• Statistics & Mathematics functions & conditional formatting
• Statistics & mathematics for Data Analysis
• Lookup, Index & Match, Logical, Text Functions, Pivot Tables
• Data Cleaning for Data Analysis
• Introduction to Power Query
• Charts, Dashboards, Regression, and Forecasting
• Macros & coding (Advanced Excel )

• Introduction to Tableau, Charts and Maps, Fundamentals of Data Visualization and Reporting.
• Introduction to Data Cleaning and Preparation using Power BI
• Introduction to Calculated Fields, Table Calculations, Aggregations, Granularity and LOD Expressions.
• Introduction to Feature Engineering and Data Modelling in Power
• Introduction to Data Extracts, Filters, Tableau Dashboards, Tableau Storyboards, and Formatting.
• Introduction to Charts, Maps, and Dashboards in Power

• Variables, Operators, Strings, Datatypes, and Data Structures such as Lists, Tuples, Dictionaries, and Sets.
• Introduction to Fundamentals of R Programming
• Functions, Parameters, Arguments, Anonymous Functions, Strings, String Methods, Regular Expressions
• Introduction to Statistical Analysis and Functions in R
• Introduction to Loops, Conditionals, Break, Continue, Object Oriented Concepts, and Space-Time Complexity.
• Introduction to Data Visualization using ggplot in R

• Descriptive Statistics, Basic and Conditional Probability
• EDA, and Data Preparation using SAS
• Introduction to Inferential Statistics and Hypothesis Testing
• Using SQL Queries in SAS

• Introduction to Numpy and Pandas
• Query Analysis using Pandas
• Introduction to Matplotlib and Seaborn
• Introduction to Plotly and Express
• Dealing with Missing Values, Dealing with Outliers and Skewness, and Encoding Categorical Data
• Introduction to Data Manipulation Functions, Statistical Transformations, and Feature Engineering.
• Introduction to Sampling and Resampling Techniques, Introduction to Feature Scaling Techniques.

a.  Supervised Learning

• Introduction to Linear and Logistic
• Regularization Techniques such as Lasso, Ridge, Elastic
• Introduction to KNN, SVM, and Naive Bayes
• Implementation of Regression Algorithms in Real world
• Introduction to Decision Trees and Random
• Implementation of Classification Algorithms in Real-world
• Introduction to Boosting Algorithms and Imbalanced Machine
• Introduction to Advanced Modelling

b. Unsupervised Learning

• Introduction and Implementation of K Means and Hierarchical
• Evaluation Metrics for Unsupervised
• Introduction and Implementation of PCA and
• Case Study on Dimensionality

c.  Time Series and Recommender Systems

• Time Series Fundamentals, AR, MA, ARMA, ARIMA, SARIMA, ARIMAX
• Implementation of Time Series
• Content & Collaborative-based
• Implementation of Recommender

a.  SQL Databases and Big Data Analysis

• Database Fundamentals, DDL, DML, DQL

• Fundamentals of Mongo DB: Documents and
• SQL Joins, Sub-Queries, Set Operations, and Writing Complex
• Introduction to Mongo DB Replica sets, Shading and
• Accessing and Loading Databases and Performing Query Analysis in
• Fundamentals of Web
• Introduction to Py-Spark in Python and Spark for Big Data
• Introduction to Beautiful Soup and

b.  Cloud

• AWS
• Usage of AWS in Data Science
• AWS S3, and AWS
• Deployment of a Machine Learning Classification Model

a. Introduction to Model Deployment

• • Overview of Model Deployment, ML System Architecture, Packaging ML Model for Production.
  • Fundamentals of REST
  • Serving and Deploying the Model via REST API, Continuous Integrations, and Deployment Pipelines
  • Fundamentals of AWS, AWS S3, and AWS
  • Deploying ML API with Containers, Differential Testing, Deploying to IaaS (AWS EC2).
  • Deployment of a Machine Learning Classification

b. Introduction to Natural Language Processing

• • Fundamentals of Natural Language Processing, Part of Speech Tagging, Named Entity Recognition
  • Fundamentals of Text Data Cleaning
  • Introduction to Text Classification, Semantics Rule, and Fundamentals of Sentimental Analysis.
  • Working on Real-world Text Classification problem
  • Understanding the Complex concepts of Topic Modeling and Text Summarization Techniques.
  • Working on Real-world Topic Modeling problem

c. Introduction to Deep Learning

• • Introduction to Artificial Neural Networks
  • Fundamentals of Tensor flow
  • Introduction to Convolutional Neural Networks and CNN Architectures
  • Fundamentals of PyTorch
  • Data Structured Algorithm and Generative AI

• Diversity and Inclusion
• Organisational behavior
• Ethical practices
• Team Dynamics
• Organisational culture and structure

• Foundations of Corporate Governance
• Roles and Responsibilities
• Corporate Ethics and Ethical Decision- Making
• Risk Management and Internal Control
• Corporate social Responsibility (CSR) and ESG
• Regulatory and Legal Frameworks
• Global Perspectives and Challenges
• Contemporary Issues and Trends

• Foundations of Operations Management
• Supply Chain Strategy and Design
• Supply Chain Analytics and Technology
• Inventory and Logistics Management
• Quality and Performance Management
• Strategic Sourcing and Procurement
• Research Methods and Theoretical Frameworks (DBA-specific)
• Emerging Topics & Trends