• 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