In this week, learners will build a foundation in core Data Science and Generative AI concepts, develop Python skills for data manipulation and analysis, and explore practical applications of Generative AI. The week concludes with a hands-on case study applying Data Science to solve a real-world business problem.

• Introduction to Data Science
• Introduction to Generative AI
• Python Programming Essentials

This course teaches you how to read, explore, manipulate, and visualize data to tell stories, solve business problems, and deliver actionable insights and business recommendations by performing exploratory data analysis using some of the most widely used Python packages.

Python Fundamentals for Working with Data

• Variables and Data Types
• Data Structures
• Conditional and Looping Statements
• Functions

Data Manipulation Using NumPy and Pandas

• NumPy Arrays and Functions
• Accessing and Modifying NumPy Arrays
• Saving and Loading NumPy Arrays
• Pandas Series (Creating, Accessing, and Modifying Series)
• Pandas Dataframes (Creating, Accessing, Modifying, and Combining Dataframes)
• Pandas Functions
• Saving and Loading Datasets Using Pandas

Exploratory Data Analysis for Extracting Insights

• Data Overview
• Univariate Analysis (Histogram, Boxplots, and Bar Graphs)
• Bivariate/Multivariate Analysis (Line Plot, Scatterplot, Lmplot, Jointplot, Violin Plot, Striplot, Swarmplot, Catplot, Pairplot, Heatmap)
• Customizing Plots
• Missing Value Treatment
• Outlier Detection and Treatment

In this course, you will build practical expertise in Generative Al by mastering Prompt Engineering and Large Language Model workflows. The course covers prompt design, text classification, summarization, and the application of LLMS to solve real-world business problems.

Introduction to Prompt Engineering

• Introduction to Prompts
• The Need for Prompt Engineering
• Different Types of Prompts (Conditional, Few-Shot, Chain-of-Thought, Returning Structured Output)
• Limitations of Prompt Engineering

Text Analysis with LLMs

• Introduction to Text-to-Label Generation
• Data Preparation Process
• Introduction to Text-to-Text Generation
• Structure of Text Generation Tasks

This course teaches you how to perform statistical analysis using Python to evaluate the reliability of a particular business estimate using confidence intervals and test hypotheses (assumptions) before putting them into action and committing resources by analyzing data distributions and performing hypothesis testing.

Inferential Statistics Foundations

• Experiments, Events, and Definition of Probability
• Introduction to Inferential Statistics
• Introduction to Probability Distributions (Random Variable, Discrete and Continuous Random Variables, Probability Distributions)
• Binomial Distribution
• Normal Distribution

Data Sampling and Inference for Accurate Insights

• Sampling
• Central Limit Theorem
• Estimation
• Introduction to Hypothesis Testing
• Hypothesis Formulation and Performing a Hypothesis Test
• One-tailed and Two-tailed Tests
• Confidence Intervals and Hypothesis Testing

Common Statistical Tests for Informed Decisions

• Test for One Mean
• Test for Equality of Means
• Chi-square Test of Independence
• One-way ANOVA

This course helps you explore linear models to capture the relationships between variables and a known continuous outcome, check the statistical validity of the models, and draw statistical inferences to gain business insights into key factors that influence business decision-making.

Introduction to Modeling Linear Regression

• Introduction to Learning from Data
• Simple and Multiple Linear Regression
• Evaluating a Regression Model
• Pros and Cons of Linear Regression

Statistical Inferences from Linear Regression

• Statistician vs ML Practitioner
• Linear Regression Assumptions
• Statistical Inferences from a Linear Regression Model

In this course, you will explore classification models to capture the relationships between variables and a known categorical outcome, and gain business insights by identifying the key factors that influence decision-making.

Logistic Regression for Probability-Based Insights

• Introduction to Logistic Regression
• Interpretation from a Logistic Regression Model
• Changing the Threshold of a Logistic Regression Model
• Evaluation of a Classification Model, Pros and Cons

Decision Tree for Transparent Decision Making

• Introduction to Decision Tree
• Different Impurity Measures
• Splitting Criteria in a Decision Tree
• Methods of Pruning a Decision Tree
• Regression Trees, Pros and Cons

In this course, you will learn how to combine the decisions from multiple models using ensemble techniques to improve model performance and make better predictions, and employ feature engineering techniques and hyperparameter tuning to arrive at generalized, robust models to optimize associated business costs.

