Module-1

• Introduction – Data Science (AI/ML)?

• Data Extraction

• Data Wrangling

• Data Exploration

• Data Visualisation

• Statistics

Module-2 Python

• Overview of Python

• Creating “Hello World” code

• Variables

• Python files I/O Functions

• Numbers

• Strings and related operations

• Tuples and related operations

• Lists and related operations

• Dictionaries and related operations

• Tuple – properties, related operations, compared with a list

• List – properties, related operations

• Dictionary – properties, related operations

Module-3

• NumPy – arrays

• Operations on arrays

• Indexing slicing and iterating

• Pandas – data structures & index operations

• Reading and Writing data from Excel/CSV formats into Pandas

• Matplotlib library

Module-4

• Python Revision (numpy, Pandas, scikit learn, matplotlib)

• What is Machine Learning?

• Machine Learning Use-Cases

• Machine Learning Process Flow

• Machine Learning Categories

• Linear regression

• What are Classification and its use cases?

Supervised Learning

• What is Decision Tree?

• Confusion Matrix

• What is Random Forest?

• What is Naïve Bayes?

• How Naïve Bayes works?

• Implementing Naïve Bayes Classifier

• What is Support Vector Machine?

Module-5

Unsupervised Learning

• What is Clustering & its Use Cases?

• What is K-means Clustering?

• How does K-means algorithm work?

• What is Hierarchical Clustering?

• How Hierarchical Clustering works? Reinforcement Learning

• What is Reinforcement Learning

• Why Reinforcement Learning

• Elements of Reinforcement Learning

• Exploration vs Exploitation dilemma

• Market Basket Analysis

Module-6

• Introduction to Dimensionality

• Why Dimensionality Reduction

• PCA (Principal Component Analysis)

• Factor Analysis Time Series Analysis (TSA)

• What is Time Series Analysis?

• Importance of TSA

• Components of TSA

• White Noise

• AR model (Auto regression )

• MA model (moving-average )

• ARMA model (Auto regressive moving average)

• ARIMA model ( Auto Regressive Integrated Moving Average )

• Stationarity

• Data Visualization

Hands on for all the modules:

o Creating “Hello World” code

o Linear Regression

o Logistic regression

o Decision tree

o Principal Component Analysis (PCA)

o Factor Analysis

o Time Series Analysis/ Forecasting

o Market Basket Analysis

o Data Visualization