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, R, Spark 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 R
• Introduction to Machine Learning

Goal – In this Module, you should learn about different statistical techniques and terminologies used in data analysis.

Objectives – At the end of this Module, you should be able to:

• Define Statistical Inference
• List the Terminologies of Statistics
• Illustrate the measures of Center and Spread
• Explain the concept of Probability
• State Probability Distributions

Topics:

• What is Statistical Inference?
• Terminologies of Statistics
• Measures of Centers
• Measures of Spread
• Probability
• Normal Distribution
• Binary Distribution
• Central Limit theorem

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
• Exploratory Data Analysis
• Visualization of Data

Hands-On/Demo: 

•       Loading different types of dataset in R 
•       Arranging the data 
•       Plotting the graphs

Goal – Get an introduction to Machine Learning as part of this Module. You will discuss the various categories of Machine Learning and implement Supervised Learning Algorithms.

Objectives – At the end of this module, you should be able to:

• Define Machine Learning
• Discuss Machine Learning Use cases
• List the categories of Machine Learning
• Illustrate Supervised Learning Algorithms

Topics:

• What is Machine Learning?
• Machine Learning Use-Cases
• Machine Learning Process Flow
• Machine Learning Categories
• Supervised Learning (Linear Regression & Logistic Regression)

Hands-On/Demo:

• Implementing Linear Regression model in R
• Implementing Logistic Regression model in R 

Goal – In this module, you should learn the Supervised Learning Techniques and the implementation of various Techniques, for example, Decision Trees, Random Forest Classifier etc. 

Objectives – At the end of this module, you should be able to:

• Define Classification
• Explain different Types of Classifiers such as Decision Tree, Random Forest, Naïve Bayes Classifier, Support Vector Machine…etc

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?
• What is Navies Bayes?
• Support Vector Machine: Classification

Hands-On/Demo:

• Implementing Decision Tree model in R
• Implementing Linear Random Forest in R
• Implementing Navies Bayes model in R
• Implementing Support Vector Machine in R

Learn about Unsupervised Learning and the various types of clustering that can be used to analyze the data.

Objectives – At the end of this module, you should be able to:

• Define Unsupervised Learning
• Discuss the following Cluster Analysis (K – means Clustering, C – means Clustering, & Hierarchical Clustering)

Topics:

• What is Clustering & its Use Cases?
• What is K-means Clustering?
• What is C-means Clustering?
• What is Canopy Clustering?
• What is Hierarchical Clustering?

Hands-On/Demo:

• Implementing K-means Clustering in R
• Implementing C-means Clustering in R
• Implementing Hierarchical Clustering in R

In this module, you should learn about association rules and different types of Recommender Engines.

Objectives – At the end of this module, you should be able to:

• Define Association Rules
• Define Recommendation Engine
• Discuss types of Recommendation Engines (Collaborative Filtering & Content-Based Filtering

• Illustrate steps to build a Recommendation Engine

Topics:

• What is Association Rules & its use cases?
• What is Recommendation Engine & it’s working?
• Types of Recommendation Types
• User-Based Recommendation
• Item-Based Recommendation
• Difference: User-Based and Item-Based Recommendation
• Recommendation Use-case

Hands-On/Demo:

• Implementing Association Rules in R
• Building a Recommendation Engine in R

Discuss Unsupervised Machine Learning Techniques and the implementation of different algorithms, for

Objectives – At the end of this module, you should be able to:

• Define Text Mining
• Discuss Text Mining Algorithms (Bag of Words Approach & Sentiment Analysis)

Topics:

• The concepts of text-mining
• Use cases
• Text Mining Algorithms
• Quantifying text

Hands-On/Demo:

• Implementing Bag of Words approach in R
• Implementing Sentiment Analysis on twitter Data using R

In this module, you should learn about Time Series data, different component of Time Series data, Time Series modelling – Exponential Smoothing models and ARIMA model for Time Series forecasting.

