• 400+ Hours of Learning
• 100+ Hours of Live Sessions
• Graded Assessments Every Module
• 1 Capstone Project from the Domain of Your Choice
• 20+ Case Studies and Assignments
• Professional Certificate Program from University of Maryland's Robert H. Smith School of Business
• 10-12 Hours/Week of Learning
• Get a Young Talent Scholarship of 50k or an assured job opportunity

Top Skills You Will Learn

Statistics, Predictive Analytics using Python, Machine Learning, Data Visualization, Predictive Modelling, Business Problem Solving etc.

Job Opportunities

• Data Analyst, Data Scientist, Financial Analyst, Management Analyst/Consultant, Market Researcher, Operations Research Analysts, Independent Consultant, Business Analyst, Predictive Modeling Analyst/Consultant, Business Intelligence and Analytics Manager, Strategy Consultant, Risk Analyst

Minimum Eligibility

The program design and curriculum assumes an undergraduate education has been earned by the learner. High school graduates/ Associate degree holders can pursue this program.

Who Is This Program For?

1. Software Engineers who wish to enter DS
2. Mid Level IT professionals
3. Non Technical Business Analysts
4. Marketing/ Healthcare/ Domain based professionals who wish to apply analytics
5. Early career technologists with understanding of math and basic logic for programming

Syllabus covered

Preparatory Content

1. Data Analysis in Excel
2. Math for DS

Data Science Toolkit

1. Intro to DS Landscape + Business Problem Solving
2. Python Programming Essentials I - Variables, Expressions, and Control Statements
3. Python Programming Essentials II - Functions and Data Structures
4. Python Libraries for Data Science - NumPy
5. Python Libraries for Data Science - Pandas
6. Python Assignment
7. Data Analysis using SQL
8. Practical Data Considerations: Data Cleaning and Preparation
9. Course Project

Statistical Analysis & Visualisation

1. Exploratory Data Analysis
2. Visualization in Python
3. Visualisation using Tableau
4. Data Storytelling
5. Visualisation and Storytelling Assignment
6. Inferential Statistics
7. Hypothesis Testing
8. Designing Business Experiments
9. Introduction to Linear Regression in explanatory & inferencing setting
10. Course Project: Statistics

Machine Learning

1. Linear Regression in a predictive setting
2. Introduction to Classification: Logistic Regression
3. Evaluation methods in Classification Models
4. Model Selection & Practical Consideration around Modelling + KNN
5. Decision Tree Models
6. Assignment
7. Unsupervised Learning: Clustering
8. Unsupervised Learning: Association Rules Mining (Market Basket Analysis)
9. Course Project: Machine Learning

Advanced Machine Learning

1. Introduction to Ensemble Models: Random Forest & Boosting
2. Introduction to Deep Learning
3. Classification & Regression Models using Neural Networks
4. Introduction to Convolutional Neural Networks
5. Introduction to Natural Language Processing
6. Modelling on Text data
7. Assignment
8. Framework to AI & Business Strategy
9. Executing AI Strategy
10. Capstone Project

• AICTE Approved MBA (Executive) Degree from NMIMS Global Access
• Awarded Category 1 Autonomy by UGC
• 200+ hours of live interaction with faculty
• 360-degree career support
• Student Support available all day for your convenience (24*7)
• Alumni status by NMIMS Global Access School for Continuing Education
• Business Simulations from Harvard Business Publishing
• 3-month industry-relevant capstone project
• One-on-one interaction with industry mentors
• The program is managed and delivered by NMIMS

Top Skills You Will Learn

Core Management Skills and application of Analytics in - Finance, Marketing, Economics, Human Resources, Operations and Supply Chain, Machine Learning, and Big Data Technologies

Job Opportunity

Management Consulting, Growth Manager, Business consultant, Leadership Roles in Business Analytics, Sales, HR, Marketing & Finance, Growth & Strategy roles

Minimum Eligibility

Bachelor’s degree (10+2+3) in any discipline from a recognised university with minimum 55% marks and 3+ years of work experience OR, Bachelor’s degree (10+2+3) in any discipline from a recognised university with minimum 55% marks and a postgraduation / professional degree with minimum 55% marks and 2+ years of work experience

Who Is This Programme For?

