and Auto Arima techniques used in Time Series.

    In this module, you will learn how to convert unstructured text into a structured text to discover relevant insights. This method is known as Text Data Mining.
  • Text cleaning, regular expressions, Stemming, Lemmatization
    Text Cleaning is a necessary procedure to emphasize the attributes for your machine learning model to choose. Regular Expression is a language that states text search strings. Stemming is a technique used in Natural Language Processing (NLP), which plucks out the base form of words by the removal of affixes from the words. Lemmatization is another commonly used NLP technique, which combines the different inflected word forms to be analysed as a single item.
  • Word cloud, Principal Component Analysis, Bigrams & Trigrams
    A word cloud is a data visualization technique, which is used to represent text data. This module will make you learn everything about Word cloud, Principal Component Analysis, Bigrams, and Trigrams used in Data Visualization.
  • Web scrapping, Text summarization, Lex Rank algorithm
    Web Scraping is the process of extracting data from the web. This module will teach you how to collect and parse data using Web Scraping and learn how to implement Text Summarization and Lex Rank algorithm.
  • Latent Dirichlet Allocation (LDA) Technique
    The word ‘Latent’ indicates that the model discovers the ‘yet-to-be-found’ or hidden topics from the documents. ‘Dirichlet’ indicates LDA’s assumption that the distribution of topics in a document and the distribution of words in topics are both Dirichlet distributions. ‘Allocation’ indicates the distribution of topics in the document.
  • Word2vec Architecture (Skip Grams vs CBOW)
    Word2vec is a method to create word embeddings by using a two-layer neural network efficiently. It was developed by Tomas Mikolov et al. at Google in 2013 to make the neural-network-based training of the embedding more efficient and since then has become the de facto standard for developing pre-trained word embedding.
    In this module, you will learn about the differences between Skip Grams and CBOW (Continuous Bag of Words).
  • Text classification, Document vectors, Text classification using Doc2vec
    In this module, you will learn even more about Text Classification and Document Vectors using Doc2vec.

    The last topic in this PG in Data Science program is Data Visualization. The process of the graphical representation of data and information is known as Data Visualization. This module will teach you how to use data visualization tools for providing data trends and patterns.
  • Building interactive dashboards using Tableau
    In this module, you will learn how to create an interactive Tableau dashboard and charts to organise data.
  • Data Visualization using Tableau
    Tableau is the most widely used data visualization tool to solve problems. This module will teach you everything you need to know about Data Visualization using Tableau.
*Online Instruction
Capstone Project
You will get your hands dirty with real-time projects under industry experts’ guidance, from Data Science, using Python to Machine Learning, SQL and Tableau. Successful completion of the project will earn you a post-graduate certification in Data Science and Engineering course.
Career Preparation: Aptitude Skill Training and Development, Resume Review Workshops, Interview Preparation
This post-graduate certification program in Data Science and Engineering will guide you through your career path with Aptitude Skill Training and Development. The program will also guide you in building your professional resume, attending mock interviews to boost your confidence and nurture you to nail your professional interviews.
PG Certificate from Great Lakes Executive Learning
Earn a Postgraduate Certificate in the top-rated Data Science and Engineering course from Great Lakes Executive Learning.
Its exhaustive Curriculum will foster you into a highly-skilled professional and help you land a job at the world’s leading corporations.
Exclusive Campus Hiring Drive
If you are looking for a Data Science course with placement opportunities, this is the right Data Science and Engineering course to excel in your career. This course offers you exclusive campus hiring opportunities with three months of placement assistance after the program completion.
Languages and Tools covered
and more...
Hands on Projects
1000+
Projects Completed
15+
Domains
Healthcare
Predictive model for Diabetes Treatments
Retail
Retail Sales prediction
Realty
Realty Predictive modeling on House Value
BFSI
Prediction of Loan interest rates
And More...
Our Faculty
Learn from leading academicians in the field of Data Science and Engineering and top industry practitioners.
  • Dr. Abhinanda Sarkar
    Phd in Statistics (Stanford University)
    Faculty Director, Great Learning
  • Dr. Srabashi Basu
    Phd (Penn State University, USA)
    Professor, Business Analytics
  • Dr. Mudit Kulshreshtha
    Phd (IGIDR, Advanced Research Institute of Reserve Bank of India)
    Professor, Great Lakes
  • Dr. D Narayana
    Phd (Pierre & Marie Curie University, Paris)
    Professor, Artificial Intelligence and Machine Learning, Great Learning
  • Mr. Rajesh Jakhotia
    MP (IIM, Calcutta)
    Founding Member, K2 Analytics Finishing School
  • Prof. Mukesh Rao
    PGDBA (SIMS)
    Director, Great Learning
  • Mr. Deepesh Singh
    Executive Programme (IIM Lucknow)
    Senior Data Scientist, ABInBev
  • Mr. Gurumoorthy Pattabiraman
    Msc (Madras School of Economics)
    Faculty, Data Science & ML, Great Learning
  • Mr. Kathirmani Sukumar
    B.E (Rajalakshmi Engineering college)
    Foundr, Quelit
  • Mr. Pushkar Shah
    MS (The University of Edinburg)
    Teaching Faculty, Great Lakes
  • Prof Saurabh Aggarwal
    M Tech (IIT, Kanpur)
    Professor, HBTI Kanpur
  • Mr. R Vivekanand
    MBA (Monash University Melbourne Vic.)
    FOperations Director, Wilson Consulting Private Limited
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  • Lesser intake - Limited no. of students in 1 classroom (as per guidelines)
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  • Option to move to Live Virtual Classes in case of insecurity anytime during the program
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