Data Science with R and Python

• Statistical Learning 
• Measures of central tendency, Measures of dispersion, Probability theory, Hypothesis testing, ANOVA, Types of graphs and plots. 
• Python Environment Setup and Essentials 
• Hands-on Exercise – Installing Python Anaconda for the Windows, Linux and Mac. 
• R Environment Setup and Essentials 
• Hands-on Exercise – Installing R for the Windows, Linux and Mac, Exploratory data analysis, Basic operators in R, Data Manipulation, Data visualisation. 
• Python language Basic Constructs 
• OOP concepts in Python 
• Hands-on Exercise – important concepts in OOP like polymorphism, inheritance, encapsulation, Python functions, return types, and parameters, Lambda expressions, 
• NumPy for mathematical computing 
• Hands-on Exercise – How to import NumPy module, creating array using ND-array, calculating standard deviation on array of numbers, calculating correlation between two variables. 
• SciPy for scientific computing 
• Hands-on Exercise – Importing of SciPy, applying the Bayes theorem on the given dataset. 
• Matplotlib for data visualization 
• Hands-on Exercise – deploying MatPlotLib for creating Pie, Scatter, Line, Histogram. 
• Pandas for data analysis and machine learning 
• Hands-on Exercise – working on importing data files, selecting record by a group, applying filter on top, viewing records, analyzing with linear regression, and creation of time series. 
• Introduction to Machine Learning with R and Python 
• The need for Machine Learning, Introduction to Machine Learning, types of Machine Learning, such as supervised, unsupervised and reinforcement learning, why Machine Learning with Python, R and applications of Machine Learning. 
• Supervised Learning and Linear Regression 
• Hands-on Exercise – Implementing linear regression from scratch with R and Python, Using Python library Scikit-learn to perform simple linear regression and multiple linear regression, Implementing train–test split and predicting the values on the test set. 
• Classification and Logistic Regression 
• Hands-on Exercise – Implementing logistic regression from scratch with R and Python, Using Python library Scikit-learn to perform simple logistic regression and multiple logistic regression, Building a confusion matrix to find out the accuracy, true positive rate, and false-positive rate. 
• Decision Tree and Random Forest 
• Hands-on Exercise – Implementing a decision tree from scratch in R and Python, Using Python library Scikit-learn to build a decision tree and a random forest, Visualizing the tree and changing the hyperparameters in the random forest. 
• Naïve Bayes and Support Vector Machine 
• Hands-on Exercise – Using Python library Scikit-learn to build a Naïve Bayes classifier and a support vector classifier. 
• Unsupervised Learning 
• Hands-on Exercise – Using Python library Scikit-learn to implement K-means clustering, Implementing PCA (principal component analysis) on top of a dataset. 
• Natural Language Processing and Text Mining 
• Project 
• Time Series Analysis 
• Hands-on Exercise – Analyzing time series data, the sequence of measurements that follow a non-random order to recognize the nature of the phenomenon, and forecasting the future values in the series. 

Certification Project

• The learner will have to submit a certification project and will be rewarded with a certificate once this project is completed. 
• Multiple Choice Questions & Answers: 
• Learners will be asked with multiple choices Q&A during the training sessions and points will be provided. 
• Scenario-based Questions & Answers: 
• Learners will have to submit scenario-based Q&A and points will be provided. 
• Sample Project: 
• A Sample Project will be discussed and shown to the learners that will help learners to start working in a project.