Working professionals from any domain: Marketing and Sales, HR, Finance, Operations, Software and IT. Anyone looking for an analytics-oriented leadership role.

Syllabus covered

Term 1 - Business Communication

1. Effective Communication
2. Building a Personal Brand

Term 1 - Managerial Economics

1. MicroEconomics
2. MacroEconomics
3. Game Theory & Strategy

Term 1 - Information Systems for Managers

1. Information Systems for Effective Decision Making
2. Excel for Managers

Term 1 - Marketing Management

1. Consumer Behaviour
2. Marketing Strategy
3. Marketing Channels

Term 1 - Basics of Python

1. Introduction to Python
2. Data and Control Structures

Term2 - Python for Business Analytics

1. Python Libraries
2. Python Masterclass with Rahim

Term 2 - Behavioural Sciences and Human Resource Management

1. Organizational Behaviour
2. HRM Strategy

Term 2 - Business Statistics

1. Fundamentals of Statistics
2. Inferential Statistics
3. Association & Causation
4. Statistics Masterclass with Mahesh Iyer

Term 2 - Financial Accounting for Managers

1. Accounting Principles & Statements
2. Statement & Ratio Analysis

Term 3 - Business Problem Solving , Tableau and Story Telling

1. Business Problem Solving
2. Visualisation Using Tableau
3. Visualisation using Power BI
4. Storytelling Masterclass with Rahim

Term 3 - Operations & Supply Chain Management

1. Supply Chain Management and Strategic Planning
2. Source-Make-Move

Term 3 - Corporate Finance

1. Basics of Corporate Finance
2. Advanced Project Evaluation

Term 3 - Exploratory Data Analytics, Big Data and SQL

1. Exploratory Data Analysis
2. SQL
3. Big Data

Term 4 - Strategic Business Management

1. Business Analysis
2. Strategy Formulation
3. Business Strategy Case Studies

Term 4 - Machine Learning - I

1. Fundamentals of Machine Learning
2. Supervised Learning: Regression
3. Supervised Learning: Classification

Term 4 - Model Selection and Machine Learning - II

1. Decision Trees
2. Model Selection and Optimisation
3. Ensembles

Term 4 - Advanced Machine Learning

1. Time Series Forecasting
2. Unsupervised Learning
3. Beyond Basic Machine Learning

Term 5 - Marketing, Web and Social Media Analytics

1. Consumer Behaviour Analytics
2. Product and Price Optimisation
3. Marketing Campaign Analytics
4. Case Studies

Term 5 - Fraud and Risk Analytics

1. Fraud Analytics
2. Credit Risk Analysis

Term 5 - Supply Chain And Logistics Analytics

1. Demand Forecasting
2. Inventory Optimisation
3. Network Design & Optimisation

Term 5 - HR Analytics

1. HR Analytics -I
2. HR Analytics -II

Term 6 - Capstone Project

1. Capstone Mini Project
2. Capstone Simulation

• Complimentary Python Programming Bootcamp
• Only online M.Sc in DS for working professionals
• 60+ Case Studies and Projects
• Timely Doubt Resolution
• IIIT Bangalore & LJMU Alumni Status
• No Cost EMI Option
• Career mentorship Sessions(1:1)
• Just-in-Time Interviews
• Personalised Industry Session
• Industry Readiness Assessments
• Career Essential Soft Skills Program
• 500+ Hours of Learning
• Fortnightly Group Mentorship with Industry Mentors
• Support available 7 days, 24x7
• Global access to job opportunities
• NASSCOM certificate
• AI Powered Profile Builder
• High Performance Coaching(1:1)
• Career Bootcamp

Top Skills You Will Learn

Statistics, Predictive Analytics using Python, Machine Learning, Data Visualization, Big Data Analytics, etc.

