Python Certification Training for Data Science

Goal: Give a brief idea of what Python is and touch on the basics. 

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

• Define Python
• Understand the need for Programming
• Know why to choose Python over other languages
• Setup Python environment
• Understand Various Python concepts – Variables, Data Types Operators, Conditional Statements and Loops
• Illustrate String formatting
• Understand Command Line Parameters and Flow control

Topics:

• Overview of Python
• Companies using Python
• Other applications in which Python is used
• Discuss Python Scripts on UNIX/Windows
• Variables
• Operands and Expressions
• Conditional Statements
• Loops
• Command Line Arguments
• Writing to the screen

Hands-on/Demo:

• Creating “Hello World” code
• Variables
• Demonstrating Conditional Statements
• Demonstrating Loops

Goal: Learn different types of sequence structures, related operations, and their usage. Also learn diverse ways of opening, reading, and writing to files. 

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

• Understand Operations performed on Files
• Learn what Sequences are
• Execute Sequence Operations
• Understand Types of Sequences in Python: Lists, Tuples, Strings, Sets, Dictionaries

Topics:

• Python files I/O Functions
• Lists and related operations
• Tuples and related operations
• Strings and related operations
• Sets and related operations
• Dictionaries and related operations

Hands-on/Demo:

• Tuple - properties, related operations, compared with a list
• List - properties, related operations
• Dictionary - properties, related operations
• Set - properties, related operations

Learning Objectives: In this Module, you will learn how to create generic python scripts, how to address errors/exceptions in code and finally how to extract/filter content using regex. 

Topics:

• Functions
• Function Parameters
• Global Variables
• Variable Scope and Returning Values
• Lambda Functions
• Object-Oriented Concepts
• Standard Libraries
• Modules Used in Python
• The Import Statements
• Module Search Path
• Package Installation Ways
• Errors and Exception Handling
• Handling Multiple Exceptions

Hands On/Demo:

• Functions - Syntax, Arguments, Keyword Arguments, Return Values
• Lambda - Features, Syntax, Options, Compared with the Functions
• Sorting - Sequences, Dictionaries, Limitations of Sorting
• Errors and Exceptions - Types of Issues, Remediation
• Packages and Module - Modules, Import Options, sys Path

Skills:

• Error and Exception management in Python
• Working with functions in Python

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 Use-Cases
• Machine Learning Process Flow
• Machine Learning Categories
• Linear regression
• Gradient descent

 Hands On:

• Linear Regression – Using Boston Dataset

Goal: Through this Module, you will understand in detail about Data Manipulation. 

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

• Perform function manipulations on Data objects
• Perform Concatenation, Merging and Joining on DataFrames
• Iterate through DataFrames
• Explore Datasets and extract insights from it

Topics:

• Basic Functionalities of a data object
• Merging of Data objects
• Concatenation of data objects
• Types of Joins on data objects
• Exploring a Dataset
• Analyzing a dataset

Hands-on/Demo:

• Pandas Function- Ndim(), axes(), values(), head(), tail(), sum(), std(), iteritems(), iterrows(), itertuples(), GroupBy operations, Aggregation, Concatenation, Merging and joining

Learning Objectives: In this module, you will learn the concept of Machine Learning and its types. 

Topics:

• Python Revision (numpy, Pandas, scikit learn, matplotlib)
• What is Machine Learning?
• Machine Learning Use-Cases
• Machine Learning Process Flow
• Machine Learning Categories
• Linear regression
• Gradient descent

Hands On/Demo:

• Linear Regression – Boston Dataset

Skills:

• Machine Learning concepts
• Machine Learning types
• Linear Regression Implementation

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?

 Hands On:

• Implementation of Logistic regression, Decision tree, 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

 Hands On:

• PCA
• Scaling

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

 Hands On:

• Implementation of Naïve Bayes, SVM

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

 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

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

 Hands On:

• Apriori Algorithm
• Market Basket Analysis

 Introduction to Reinforcement Learning

Learning Objectives: The aim of this module is to introduce you to the fundamentals of Reinforcement Learning and its elements. This module also introduces you to OpenAI Gym - a programming environment used for implementing RL agents. 

Topics:

• Branches of Machine Learning
• What is Reinforcement Learning?
• The Reinforcement Learning Process
• Elements of Reinforcement Learning
• RL Agent Taxonomy
• Reinforcement Learning Problem
• Introduction to OpenAI Gym

 Bandit Algorithms and Markov Decision Process

Learning Objectives: The aim of this module is to learn Bandit Algorithms and Markov Decision Process. 

Topics:

• Bandit Algorithms
• Markov Process
• Markov Reward Process
• Markov Decision Process

 Dynamic Programming & Temporal Difference Methods

Learning Objectives: The aim of this module is to develop an understanding of Dynamic Programming Algorithms and Temporal Difference Learning methods. 

Topics:

• Introduction to Dynamic Programming
• Dynamic Programming Algorithms
• Monte Carlo Methods
• Temporal Difference Learning Methods

 Deep Q Learning

Learning Objectives: The aim of this module is to learn Policy Gradients and develop an understanding of Deep Q Learning 

Topics:

• Policy Gradients
• Policy Gradients using TensorFlow
• Deep Q learning
• Q learning with replay buffers, target networks, and CNN

 In-class Project

Goal:

•  The aim of this module is to provide you hands-on experience in Reinforcement Learning.

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

 Hands on:

• Checking Stationarity
• Converting a non-stationary data to stationary
• Implementing Dickey Fuller Test
• Plot ACF and PACF
• Generating the ARIMA plot
• TSA Forecasting

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

 Hands on:

• Cross Validation
• AdaBoost