Goal: Get an introduction to Data Science in this Module and see how Data Science helps to analyze large and unstructured data with different tools.
Objectives: At the end of this Module, you should be able to:
 Define Data Science
 Discuss the era of Data Science
 Describe the Role of a Data Scientist
 Illustrate the Life cycle of Data Science
 List the Tools used in Data Science
 State what role Big Data and Hadoop, Python, R and Machine Learning play in Data Science
Topics:
 What is Data Science?
 What does Data Science involve?
 Era of Data Science
 Business Intelligence vs Data Science
 Life cycle of Data Science
 Tools of Data Science
 Introduction to Python
Goal: Discuss the different sources available to extract data, arrange the data in structured form, analyze the data, and represent the data in a graphical format.
Objectives: At the end of this Module, you should be able to:
 Discuss Data Acquisition techniques
 List the different types of Data
 Evaluate Input Data
 Explain the Data Wrangling techniques
 Discuss Data Exploration
Topics:
 Data Analysis Pipeline
 What is Data Extraction
 Types of Data
 Raw and Processed Data
 Data Wrangling
 Exploratory Data Analysis
 Visualization of Data
Goal: In this module, you will learn the concept of Machine Learning and it’s types.
Objective: At the end of this module, you should be able to:
 Essential Python Revision
 Necessary Machine Learning Python libraries
 Define Machine Learning
 Discuss Machine Learning Use cases
 List the categories of Machine Learning
 Illustrate Supervised Learning Algorithms
 Identify and recognize machine learning algorithms around us
 Understand the various elements of machine learning algorithm like parameters, hyper parameters, loss function and optimization.
Topics:
 Python Revision (numpy, Pandas, scikit learn, matplotlib)
 What is Machine Learning?
 Machine Learning UseCases
 Machine Learning Process Flow
 Machine Learning Categories
 Linear regression
 Gradient descent
Goal: In this module, you will learn Supervised Learning Techniques and their implementation, for example, Decision Trees, Random Forest Classifier etc.
Objective: At the end of this module, you should be able to:
 Understand What is Supervised Learning?
 Illustrate Logistic Regression
 Define Classification
 Explain different Types of Classifiers such as Decision Tree and Random Forest
Topics:
 What is Classification and its use cases?
 What is Decision Tree?
 Algorithm for Decision Tree Induction
 Creating a Perfect Decision Tree
 Confusion Matrix
 What is Random Forest?
Goal: In this module you will learn about impact of dimensions within data. You will be taught to perform factor analysis using PCA and compress dimensions. Also, you will be developing LDA model.
Objective: At the end of this module, you should be able to:
 Define the importance of Dimensions
 Explore PCA and its implementation
 Discuss LDA and its implementation
Topics:
 Introduction to Dimensionality
 Why Dimensionality Reduction
 PCA
 Factor Analysis
 Scaling dimensional model
 LDA
Goal: In this module, you will learn Supervised Learning Techniques and their implementation, for example, Decision Trees, Random Forest Classifier etc.
Objective: At the end of this module, you should be able to:
 Understand What is Naïve Bayes Classifier
 How Naïve Bayes Classifier works?
 Understand Support Vector Machine
 Illustrate How Support Vector Machine works?
 Hyperparameter optimization
Topics:
 What is Naïve Bayes?
 How Naïve Bayes works?
 Implementing Naïve Bayes Classifier
 What is Support Vector Machine?
 Illustrate how Support Vector Machine works?
 Hyperparameter optimization
 Grid Search vs Random Search
 Implementation of Support Vector Machine for Classification
Goal: In this module, you will learn about Unsupervised Learning and the various types of clustering that can be used to analyze the data.
Objective: At the end of this module, you should be able to:

 Define Unsupervised Learning
 Discuss the following Cluster Analysis
o K – means Clustering
o C – means Clustering
o Hierarchical Clustering
Topics:
 What is Clustering & its Use Cases?
 What is Kmeans Clustering?
 How Kmeans algorithm works?
 How to do optimal clustering
 What is Cmeans Clustering?
 What is Hierarchical Clustering?
 How Hierarchical Clustering works?
Goal: In this module, you will learn Association rules and their extension towards recommendation engines with Apriori algorithm.
Objective: At the end of this module, you should be able to:
 Define Association Rules
 Learn the backend of recommendation engines and develop your own using python
Topics:
 What are Association Rules?
 Association Rule Parameters
 Calculating Association Rule Parameters
 Recommendation Engines
 How Recommendation Engines work?
 Collaborative Filtering
 Content Based Filtering
Goal: In this module, you will learn about developing a smart learning algorithm such that the learning becomes more and more accurate as time passes by. You will be able to define an optimal solution for an agent based on agent environment interaction.
Objective: At the end of this module, you should be able to
 Explain the concept of Reinforcement Learning
 Generalize a problem using Reinforcement Learning
 Explain Markov’s Decision Process
 Demonstrate Q Learning
Topics:
 What is Reinforcement Learning
 Why Reinforcement Learning
 Elements of Reinforcement Learning
 Exploration vs Exploitation dilemma
 Epsilon Greedy Algorithm
 Markov Decision Process (MDP)
 Q values and V values
 Q – Learning
 α values
Goal: In this module, you will learn about Time Series Analysis to forecast dependent variables based on time. You will be taught different models for time series modelling such that you analyse a real time dependent data for forecasting.
Objective: At the end of this module, you should be able to:
 Explain Time Series Analysis (TSA)
 Discuss the need of TSA
 Describe ARIMA modelling
 Forecast the time series model
Topics:
 What is Time Series Analysis?
 Importance of TSA
 Components of TSA
 White Noise
 AR model
 MA model
 ARMA model
 ARIMA model
 Stationarity
 ACF & PACF
Goal: In this module, you will learn about selecting one model over another. Also, you will learn about Boosting and its importance in Machine Learning. You will learn on how to convert weaker algorithms to stronger ones.
Objective: At the end of this module, you should be able to:
 Discuss Model Selection
 Define Boosting
 Express the need of Boosting
 Explain the working of Boosting algorithm
Topics:
 What is Model Selection?
 Need of Model Selection
 Cross – Validation
 What is Boosting?
 How Boosting Algorithms work?
 Types of Boosting Algorithms
 Adaptive Boosting