Job Opportunity

Data Analyst, Data Scientist, Product Analyst, Machine Learning Engineer, Business Analyst

Minimum Eligibility

Bachelor’s Degree with minimum 50% or equivalent passing marks. No coding experience required.

Who Is This Programme For?

Any specialization can be chosen regardless of background. The program caters to: Engineers, Marketing & Sales Professionals, Freshers, Data Professionals, Domain Experts, Software & IT Professionals

Syllabus covered

Pre-Program Preparatory Content

1. Data Analysis in Excel
2. Analytics Problem Solving

Data Toolkit

1. Introduction to Python
2. Programming in Python
3. Python for Data Science
4. Data Visualization in Python
5. Exploratory Data Analysis
6. Credit EDA Case Study
7. Inferential Statistics
8. Hypothesis Testing
9. Data Analysis using SQL
10. Advanced SQL & Best Practices
11. SQL Assignment: RSVP Movies

Machine Learning

1. Linear Regression
2. Linear Regression Assignment
3. Logistic Regression
4. Unsupervised Learning: Clustering
5. Business Problem Solving
6. Clustering Assignment (Optional)
7. Case Study: Lead Scoring

Specialisation- Deep Learning

1. Tree Models
2. Model Selection & General ML Techniques
3. Bagging and Boosting
4. Advanced Regression
5. Advanced Regression Assignment
6. Principal Component Analysis
7. Time Series Analysis
8. Telecom Churn Case Study
9. Introduction to Neural Networks
10. Convolutional Neural Networks - Introduction and Industry Applications
11. CNN Assignment
12. Recurrent Neural Networks
13. Gesture Recognition
14. Capstone Project

Specialisation- Natural Language Processing

1. Tree Models
2. Model Selection & General ML Techniques
3. Principal Component Analysis
4. Advanced Regression
5. Advanced Regression Assignment
6. Bagging and Boosting
7. Time Series Analysis
8. Telecom Churn Case Study
9. Lexical Processing
10. Syntactic Processing
11. Syntactic Processing -Assignment
12. Neural Nets for NLP
13. Semantic Processing
14. Case Study: Automatic Ticket Classification
15. Capstone Project

Specialisation- Business Analytics

1. Tree Models
2. Time Series Forecasting
3. Retail-Giant Sales Forecasting Assignment
4. Model Selection & General ML Techniques
5. Advanced Excel
6. Visualisation using Tableau
7. Telecom Churn Case Study
8. Structured Problem Solving using Frameworks
9. Structured Problem Solving Assignment
10. Operations Research
11. Data Storytelling
12. Business Case Study
13. Capstone Project

Specialisation- Business Intelligence/Data Analytics

1. Data Modelling
2. Advanced SQL and Best Practices
3. SQL Weeklong Lab
4. Advanced Excel
5. NoSQL Databases and MongoDB
6. Introduction to Big Data and Cloud
7. Hive and Querying
8. Hive Case Study
9. Visualisation using Tableau
10. Sports Analytics - IPL Visualisation Assignment
11. Visualisation using PowerBI
12. Visualisation using Plotly
13. Data Storytelling
14. Business Case Study
15. Capstone Project

Specialisation- Data Engineering

1. Introduction to Big Data (Optional)
2. Introduction to Cloud and AWS Setup
3. Introduction to Hadoop and MapReduce Programming
4. Assignment (optional)
5. Data Management and Relational Database Modelling
6. NoSQL Databases and Apache HBase and NoSQL Databases and MongoDB(Optional)
7. Data Warehousing (Optional)
8. Data Ingestion with Apache Sqoop and Apache Flume
9. Hive & Querying
10. Assignment (optional)
11. Amazon Redshift
12. Introduction to Apache Spark
13. Project: ETL Data Pipeline
14. AWS Cloud Infrastructure (Optional)
15. Optimising Spark for Large Scale Data Processing
16. Apache Flink (Optional)
17. Real-Time Data Streaming with Apache Kafka
18. Real-Time Data Processing using Spark Streaming
19. Assignment (Optional)
20. Building Automated Data Pipelines with Airflow
21. Analytics using PySpark
22. Project: Real Time data processing
23. Capstone Project

