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What is the detailed curriculum of this programme? -

September 2, 2021
Module 0
  1. Google Colab
  2. Git
  3. Python
    • Matplotlib
    • Numpy
    • Pandas
  4. Basic mathematics
  5. Basic data visualization
  6. Website building
  7. App sandbox
  8. Introduce the data stories module
Module 1: Computational Data Science in Practice
  • Introduction
    1. What is data science?
    2. What is computational data science?
    3. Examples from different fields
  • Computational Science:
    1. Computation for Data Science
      • Behind the scenes of data science python modules, e.g., how does numpy broadcast and do computations fast
      • Parallel programming ideas behind data science routines
    2. GPU Revolution
      • GPU computing for Machine Learning, Data Science and Artificial Intelligence
    3. Hardware Evaluation for Data Science
      • Performance vs cost
      • Enterprise decision on cloud vs servers
  • A view of the Practice
  • Business Analytics
Module 2: Mathematics of Data Science
  • Probability and Statistics, Bayesian
  • Computational Linear Algebra
  • Computational Methods in Data Science
  • Optimization: The backbone of ML
Mini-Project 1 Python assignments through this term
Module 3: Data Engineering
  • Introduction to Big Data storage systems
    1. Relational Databases
    2. NoSQL Databases: HBase, Graph DB
    3. Distributed File Systems/HDFS
    4. Cloud storage
  • Introduction to Big Data processing platforms
    1. Data Volume: Hadoop, Spark
    2. Data Velocity: Storm, Complex Event Processing
    3. Cloud platforms
  • Deep dive into Apache Spark
    1. Resilient Distributed Dataset
    2. Transformations, Actions
    3. Designing computational and analytics application using Spark
    4. Hands on with PySpark programming
Mini-Project 2 Project exercise in pyspark
Module 4
  • Machine Learning
    1. Classification and Regression
      • Regression with regularization
      • Logistic Regression
      • Support Vector Machines
    2. Random Trees and Ensemble Methods
      • Classification trees
      • Ensemble methods
    3. Dimensionality Reduction
    4. Reinforcement Learning
      • MDP
      • Q-learning
Module 5 Neural Networks

  1. Multi Layer Perceptrons
  2. Convolutional Neural Networks
  3. Recurrent Neural Network
  4. Auto Encoder
  5. Generative Adversarial Network
  6. Graph Neural Networks
  7. Applications
Mini-Project 3 Project exercise in ML and NN
Module 6 Business Analytics

  1. Time Series Analysis
  2. Spreadsheet Exercises
  3. Data and Models for Econometric Analysis
  4. How analytics can transform business?
  5. How to explain statistics as a consultant to clients?
Mini-Project 4 Business Analytics Project/Hands-On
Additional Activities Case Studies
Data Stories