This module covers the basic requirements to understand concepts in data science and to do practical data science. Essential mathematics and statistics are covered in detail, forming a good foundation for all concepts in subsequent modules.

• 1

  Basic Programming using Python & R

• 2

  Data Handling using Python & R

• 3

  Querying Database using SQL (& NoSQL)

• 4

  Visualization using Python & R

• 5

  Essential Mathematics and Statistics

• 6

  Data Analytics Workflow

• 7

  Bonus: Further Topics and References for Self-study

Where do you begin with a new dataset in hand? Here, you will learn how to conduct such an exploratory analysis. You will familiarize yourself with different techniques for data preprocessing, feature engineering, and advanced visualizations. The next part of the module covers various AI models (machine learning, deep learning, generative AI) for performing supervised tasks (predictions) and unsupervised tasks (finding hidden patterns in datasets). Recent years have seen a rise in the volume and velocity of data. Essential concepts and tools involved in handling AI models for massive datasets are also covered.

• 1

  Exploratory Data Analytics

• 2

  Basic AI Models: Machine Learning (Unsupervised and Supervised Learning)

• 3

  Advanced AI Models Part 1: Deep Learning

• 4

  Advanced AI Models Part 2: Generative AI

• 5

  Automation of AI Modelling Workflow

• 6

  Bonus: Scaling AI Models to handle Big Data using Apache Spark

Different types of data, like time series, text, etc., require additional preprocessing steps and dedicated methods to perform various tasks (e.g., classification, forecasting, etc.). This module includes hands-on practice with such requirements. Applications are also included; examples include forecasting of stock price using time series analysis, understanding supply chain disruptions via network analysis, sentiment analysis using NLP, etc.

• 1

  Natural Language Processing (Text Data)

• 2

  Time Series Analysis and Forecasting

• 3

  Network Analysis and Applications

• 4

  Applications: Social Media, Fraud Detection, Recommender Systems, Web Analytics, etc.

• 5

  Bonus: Computer Vision (Image Data)

Many organizations nowadays prefer to see results over the web. For example, the government wishes to have a real-time dashboard through which it can share actionable information with the public. This module covers hands-on practice with building such web apps for information provision and decision support. Doing data science on the cloud will also be discussed along with hands-on practice.

• 1

  Introduction to Web

• 2

  Big Data Management

• 3

  Web Dashboards (Interactive Graphs and Tables)

• 4

  Basic ML & AI-based Web Apps

• 5

  Deploying Apps on Web and Automation

• 6

  Bonus: Cloud Analytics (AWS/GCP/Azure)

All modules covered till now include hands-on practice with datasets. This module goes further to discuss and practice with real-world contexts. The module covers applications of concepts covered in previous modules to real-world business contexts (operational and strategic) and the usage of data science and artificial intelligence in different real-world industry use cases. Privacy, ethics, and regulatory concerns in different industries and business contexts are also covered, which can share actionable information with the public. This module covers hands-on practice with building such web apps for information provision and decision support. Doing data science on the cloud will also be discussed along with hands-on practice.

• 1

  Operational and Strategic Business Contexts

• 2

  Industry Cases (Finance, Health, Energy, etc.)

• 3

  Statistics and Machine Learning Use Cases

• 4

  Generative AI Use Cases

• 5

  Exclusive Case Studies using Real-world Datasets

• 6

  Bonus: Case Selection based on Learners' Interest