Specialisation- Data Science Generalist

1. Tree Models
2. Boosting (optional)
3. Model Selection & General ML Techniques
4. Telecom Churn Case Study (optional)
5. Principal Component Analysis
6. ML Lab 1: Classification
7. Advanced Regression + ML Lab 2: Regression
8. Text Analytics & Processing + Text-Based Predictive Modelling
9. Visualisation using Tableau
10. Data Storytelling
11. Business Case Study
12. Data Modelling
13. Advanced SQL - Week II
14. SQL Weeklong Lab
15. Data Structures - Sets, Dictionaries, Stacks, Queues
16. Algorithm Analysis + Recursion
17. Searching and Sorting
18. Python Weeklong Lab-I
19. Python Weeklong Lab-II
20. Capstone Project

Research Methodologies

1. Introduction to Research and Research Process
2. Research Design
3. Literature Reviewing
4. Research Project Management
5. Report Writing and Presentation Skills
6. Scientific Ethics

Dissertation

1. Investigate dietary patterns and metabolite fingerprints of takeaway (fast) food consumers using PCA and clustering methods
2. Investigate a diagnosis of eye diseases using imaging ophthalmic data
3. Structure medical images with information geometry
4. Using social media feed to place tweets regarding natural disasters on a map
5. Preventing credit card fraud through pattern recognition
6. Developing a recommender system for a Media giant
7. Risk modelling for Financial activities and Investment Banking

• Designed for Working Professionals
• No Cost EMI Options Available
• Fullstack Academy Live Training
• AI Powered Profile Builder
• Student Support available all day for your convenience (24*7)
• Interview Preparation
• Just-in-Time Interviews
• 100+ Hours of Learning
• Certificate Programme from Caltech
• Caltech Approved Faculty Masterclasses
• Exclusive Job opportunities Portal
• Career Mentorship Sessions (1:1)
• High Performance Coaching (1:1)
• Caltech approved Faculty Masterclasses

Top Skills You Will Learn

Python, Linux, Cryptography, Threat Modelling, Splunk, Kali Linux, OWASP Top 10, Penetration Testing, NIST Cybersecurity Framework.

Job Opportunities

Cybersecurity Analyst, Cybersecurity Manager.

Minimum Eligibility

English language skills, Computer skills. Graduation with 50% marks (preferred). Undergrad/Freshers from STEM disciplines can also apply. Programming experience is not mandatory.

Who is this Program For?

Project Leads and Managers in IT/Tech Companies; Non-Tech professionals & Tech Support professionals; Freshers and young IT professionals.

Syllabus covered

Information Security Essentials

1. Cyber Foundations
2. Python Programming - I
3. Python Programming - II
4. Python Assessment
5. Introduction to cybersecurity
6. Linux and CLI - Part I
7. Linux and CLI - Part II

Red Team - Network and Application Security

1. Web and Networking
2. Data Capture with Wireshark
3. HTTP
4. Cryptography and Encoding
5. Assessment on Security Essentials
6. Passive and Active Recon
7. Shells, Payloads, and File Transfers
8. Injection Attacks
9. Password Attacks and Privilege Escalations
10. Attacking with Metasploit
11. Red Team Project

Blue Team

1. Fundamentals of Blue Team & Threat Modeling
2. Asset Mapping
3. Vulnerability Scanning
4. Firewalls
5. Intrusion Detection and Prevention Systems
6. Defense In Depth
7. Intro to Logs and Log Hunting
8. Midterm Review
9. SIEM - Splunk
10. Incident Response and Techniques
11. Digital Forensics and Incident Recovery
12. Boss the SOC and Rhino Hunt
13. Blue Team : Final Achievement

Advanced Optional Content

1. Data and Database Security
2. IAM - AWS
3. Compliance and Risk Management