Objectives – At the end of this module, you should be able to:

• Describe Time Series data
• Format your Time Series data
• List the different components of Time Series data
• Discuss different kind of Time Series scenarios 
• Choose the model according to the Time series scenario
• Implement the model for forecasting
• Explain working and implementation of ARIMA model
• Illustrate the working and implementation of different ETS models
• Forecast the data using the respective model

 Topics:

• What is Time Series data?
• Time Series variables
• Different components of Time Series data
• Visualize the data to identify Time Series Components
• Implement ARIMA model for forecasting
• Exponential smoothing models
• Identifying different time series scenario based on which different Exponential Smoothing model can be applied
• Implement respective ETS model for forecasting

Hands-On/Demo:

•       Visualizing and formatting Time Series data
•       Plotting decomposed Time Series data plot
•       Applying ARIMA and ETS model for Time Series forecasting
•       Forecasting for given Time period

1. Basics of Python for Data Analysis
   • Why learn Python for data analysis?
   • Python 2.7 v/s 3.4
   • How to install Python?
   • Running a few simple programs in Python
2. Python libraries and data structures
   • Python Data Structures
   • Python Iteration and Conditional Constructs
   • Python Libraries
3. Exploratory analysis in Python using Pandas
   • Introduction to series and data frames
4. Data Mugging in Python using Pandas
5. Building a Predictive Model in Python
   • Logistic Regression
   • Decision Tree
   • Random Forest

Objectives of this module:

• To make the learner familiar with the basic principles of AI,
• Capable of using heuristic searches
• Aware of knowledge based systems
• Able to use fuzzy logic and neural networks
• Learn various applications domains AI

Foundations of AI

• Applications of AI
• Fundamentals of AI.
• Neural Network Foundations

Introduction to Artificial Neural Networks

• The Detailed ANN
• The Activation Functions
• How do ANNs work & learn
• Gradient Descent
• Stochastic Gradient Descent
• Backpropagation
• Understand limitations of a Single Perceptron
• Understand Neural Networks in Detail
• Illustrate Multi-Layer Perceptron
• Understand How Deep Network Works?
• How Backpropagation Works?
• Illustrate Forward pass, Backward pass
• Different variants of Gradient Descent

Convolutional Neural Networks

• Image classification
• Text classification
• Image classification and hyper-parameter tuning
• Emerging NN architectures

What are RNNs – Introduction to RNNs

• Recurrent neural networks rnn
• LSTMs understanding LSTMs
• long short term memory neural networks lstm in python

Tensorflow with Python

• Introducing Tensorflow
• Why Tensorflow?
• What is tensorflow?
• Tensorflow as an Interface
• Tensorflow as an environment
• Tensors
• Computation Graph
• Installing Tensorflow
• Tensorflow training
• Prepare Data
• Tensor types
• Loss and Optimization
• Running tensorflow programs

Building Neural Networks using Tensorflow

• Tensors
• Tensorflow data types
• CPU vs GPU vs TPU
• Tensorflow methods
• Introduction to Neural Networks
• Neural Network Architecture
• Linear Regression example revisited
• The Neuron
• Neural Network Layers
• The MNIST Dataset
• Coding MNIST NN

Deep Learning using Tensor flow

• Deepening the network
• Images and Pixels
• How humans recognise images
• Convolutional Neural Networks
• ConvNet Architecture
• Overfitting and Regularization
• Max Pooling and ReLU activations
• Dropout
• Strides and Zero Padding
• Coding Deep ConvNets demo
• Debugging Neural Networks
• Visualising NN using Tensorflow
• Tensorboard
• Auto-encoders and unsupervised learning
• Stacked auto-encoders and semi-supervised learning
• Regularization – Dropout and Batch normalization

Introduction to Data Visualization

• What is Data Visualization? 
• How does it work? 
• Why is it important?
• Important types of Data Visualizations? 
• Tools to make visualizations 
• Which industry uses it?
• Case Study

Data Analytics in Excel

• What is Data Analytics in Excel? 
• How does it work? 
• Why is it important? 
• Important types of Data 
• Tools used for Data Analytics in Excel 
• Which industry uses it 
• Case Study

Tableau 

• Introduction to Tableau 
• How to connect to Data? 
• Loading Data on Tableau 
• Loading Workbooks on Tableau 
• Navigating Tableau 
• Interactive Elements in Tableau 
• Dimensions and Measures in tools 
• Building your first visualization 
• Improving your first visualization 
• Bringing it all together

Power BI

• Introduction to BI 
• Why Power BI is important today? 
• Key benefits of using Power BI 
• Flow of Power BI 
• Components of Power BI 
• Architecture of Power BI 
• Building Blocks of Power BI 
• Data sources in Power BI Desktop 
• Connecting to a Data Source 
• Combining Data – Merging & Appending 
• Cleaning Irregularly formatted data 
• How to Perform Modelling of Data? 
• Optimizing Data Models