Data Science

  Module 1: Python Fundamentals (40 hours)
Python basics: variables, data types, operators, and expressions
 Data types: integers, floats, booleans, strings, and lists
 Operators: arithmetic, logical, and comparison
 Expressions and statements
Control structures: loops, conditionals, and functions
 If-else statements and conditional expressions
 Loops: for and while
 Functions: syntax, parameters, and return statements
 Recursion
Object-oriented programming concepts: classes, objects, and inheritance
 Class definitions: instance variables, methods, and constructors
 Objects: creating and using objects, passing objects as arguments
 Inheritance: extending classes, method overriding, and super()
Introduction to data structures: lists, tuples, and dictionaries
 Lists: indexing, slicing, appending, and sorting
 Tuples: creating, packing and unpacking, and iterating
 Dictionaries: creating, accessing, and updating key-value pairs
Input and output operations with files
 Reading and writing text files
 Handling exceptions and errors
 Using the OS and SYS modules

  Module 2: SQL Fundamentals (30 hours)
Relational databases: tables, records, and fields
 Creating tables and defining relationships
 Inserting data into tables
 Updating and deleting data from tables
 Using aggregate functions
SQL basics: SELECT, INSERT, UPDATE, DELETE statements
 SELECT statement: querying data from one or multiple tables
 INSERT statement: adding data to a table
 UPDATE statement: modifying existing data
 DELETE statement: removing data from a table
Querying multiple tables with joins
 Inner, left, right, and full outer joins
 Self-joins and subqueries
 Using aliases and aggregate functions with joins
 Using union and intersect operators
Aggregating data with GROUP BY and HAVING clauses
 Grouping data by one or more columns
 Using aggregate functions with GROUP BY
 Filtering groups with the HAVING clause
 Using nested queries with GROUP BY and HAVING
Subqueries and correlated subqueries
 Using subqueries in SELECT, FROM, WHERE, and HAVING clauses
 Comparing subqueries with joins
 Using correlated subqueries

  Module 3: Data Exploration and
Visualization with Tableau and Power BI (30 hours)
Introduction to Tableau and Power BI
 Getting started with Tableau and Power BI
 Connecting to data sources
 Creating visualizations and dashboards
 Sharing and publishing reports
Creating charts, graphs, and maps
 Creating basic charts and graphs
 Using different chart types
 Adding labels, titles, and annotations
 Creating geographic maps
Calculated fields and table calculations
 Creating calculated fields
 Using basic arithmetic and logical operators
 Using aggregate functions in calculated fields
 Creating table calculations
Interactive dashboards and storyboards
 Creating interactive dashboards
 Using filters and parameters
 Adding interactivity with actions
 Creating a storyboard with Tableau and Power BI

  Module 4: Machine Learning Fundamentals
(40 hours)
Introduction to Machine Learning
 Supervised vs unsupervised learning
 Linear regression
 Model evaluation metrics
Classification algorithms
 Decision trees
 Random forests
 Naive Bayes
Clustering algorithms
 K-means
 Hierarchical clustering
Dimensionality Reduction techniques
 PCA
 t-SNE
Model selection and tuning
 Grid search
 Cross-validation

  Module 5: Deep Learning Fundamentals
(40 hours)
Introduction to Neural Networks
 Feedforward neural networks
 Backpropagation algorithm
 Activation functions
Convolutional Neural Networks (CNN)
 Image classification
 Object detection
Recurrent Neural Networks (RNN)
 Sequence modelling
 Sentiment analysis
Autoencoders and Generative Adversarial Networks (GAN)
 Image generation
 Image to Image Translation

  Module 6: Artificial Intelligence Fundamentals
(25 hours)
Introduction to AI
 Agents and environments
 Search algorithms
Logic and Planning
 Propositional logic
 First-order logic
 Planning
Natural Language Processing (NLP)
 Text classification
 Information extraction
 Sentiment analysis
Robotics
 Kinematics and dynamics
 Path planning
 Control