Introduction to Data Science

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, Spark and Machine Learning play in Data Science
• 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 Big Data and Hadoop
• Introduction to R
• Introduction to Spark
• Introduction to Machine Learning
• None

Data Extraction, Wrangling and Exploration

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
• Data Analysis Pipeline
• What is Data Extraction
• Types of Data
• Raw and Processed Data
• Data Wrangling
• Exploratory Data Analysis
• Visualization of Data
• Loading different types of dataset in Python
• Arranging the data
• Plotting the graphs

Recommender Engines

Goal – In this module, you should learn about association rules and different types of Recommender
Objectives – At the end of this module, you should be able to:
• Define Association Rules
• Define Recommendation Engine
• Discuss types of Recommendation Engines
o Collaborative Filtering and Content-Based Filtering
• Illustrate steps to build a Recommendation Engine
• 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 Use-case
• Implementing Association Rules in Python
• Building a Recommendation Engine in Python

Time Series

Goal – In this module, you should learn about Time Series data, different component of Time Series
data, Time Series modeling – Exponential Smoothing models and ARIMA model for Time Series
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
• 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

Deep Learning

Goal – Get introduced to the concepts of Reinforcement learning and Deep learning in this Module.
These concepts are explained with the help of Use cases. You will get to discuss Artificial Neural
Network, the building blocks for artificial neural networks, and few artificial neural network
Objectives – At the end of this module, you should be able to:
• Define Reinforced Learning
• Discuss Reinforced Learning Use cases
• Define Deep Learning
• Understand Artificial Neural Network
• Discuss basic Building Blocks of Artificial Neural Network
• List the important Terminologies of ANN’s
• Reinforced Learning
• Reinforcement learning Process Flow
• Reinforced Learning Use cases
• Deep Learning
• Biological Neural Networks
• Understand Artificial Neural Networks
• Building an Artificial Neural Network
• How ANN works
• Important Terminologies of ANN’s
Hands-On/Demo: None

Statistical Inference

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
• What is Statistical Inference?

• Terminologies of Statistics
• Measures of Centers
• Measures of Spread
• Probability
• Normal Distribution
• Binary Distribution
• None

Introduction to Machine Learning

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 AlgorithmsTopics
• What is Machine Learning?
• Machine Learning Use-Cases
• Machine Learning Process Flow
• Machine Learning Categories
• Supervised Learning
o Linear Regression
o Logistic Regression
• Implementing Linear Regression model in Python
• Implementing Logistic Regression model in Python

Text Mining

Goal – Discuss Unsupervised Machine Learning Techniques and the implementation of different
algorithms, for example, TF-IDF and Cosine Similarity in this Module.
Objectives – At the end of this module, you should be able to:

• Define Text Mining
• Discuss Text Mining Algorithms
o Bag of Words Approach
o Sentiment Analysis
• The concepts of text-mining
• Use cases
• Text Mining Algorithms
• Quantifying text
• Beyond TF-IDF
• Implementing Bag of Words approach in Python
• Implementing Sentiment Analysis on twitter Data using Python 

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