Bagging Ensemble for Improved Predictive Performance

• Introduction to Ensemble Techniques
• Introduction to Bagging, Sampling with Replacement
• Introduction to Random Forest

Boosting Ensemble for Improved Predictive Performance

• Introduction to Boosting
• Boosting Algorithms (Adaboost, Gradient Boost, XGBoost)
• Stacking

Tuning and Validation Techniques for Optimized Model Performance

• Feature Engineering
• Cross-validation
• Oversampling and Undersampling
• Model Tuning and Performance
• Hyperparameter Tuning
• Grid Search
• Random Search
• Regularization

In this course, you will learn to use clustering algorithms to group data points based on their similarity, find hidden patterns or intrinsic structures in the data, and understand the importance of dimensionality reduction and how to perform it.

Insightful Data Segmentation with K-Means Clustering

• Introduction to Clustering
• Types of Clustering
• K-means Clustering
• Importance of Scaling
• Silhouette Score
• Visual Analysis of Clustering

Discovering Patterns with Hierarchical Clustering and PCA

• Hierarchical Clustering
• Cophenetic Correlation
• Introduction to Dimensionality Reduction
• Principal Component Analysis

This course will help you gain an understanding of the core concepts of databases and SQL, gain practical experience writing simple SQL queries to filter, manipulate, and retrieve data from relational databases, and utilize complex SQL queries with joins, window functions, and subqueries for data extraction and manipulation to solve real-world data problems and extract actionable business insights.

Data Retrieval and Aggregation Essentials

• Introduction to Databases and SQL
• Fetching Data
• Filtering Data
• Aggregating Data

Querying Techniques for Relational Data Analysis

• In-built Functions (Numeric, Datetime, Strings)
• Joins
• Window Functions

Advanced Querying for Enhanced Proficiency and Insights

• Subqueries
• Order of Query Execution

In this course, you will learn how to read, explore, and effectively visualize data using Tableau and tell stories by analyzing data using Tableau dashboards.

Storytelling with Data

• Dimensions Measures
• Data Types
• Calculations and Filtering
• Different Visualizations

Creating Interactive Dashboards

• Parameters
• Level of Detail Calculation
• Sets and Blends 
• Creating Interactive Dashboards
• Storyboarding

NEURAL NETWORKS AND COMPUTER VISION

This course helps you implement neural networks to synthesize knowledge from data, introduce you to the world of computer vision, demonstrate an understanding of image processing and different methods to extract informative features from images, build convolutional neural networks (CNNs) to unearth hidden patterns in image data, and leverage common CNN architectures to solve image classification problems.

Introduction to Neural Networks

• Deep Learning and History
• Multi-layer Perceptron
• Types of Activation Functions
• Training a Neural Network
• Backpropagation

Image Processing

• Overview of Computer Vision 
• Color Pixel and Image Representation
• Edge Detection
• Kernels
• Padding
• Strides, and Pooling
• Flattening to a 1D Array

Convolutional Neural Networks

• ANN Vs CNN
• CNN Architecture
• Introduction to Transfer Learning
• Common CNN Architectures

NATURAL LANGUAGE PROCESSING WITH GENERATIVE AI

This course will introduce you to the world of Natural Language Processing (NLP), help you gain a practical understanding of text embedding methods and the workings of various transformer architectures that form the foundation of Large Language Models (LLMs). You will explore how Retrieval-Augmented Generation (RAG) integrates information retrieval to enhance the accuracy and relevance of LLM responses, and learn to design and implement robust NLP solutions using open-source LLMs combined with Prompt Engineering techniques.

Introduction to Natural Language Processing

• Overview of NLP
• History of NLP
• Different Applications of NLP
• Text Cleaning
• Text Vectorization
• Sentiment Analysis

Word Embeddings

• Introduction to word embeddings
• Word2Vec
• CBOW and Skip-gram
• Negative Sampling
• Glove
• Applications of Word Embeddings

Attention Mechanism and Transformers

• Introduction to Transformers
• Components of a Transformer
• Different Transformer Architectures
• Applications of Transformers

Large Language Models and Retrieval Augmented Generation

• Introduction to LLMs
• Working of LLMs
• Applications of LLMs
• Introduction to Embeddings and Tokenization
• Byte-Pair Encoding (BPE) Tokenization
• Computation and Application of Sentence Embeddings
• Retrieval-Augmented Generation (RAG)

MARKETING AND RETAIL ANALYTICS

This course will help you understand the role of predictive modeling in influencing customer behavior and how businesses leverage analytics in marketing and retail applications to make data-driven decisions.

