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Quick Facts
| Medium Of Instructions | Mode Of Learning | Mode Of Delivery |
|---|---|---|
| English | Self Study | Video and Text Based |
Courses and Certificate Fees
| Certificate Availability | Certificate Providing Authority |
|---|---|
| yes | UMD |
The Syllabus
- Intro to Program
- Introduction to DS Landscape
- Python Programming Essentials I - Variables, Expressions, and Control Statements
- Python Programming Essentials II - Functions and Data Structures
- Python Libraries for Data Science - NumPy
- Python Libraries for Data Science - Pandas
- Python Assignment
- Data Analysis using SQL
- Practical Data Considerations: Data Cleaning and Preparation
- Course Project: Python
- Exploratory Data Analysis
- Visualization in Python
- Visualisation using Tableau
- Data Storytelling
- Visualisation and Storytelling Assignment
- Inferential Statistics
- Hypothesis Testing
- Designing Business Experiments
- Course Project: Statistics
- Introduction to Linear Regression in Explanatory & Inferencing Setting
- Linear Regression in a Predictive Setting
- Introduction to Classification: Logistic Regression
- Evaluation methods in Classification Models
- Model Selection & Practical Consideration around Modelling + KNN
- Decision Tree Models
- Assignment
- Unsupervised Learning: Clustering
- Capstone Project 1
- Introduction to Ensemble Models: Random Forest & Boosting
- Unsupervised Learning: Association Rules Mining (Market Basket Analysis)
- Introduction to Deep Learning
- Classification & Regression Models using Neural Networks
- Introduction to Convolutional Neural Networks
- Introduction to Natural Language Processing
- Framework to AI & Business Strategy
- Modelling on Text data
- Assignment
- Executing AI Strategy
- Advanced Python Programming
- Advanced SQL
- Introduction to Time Series
- Recommender Systems
- DL in Tf & Optimization (Optional)
- RNN
- Applications in CV
- Assignment
- Capstone Project 2
Instructors
Articles
6 Feb'24, 10:41 AM
6 Feb'24, 10:41 AM