PG Program in Data Science
& Machine Learning
Learn from IIBM Institute of Data Science that makes you job-ready and build a rewarding career
India’s top ranked program | 9 Months | Online Mentorship
Why we’re ranked #1
Comprehensive
Curriculum
- Designed for working professionals
- Modelling, Forecasting, Data Science, Analytics, Data visualisation and more
Personalised
Mentorship
- Designed for working professionals
- Modelling, Forecasting, Data Science, Analytics, Data visualisation and more
Dedicated Career
Assistance
- Job opportunities shared by 350+ companies
- 7000+ Alumni transitions with 48% avg salary hike
Curriculum
Module 1: Fundamentals of Data Analytics
2 Hours
Insurance, Automobile, Retail , Banking, Retial, Marketing, Aviation, Defence, Social Services, Computer Vision
Insights, Reports, Historical Performance, Trend, Visualization
Descriptive, Diagnostic, Predictive, Prescriptive, Exploratory
Difference between AI, Machine Learning, Deep Learning and Data Science
Module 2: Basics concepts in Statistics for Data Analytics
4 Hours
Undertand difference between Population vs Sample, importance of statistical concepts in data science and ML models
Know the foundation principal in statistics – Central Limit Theorem
Understand the importance of Mean, Medium, Mode of a variable
Understand the importance of Variance, Standard Deviation of a variable
Different scales of measuring data – Nominal, Ordinal, Interval, Ratio
Application of central tendencies for data analysis
Usage of correlation, regression concepts for data analysis
Module 3: Advanced concepts in Statistics for Data Analytics
6 Hours
Understand different types of data distribuitions – Uniform, Binomial, Poisson, Normal, Logarithmic, ExponentialHypoth
Learn to Perform Null Hypothesis and p-value to find the significant variables
Learn to perform t-test, z-test to measure the variance between the means of two samples or population
Learn techniques like ANOVA (1-way, 2-way, w/o replication), ANCOVA, f-test to compare the variance betweeen variables
Perform Chi-square test to evaluate distribution of sample same as expected population under study
Introduction to probability |
Types of events |
Marginal Probability |
Baye’s Theorem |
Module 4: Python essential for Data Science
15 Hours
What is Python? |
Why is Python essential for Data Science? |
Versions of Python |
How to install Python |
Anaconda Distribution |
How to use Jupyter Notebooks |
Command line basics |
GitHub overview |
How to execute Python scripts from command line |
Python Data Types |
Programming Concepts |
Python, Operators |
Conditional Statement, Loops |
Lists, Tuples, Dictionaries, Sets |
Methods and Functions |
Errors and Exception Handling |
Object Oriented Programming in Python |
Modules and Packages |
NumPy overview |
Arrays & Matrices |
NumPy basic operations, functions |
NumPy for Data Analysis |
Importing Pandas |
Pandas overview |
Pandas Series and Data Frames |
Dealing with missing data |
GroupBy, Merging, Concatenating and Joining |
Data Input & Output |
Why visualize data? |
Importing MatplotLib |
Chart: Line Chart, Bar Charts and Pie Charts |
Plotting from Pandas object |
Object Oriented Plotting: Setting axes limits and ticks |
Multiple Plots |
Plot Formatting: Custom Lines, Markers, Labels, Annotations, Colors |
Importing Seaborn |
Seaborn overview |
Distribution and Categorical Plotting |
Matrix plots & Grids |
Regression Plots |
Style & Color |
Review Session |
Module 5: Data Science with Python
11 Hours
Key Terms in Data Science |
Introduction to Supervised Learning,Unsupervised Learning |
What is Reinforcement Learning? |
Regression |
Classification |
Data Science Life Cycle |
Data Science in cloud |
Structured |
Unstructuted |
Cloud |
Univariate |
Bivariate |
Multivariate |
Missing Values |
Outliers treatment |
imbalance Data Handeling |
Standardization / Normalization |
Project1 |
| Data Science Library |
| Scikit learn |
Module 6: Supervised Learning - Regression and Classification Algorithms:
20 Hours
| Understanding Regression |
| Introduction to Linear Regression |
| Linear Regression with Multiple Variables |
| Disadvantage of Linear Models |
| Interpretation of Model Outputs |
| Assumption of Linear Regression |
| Project 2 (1 Hour) |
| Understanding classification |
| Introduction to Logistic Regression |
| Odds Ratio |
| Logit Function/ Sigmoid Function |
| Cost function for logistic regression |
| Application of logistic regression to multi-class classification |
| Assumption in Logistics Regression |
| Evaluation Matrix : Confusion Matrix, Odd’s Ratio And ROC Curve |
| Advantages And Disadvantages of Logistic Regression |
