Postgraduate Program In Data Science And Analytics
Build Your Data Science and Analytics Career in Bangalore with Our 100% Job Assurance Program
For fresh graduates/Early career professionals with Tech background
Weekday
Classroom & Live Online Training
6 Months Program
300+ Learning hours
25+ Projects
10+ Tools
1500+
Students Placed
22.5LPA
Highest Salary
52%
Average Salary Hike
400+
Hiring Partners
1500+
Students Placed
22.5LPA
Highest Salary
52%
Average Salary Hike
400+
Hiring Partners
Our Alumni Work At
Join The Best Data Science And Analytics Course
Learn the real-world application of data science and build analytical models that enhance business outcomes. This 100% Job Assurance program is ideal for recent graduates and professionals who want to develop a successful data science and analytics career. You will gain practical knowledge about the implications of data science and analytics in real-world businesses and prepare to work as a data science professional in an emerging field of data science and analytics.
100% Job Assurance
Our data science course comes with a job assurance that offers you 10 guaranteed interviews at over 500 top-tier partner organisations hiring data science and analytics professionals.
Job-specific Curriculum
Learn the practical applications of data science, Python, SQL, data analytics, power BI, and tableau while gaining expertise in these subjects.
Live Learning Module
Our expert faculty delivers our robust curriculum using an interactive module and hands-on training methods to prepare you to work in various data science roles.
Real-world Projects
Implement what you’ve learned with over 25 real-world projects and case studies specially formulated by industry experts to make you job-ready.
Dedicated Career Services
Our career services include resume development, profile enhancement, career mentorship, job assurance workshops and one-on-one career counselling to ensure you land the right job.
Imarticus COE Organised Hackathons
Make your resume stand out by participating in coding hackathons to tackle complex business problems and get an opportunity to compete in a national-level competition.
Trusted By Millions Of Learners Around The World
Curriculum
Our leading edge curriculum covers fundamentals and more complex data science and analytics concepts.
Foundation
We’ve added a foundation module for those who have no programming knowledge.
The module covers the basics of programming for non-programmers. Learn programming concepts and implement those concepts to write efficient code. Get introduced to Lists and OOPS concepts.
What Will You Achieve?
Build a solid foundation for programming
Practice coding skills with 20+ coding questions
Topics
Introduction to Programming
Variables & Arithmetic Expressions
Functions
Data Types
Conditions and Conditional statements
Lists
OOPS
Core Track
Learn the principles of data science, machine learning and python programming in the core track.
The module covers the basics of Excel for data science. Learn Excel essentials to get strong hold on using Excel for data analysis. Get introduced to pivot tables, formulae and charts.
What Will You Achieve?
Build strong foundation of Excel for data analysis
Summarise data with pivot tables and charts
Topics
Intro to Excel
Importing data
Formatting in Excel
Excel Formulae
Data Validation
Calculations
Lookup and Reference
Pivot Tables
Charts
What-if Analysis
Intro to Macros
The module introduces learners to SQL programming. Learn how to query data from the databases and create datasets for data analysis.
What Will You Achieve?
Build a strong SQL foundation for data querying
Create datasets for data analysis
Topics
Introduction to SQL
DDL Statements
DML Statements
DQL Statements
Aggregate Functions
Date functions
Union, Union All & Intersect Operators
Joins
Views & Indexes
Sub-Queries
Exercise on SQL
The module introduces the learners to Python programming. Work with multiple Python data science libraries to execute essential tasks like mathematical calculations, data manipulation, etc. Learn data visualisation with Python.
What Will You Achieve?
Master Python programming
Run data analysis process using Python libraries
Create useful charts for data visualisation
Topics
Python Introduction
Variables
Functions
Python Operators
Python Flow Controls
Conditional Statements
Loops
Python Collection Objects
- Strings
- List
- Tuple
- Dictionary
List Comprehension
User-defined Functions
Function Arguments
Lambda Functions
Introduction to NumPy
NumPy Array
Creating NumPy Array
Array Attributes
Array Methods
Array Indexing
Slicing Arrays
Array Operation
Iteration through Arrays
Introduction to Pandas
Pandas Series
Creating Pandas Series
Accessing Series Elements
Filtering a Series
Arithmetic Operations
Series Ranking and Sorting
Checking Null Values
Concatenate a Series
Pandas Dataframe - Introduction
Dataframe Creation
Reading Data from Various Files
Understanding Data
Accessing Dataframe Elements using Indexing Dataframe Sorting
Ranking in Dataframe
Dataframe Concatenation
Dataframe Joins
Dataframe Merge
Reshaping Dataframe
Pivot Tables
Cross Tables
Dataframe Operations
Checking Duplicates
Dropping Rows and Columns
Replacing Values
Grouping Dataframe
Missing Value Analysis & Treatment
Visualisation using Matplotlib
Plot Styles & Settings
Line Plot
Multiline Plot
Matplotlib Subplots
Histogram
Boxplot
Pie Chart
Scatter Plot
Visualisation using Seaborn
Strip Plot
Distribution Plot
Joint Plot
Violin Plot
Swarm Plot
Pair Plot
Count Plot
Heatmap
Summary Statistics
Missing Value Treatment
Dataframe Analysis using Groupby
Advanced Data Explorations
The module introduces the learners to statistics. Review, analyse, and draw conclusions from data. Apply quantified mathematical models to appropriate variables for data analysis.