• RFM Analysis
• Cluster Analysis
• Market Basket Analysis
• Customer Lifetime Value (CLV) Model

FINANCIAL AND RISK ANALYTICS

This course will help you develop a deep appreciation of credit and market risk and understand how banks and other financial institutions use predictive analytics for modeling their risk.

• Credit Risk Modelling
• PD Model - Altzman's and Discriminant Function
• Market Risk Optimization

WEB AND SOCIAL MEDIA ANALYTICS

This course will help you understand and appreciate the most widely used tools of web analytics which form the basis for rational and sound online business decisions, and learn how to analyze social media data, including posts, content, and marketing campaigns, to create effective online marketing strategies.

• Google Trends and Analysis
• Google Ads
• Google Analytics
• Social Media Analytics
• Text Mining and Sentiment Analysis

SUPPLY CHAIN AND LOGISTICS ANALYTICS

This course will expose you to the discipline of supply chain management and its stakeholders, understand the role of logistics in businesses and supply chains, and learn methods of forecasting prices, demand, and indexes.

• Forecasting Models
• Monte Carlo Simulation
• Supply Chain Network Optimization
• Supply Chain Management Strategy

• Linear Regression

• Decision Trees

• K-Means Clustering

• Python Programming

• Python for Data Science

• Exploratory Data Analysis (EDA)

• Analyzing Text Data

• Bagging

• Boosting

• Model Tuning

• Word Embeddings

• Attention Mechanism and Transformers

• Large Language Models and Prompt Engineering

• Retrieval Augmented Generation

• Introduction to Neural Networks

• Optimising Neural Networks

• Image Processing

• Convolutional Neural Networks

• Introduction to Model Deployment

• Containerisation

• Querying Data With SQL

• Advanced Querying

• Enhancing Query Proficiency

• Inferential Statistics Foundations

• Estimation and Hypothesis Testing

• Common Statistical Tests

• Model Interpretability

• Introduction to MLOps and DevOps

• Building ML Pipelines

• AI Assistant Development

• Fine-tuning LLMS

Course 1: Introduction to Data Science and AI Course 2: Multimodal Generative AI

Course 3: Recommendation Systems

Course 4: Object Detection and Segmentation

Course 5: Reinforcement Learning

Course 6: Time Series Forecasting

ENGINEERING AI SOLUTIONS

LEARNING OUTCOMES OF THIS UNIT:

MACHINE LEARNING

LEARNING OUTCOMES OF THIS UNIT:

• Use Python for writing appropriate codes to solve a given problem

• Apply suitable clustering/dimensionality reduction techniques to perform unsupervised learning on unlabelled data in a real-world scenario

• Apply linear and logistic regression/classification and use model appraisal techniques to evaluate develop models

• Use the concept of KNN (k-nearest neighbourhood) and SVM (support vector machine) to analyse and develop classification models for solving real-world problems

• Apply decision tree and random forest models to demonstrate multi-class classification models 

• Implement model selection and compute relevant evaluation measure for a given problem

REAL-WORLD ANALYTICS

LEARNING OUTCOMES OF THIS UNIT:

Apply knowledge of multivariate functions data transformations and

data distributions to summarise data sets

• Analyse datasets by interpreting summary statistics, model and function parameters

• Apply game theory, and linear programming skills and models, to make optimal decisions

• Develop software codes to solve computational problems for real world analytics

• Demonstrate professional ethics and responsibility for working with real world data
  
  
  
  

DATA WRANGLING

LEARNING OUTCOMES OF THIS UNIT:

• Undertake data wrangling tasks by using appropriate programming and scripting languages to extract, clean, consolidate, and store data of different data types from a range of data sources

• Research data discovery and extraction methods and tools and apply resulting learning to handle extracting data based on project needs

• Design, implement, and explain the data model needed to achieve project goals, and the

processes that can be used to convert data from data sources to both technical and

non-technical audiences

• Use both statistical and machine learning techniques to perform exploratory analysis on

data extracted, and communicate results to technical and non-technical audiences

• Apply and reflect on techniques for maintaining data privacy and exercising ethics in data handling