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Quick Facts

Medium Of InstructionsMode Of LearningMode Of Delivery
EnglishSelf StudyVideo and Text Based

Courses and Certificate Fees

Certificate AvailabilityCertificate Providing Authority
yesUMD

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

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