What Will You Achieve?
Build a strong foundation for Statistics
Analyse data and draw conclusions using statistics
Implement mathematical models for data analysis
Topics
Introduction to Statistics
Random Variables
Descriptive Statistics
Measure of Central Tendency
Measure of Dispersion
Skewness and Kurtosis
Covariance and Correlation
What is Probability?
Events and Types of Events
Sets in Probability
Probability Basics using Python
Conditional Probability
Expectation and Variance
Probability Distributions
Discrete Distributions
- Uniform
- Bernoulli
- Binomial
- Poisson
Continuous Distributions
- Uniform
- Normal
Probability Distributions using Python
Introduction to Hypothesis Testing
Terminologies used in Hypothesis Testing
Procedure for testing a Hypothesis
Test for Population Mean
Small Sample Tests
Large Sample Tests
Test for Normality
One-way ANOVA
Assumptions
ANOVA Hypothesis
Post Hoc Test
Chi-Square Test
Chi-Square Test Steps
Chi-Square Example
The module introduces the learners to machine learning and explains how different ML algorithms work. It also covers optimisation of models and model tuning. Get a complete understanding of how organisations are applying ML to solve their problems and grow the business.
What Will You Achieve?
Learn machine learning algorithms and their applications
Analyse data and make predictions
Solve real-world business problems using ML
Topics
Introduction to Machine Learning
Machine Learning Modelling Flow
Parametric and Non-parametric Algorithms
Types of Machine Learning
Introduction of Linear Regression
Types of Linear Regression
OLS Model
Math behind Linear Regression Decomposition Variability
Metrics to Evaluate Model
Feature Scaling
Feature Selection
Regularisation Techniques
Project - Property Price Prediction
Class Assessment on Linear Regression
Intro to Logistic Regression
Maximum Likelihood Estimation
Performance Metrics
Performance Measures
Bias-Variance Tradeoff
Overfitting and Underfitting Problems
Cross Validation
Project - Vaccine Usage Prediction
Home Assignment on Logistic Regression
Introduction to Decision Tree
Entropy
Information Gain
Greedy Algorithm
Decision Tree: Regression
Gini Index
Tuning of Decision Tree-Pruning
Project - Heart Disease Prediction
Introduction to Random Forests
Averaging
Bagging
Random Forest – Why & How?
Feature Importance
Advantages & Disadvantages
Project - Taxi Fare Prediction
Class Assessment on Classification
What is Clustering?
Prerequisites
Cluster Analysis
K-means
Implementation of K-means
Pros and Cons of K-means
Application of K-means
Project - E-commerce Customer Segmentation
Introduction to Hierarchical Clustering
Types of Hierarchical Clustering
Dendrogram
Pros and Cons of Hierarchical Clustering
Project - Travel Review Segmentation
Home Assignment on Clustering
Prerequisites
Introduction to PCA
Principal Component
Implementation of PCA
Case study
Applications of PCA
Project - Real Estate Data Analysis using PCA
Understand Time Series Data
Visualising Time Series Components
Exponential Smoothing
Holt's Model
Holt-Winter's Model
ARIMA
Project - Forecasting the Sales of a Furniture Store
Basics of Cloud
Machine Learning on Cloud
Deploying ML models on Cloud
The module covers data visualisation with two popular business intelligence tools - Tableau and Power BI. Create charts to articulate data and present insights to the business. Learn to showcase a data-based story to the stakeholders.
What Will You Achieve?
Master data visualisation with Tableau
Master data visualisation with Power BI
Create meaningful dashboards for the business
Topics
Introduction to Tableau
Data Connection
Tableau Interface and Basic Chart Types
Working with Metadata
Visual Analytics
Mapping
Calculations
Dashboard and Stories
Introduction
Interface
Data Connections
Data Transformation
Advance Data Transformation
Specialisation Track
At the end of the core module, we will evaluate your performance and assign a specialisation track with dedicated project mentors for optimal learning.
Sharpen ML skills through projects
Learn advanced ML skills
Get introduced to AI
Ensemble Techniques
What is Ensembling?
Bootstrap Method
Bagging
Boosting
XGBoost
AdaBoost
Some detailed project based on Classification
Association Rules - Apriori algo
Market Basket Analysis - Problem
Some detailed project based on RFM model
Some detailed project based on Regression
Neural Network
What is a Neural Network?
Parts of Neural Network
Input, Hidden and Output Node
What's happening inside Neural Network?
Forward and Backward Propagation
Cost Function & Types of Cost Function
Project - Digit Recognition using Neural Networks
Home Assignment on ANN
Convolutional Neural Network (CNN)
What is CNN?
CNN Architecture
Intro to OpenCV
What is Computer Vision?