| Project 3 (1 Hour) |
| Understanding Decision Tree |
| Building Decision Tree |
| Using ID3 / Entropy |
| CART model – Gini index |
| Stopping Criteria And Pruning |
| Hyperparameter Tunning for Decision Tree |
| Overfitting Problem |
| Tradeoff between bias and variance |
| Ensamble methods |
| Bagging |
| Boosting |
| Random Forest |
| Grid Serach CV |
| Hyperparameter Tunning for Random forest |
| eature inmportance |
| Project 4 (1 Hour) |
| Understanding Decision Tree |
| Building Decision Tree |
| Using ID3 / Entropy |
| CART model – Gini index |
| Stopping Criteria And Pruning |
| Hyperparameter Tunning for Decision Tree |
| Project 4 (1 Hour) |
| Basics of SVM |
| Margin Maximization |
| Kernel Trick |
| RBF / Poly / Linear |
| Project 6 |
| Distance as Calssifier |
| Euclidean Distance |
| Manhattan Distance |
| KNN Basics |
| KNN for Regression & Classification |
| Project 7 (1 Hour) |
Module 7: Unsupervised Learning
9 Hours
| Clustering Methods |
| Agglomerative Clustering |
| Divisive Clustering |
| Dendogram |
| Project 8 (1 Hour) |
| Basics of KMeans |
| Finding value of optimal K |
| Elbow Method |
| Silhouette Method |
| Project 9 (1 Hour) |
| Eigenvalues and Eigenvectors |
| Orthogonal Transformation |
| Using PCA |
| Project 10 (1 Hour) |
Module 8: Machine Learning with Python
25 Hours
| Data Preparation |
| Stationary Data |
| Trends /Seasonility |
| ARIMA Model |
| SARIMA & Other Models |
| Project 11 (1 Hour) |
| Deep Learning and Neural Networks (2 Hours) |
| Deep Learning Fundamentals |
| Working of Neural Networks |
| Gradient Descent and Back Propagation |
| Activation Function |
| TensorFlow Introduction (1 Hours) |
Building Artificial Neural Networks (ANN) with Python |
Project 5 |
| Introduction to OpenCV |
| Learning Basic Image manipulations Introduction to CNN |
| CNN Institution |
| Convolution Operation Pooling, Flattening Building a CNN using Python |
| Project 6 |
| Introduction to RNN |
| Sequence prediction using RNN |
| Introduction to LSTM |
| Sequence prediction using LSTM |
| Applications in text analytics , stock prediction , time series data |
| Project 7 |
| Deploying an Scikit-learn model [train, deploy] |
| Deploying a Keras model [train, deploy] |
| Autoencoders and Generative Adversarial Networks |
Using Azure ML Studio
Module 9: Tableau
10 Hours
creating views, visual analysis, mapping , generate trends
Craeating Interactive Dashboards, Adding views and objects to dashboards
Project 13
Module 10: Introduction to Database
6 Hours
| What is Database |
| Introduction to MySQL and NoSQL |
| DDL v/s DML v/s DCL v/s TCL |
| Datatypes in SQL |
| Basic SQL statements (SELECT, DELETE and UPDATE) |
| How to convert data into tables |
| COMMIT and ROLLBACK statements |
Filter Data using the WHERE and ORDER BY Clause |
Using of Filtering Operators – IN, NOT IN, IS NULL, BETWEEN |
Introduction to Joins and its types
| Introduction to Grouping |
| Using GROUP BY & HAVING |
| Single-Row, Multiple-Row Subqueries |
| Subqueries with ANY and ALL Operators |
| Conditional Expressions using CASE Clause |
Module 11: Capstone Project
10 Hours
Languages and Tools covered
and more…
Capstone Projects
Faculty
Learn from leading academicians in the field of data science and business analytics and several experienced industry practitioners from top organizations.
Qualification: M.tech in Automotive Electronics, B.tech in Electronics and Communication
Qualifications: Ph.D (CSE), M.E (CSE), MCA
Qualification: Post Graduate Program in Big Data and Analytics, PG Diploma in Artificial Intelligence & Machine Learning.
MBA (Marketing & IT)
Program Fees
PG Program in Data Science & Machine Learning
Rs. 60,000 + GST
Post Graduate Certificate from
- Personalised weekly online mentorship sessions
- 9-Month Program
- 118+ hours of online learning content
- Dedicated career support through interview workshops and 1:1 mentorship
- Access to IIBM eInstitute – curated jobs portal and exclusive career fairs
- Individual doubt-solving with expert mentors
- 16 real-world projects guided by industry experts
- Access to IIBM Institute – Industry and Peer Networking Events
Candidates can pay the course fee through Netbanking, Credit/Debit cards, Cheque or DD. Also, with our corporate financial partnerships avail education loans at 0% interest rate*.
Application Process
Step 2 : Interview Process
Go through a screening call with the Admission Director’s office.
Step 3 : Join Program
Upcoming Application Deadline
Our admissions close once the requisite number of participants enroll for the upcoming batch . Apply early to secure your seats.