1. Introduction to Programming

• What is programming?
• Compiler,Interpreter
• Source Code
• Machine code
• Algorithms
• Editors

2. Introduction to Data Science

• What is Data Science?
• Job Roles
• Terminologies
• Data Science Applications and its work flows

1. Introduction to SQL

• Introduction to databases and database management system
• Overview of MySQL
• Installing and getting started with MySQL workbench.

2. SQL Database

• Creating databases
• Dropping databases
• Introduction to tables
• Data types in MySQL

3. Data Definition Language

• Introduction DDL commands
• Creating table
• Modifying table using ALTER, DROP, TRUNCATE, RENAME

4. Data Manipulation Language

• Overview of MySQL SELECT statement
• Retrieving data
• Filtering and sorting
• Joining and combining data
• Updating and deleting data
• Inserting data
• Constraints(PRIMARY KEY, FOREIGN KEY, UNIQUE, NOT NULL.

5. MySQL Functions and Joins

MySql built-in functions, inner, outer and self joins and combining data from multiple tables.

6. MySQL subqueries and views

Subqueries and nested queries,creating and using views, normalization and denormalization

7. MySQL stored procedures and triggers

Creating and using stored procedures and triggers, database security and user management.

Project

Build a simple database to help us manage the booking process of a sports complex. The sports complex has the following facilities: 

• 2 tennis courts
• 2 badminton courts,
• 2 multi-purpose fields
• 1 archery range.

Each facility can be booked for a duration of one hour. Only registered users are allowed to make a booking. After booking, the complex allows users to cancel their bookings latest by the day prior to the booked date. Cancellation is free. However, if this is the third (or more) consecutive cancellations, the complex imposes a $10 fine

1. Language Introduction and Installation

• Python history
• Python features
• python and pycharm installation.

2. Python Basics

• Print command
• Comments,escape sequences
• Variables
• Data types
• User interactive command, operators

3. Conditional and looping statements

• Selection statements
• Control statements
• Break and continue statements
• Nested loops

4. Data Structures in python

Introduction to user defined data structures and non-primitive data structures:list,

dictionaries, set,tuples, strings and sequences,accessing and modifying elements in data

structures,comprehension: list, set and dictionary.

5. Functions in Python

Defining functions, passing arguments to functions, different types of arguments, returning

values from functions, local and global namespace, lambda function, recursion, filter,

map,reduce, eval. Generators and decorators.

6. File Handling and Exception Handling

File processing, Reading and writing files using ‘with’ statements. What is an Exception?,

raising and catching exceptions and handling errors gracefully using try-catch-finally.

7. Object Oriented Programming

Introduction to OOPs, Classes and objects, inheritance and polymorphism, encapsulation

and abstraction.

8. Modules in Python

Introduction to modules, importing modules, creating and using modules.

9. Regular Expressions

Defining regular expressions, using regular expressions with python.

10.Pandas and Numpy

Introduction to Pandas library, reading and writing data with pandas, data cleaning and

exploration with pandas. Introduction to numpy, numpy basic operation.

11. Data Visualization with Matplotlib and seaborn

Introduction, basic and advanced plotting technique

1. PowerBI Introduction

• Introduction
• PowerBI
• Power query editor
• PowerBI service, 
• Installation and configuration of powerBI desktop
• Connecting to data sources and loading data

2. Data modeling in PowerBI

Understanding data modeling and relationships creating relationships between tables, building hierarchies and calculated columns, transforming using power query.

3. Data Visualization in PowerBI

Understanding data visualization best practices, building basic visualizations(charts, graphs, tables etc..), formatting and customizing visualizations.

4. Building Interactive reports 

Using filters and slicers to create interactive reports, building drill-through reports and drill-down visualizations, creating bookmarks and scenarios.

5. Creating Dashboards

Building dashboards with multiple visualizations, creating dashboards tiles and adding images, creating custom visuals using powerBI

6. Data Analytics with PowerBI 

Understanding data analytics and DAX language, creating DAX formulas and measures.

7. Project

Financial Performance analysis: to optimize financial reporting in a firm that provides customers to track their financial health and productivity. Create data models and visualizations, and dashboards and present the project results and insights

1. Probability & Statistics

Definition of probability,conditional probability,independent events, Bayes' rule, Random variables, discrete random variable, continuous random variable, probability density function, mean, median, mode, Standard deviation, correlation, correlation coefficient. Testing of hypothesis, confidence interval, Chi-squared test, t-test

2. Introduction to Machine Learning

Machine learning concepts and applications,Overview of Supervised, Unsupervised, and Reinforcement learning.Exploring common machine learning algorithms

3. Data Preprocessing for Machine Learning

Types of Data: Categorical and Numerical Data, Understanding data preprocessing and its importance in machine learning, Handling missing data, outliers, and categorical variables, Label Encoding, Feature scaling and normalization techniques.Python Library: Numpy, Pandas, Sklearn

4. Project

Use a dataset then remove NaN values and apply label encoder and scaling methods.

5. Supervised Learning : Regression

Understanding regression analysis and its use in machine learning, Building linear regression models,

Evaluating model performance and making predictions.

6. Project using Regression

Create a model using Linear regression algorithms and predict using the best algorithm among them.

7. Supervised Learning

Classification Understanding classification analysis and its use in machine learning, Building logistic regression, Support Vector Machines, Random Forest, K-Nearest Neighbor, Naïve Bayes and Decision tree models. Evaluating model performance using different classification accuracy metrics and making predictions.

8. Project

Create a model using different algorithms for a given dataset and choose the best algorithm among them.

9. Unsupervised Learning

Clustering Understanding clustering analysis and its use in machine learning, Building k-means and hierarchical clustering models,Evaluating model performance and making predictions,

10. Project

Create a model using different algorithms and predict using the best algorithm among them.

11. Unsupervised Learning

Dimensionality Reduction Understanding dimensionality reduction and its use in machine learning, Building principal component analysis (PCA) and t-SNE models, Evaluating model performance and making predictions.

12. Natural Language Processing

Basic concept of NLP, Data Cleaning: remove punctuations, tokenization, remove stop words, stemming, lemmatization.Packages of NLP : NLTK (Natural Language ToolKit), Pattern, TextBlob,Vectorization techniques: Bag of Words, TF-IDF

13. Project

For the given paragraph do the following: word_tokenise and sent_tokenise, using stop words eliminate most common words and do stemming and lemmatization.

eep learning basics: Neural Network, perceptron,Back-Propagation, Activation functions, Deep networks, Regularization, Dropout, Batch Normalization.Python libraries for Deep learning : Keras, Tensor flow. Convolutional neural networks: Introduction to CNNs, Convolution, Correlation, FIltering. CNN architectures, Compiling and fitting a model Advanced Deep architectures:Recurrent Neural networks (RNNs), Advanced RNN: LSTM

Project

Create a deep network model using CNN and calculate its accuracy and loss values.