Object Detection
Intro to Transfer Learning
Applications
Project - Image Classification using CNN
Home Assignment on CNN
What is NLP?
Typical NLP Tasks
Sentence Segmentation & Tokenisation
Stemming, Lemmatisation
Named Entity Recognition (NER)
Stop Words Removal (English)
Applications of NLP
Introduction to the NLTK Library
Processing Raw Text
Bag-of-Words (BoW), TF-IDF
Recurrent Neural Network
What is RNN?
RNN Architecture
Project - Textual Document Classification using RNN
Build a data science project on your own by applying the learning from the bootcamps. Learn planning the project and implementing it successfully. Present your project to a team of evaluators from the industry and get valuable feedback. Add the project to you GitHub project portfolio.
What Will You Achieve?
Learn to plan a data science project
Solve a real-world problem using data science
Gain confidence on your skills by presenting the project to industry experts
Topics
Combination of all skills learned throughout the course
Project Presentation Skills
Prepare for interview opportunities by polishing the key skills. Get support to create your digital profile and resume. Sharpen your interview skills through interview preparation workshops and expert-based mock interviews. Resolve your career-related queries through career mentorship session. At the end of the module, you will be ready for real interviews.
What Will You Achieve?
Build an exciting resume and digital profile for recruiters
Sharpen interview skills with mock interview sessions
Resolve any career related queries to have clarity and confidence
Topics
Resume-building
GitHub Project Portfolio
Interview Preparation
Mock Interviews
Career Mentorship
Hiring Partner's Quote
To appreciate your efforts for finding us the best of candidates for our Data Science team here in trinity mobility....see more
My sincere gratitude towards the service rendered as Learners primary skillset a...see more
The capacity to learn is a gift, The ability to learn is a skills,The willingness to learn is a choice! And that choice is given by Imarticus learning...see more
Tools & Technologies Covered
Analytics Centre of Excellence
Analytics Centre of Excellence
Get Ready with exclusive sessions, hackathon, project competition and webinar by Imarticus Learning
The high impact benefits that you get at the Centre of Excellence by Imarticus Learning:
Data Wars Hackathons
Data Dive Webinars
Data Verse Projects
Data Fluent Seminars
Data Decisions Roundtables
Projects That You will Work On
Get an opportunity to work on real-world projects, and case studies that will give you an overview of the data science and machine learning fields and provide you with the necessary technical skills. Some of the indicative projects are:
Projects That You Will Work On
Get an opportunity to work on real-world projects, and case studies that will give you an overview of the data science and machine learning fields and provide you with the necessary technical skills. Some of the indicative projects are:
Learning Journey
Will I Get Certified?
Upon successfully completing this program, you’ll earn a Postgraduate Program in Data Science and Analytics certificate. This certification will add considerable value to your professional credentials.
What Can I Become?
Data Scientist
Data Analyst
Business Analyst
Business Intelligence Specialist
Business Analytics Professional
Analytics Manager
Data Science Consultant
Machine Learning Engineer
Data Scientist : A data scientist gathers, analyses and interprets large amounts of data. They develop statistical and predictive models and require high-level strategic and analytical skills.
Grow With Imarticus Learning
We’re focused on giving you an integrated learning experience. With our one-of-its-kind career support services, we continue to support you as you take a step into your career with a renewed perspective. Get access to over 500+ placement partners and explore unlimited opportunities.
An In-Depth Look At The Data Science Job Landscape
Download Report
100% Job Assurance
Our career services will introduce you to existing data science and analytics opportunities and ensure that you land your dream job.
Profile Enhancement
We assist you in building a robust portfolio and resume to ensure that your profile always catches the eye of prospective employers.
Resume Building
Redesign your resume with professional help and highlight your strengths in the best possible way.
Interview Prep
We have analysed the most commonly asked interview questions and built a training module that confidently prepares you for job interviews.
Career Mentoring
Our dedicated program mentors are seasoned professionals that will assist you with the curriculum, ensure you are up-to-date with in-class happenings and motivate you to move ahead in your academic journey.
#10 Guaranteed Interviews
Imarticus Data Wars Hackathon
Participate in the national-level data science hackathon
Compete to solve a challenging data science problem
Sharpen your data science application skills
Enhance your CV by winning the hackathon
Top 3 Hackathon Winners
Imarticus Data Verse Project Competition
Participate in the national-level data science competition
Create a data science project and nominate it for competition
Showcase your data science skills for real-world applications
Enhance your CV by winning the project competition
Top 3 Project Competition Winners
Transform Your Career With Imarticus
Imarticus Learning has placed more than 56,000 students in various industries.
Build Your Success Story With Imarticus
Program Fee
â‚ą 1,98,000
(Inclusive of all taxes)
Instalments
EMI Options
Registration Fees
â‚ą 10,000
Base Fee
â‚ą 1,88,000
Instalment 1
â‚ą 10,000
Instalment 2
â‚ą 65,200
Instalment 3
â‚ą 56,400
Moments Of Honour
FAQs
- Grades should be at least 40%
- Pass all Imarticus mock interviews and the capstone project evaluation
- Have an average attendance of at least 80% for each module