Data Science with R Course

Learning Objectives:

In this module, you will get an introduction to R, and understand why it is so popular among Data Scientists. Starting with the installation of R and its components, you will load and learn about frequently used libraries. This module touches upon data structures in R, loops and control statements in R and teaches you how to write custom functions, nested functions and functions with arguments.You will learn all about loop functions available in R which are efficient and can be written with a single command.

Going further, you will explore string manipulations and regular expressions and see how functions can be extremely useful for text or unstructured data manipulations. This module also teaches how to import data from various sources in R and also how to write files from R and connect to various databases from R. You will get an overview of visualization in R with base and ggplot libraries, and grasp the Grammar of Graphics in a very structured easy-to-understand manner. The module ends with a hands-on session on a real-life case study.

Topics Covered:

• Intro to R Programming
• Installing and Loading Libraries
• Data Structures in R
• Control & Loop Statements in R
• Functions in R
• Loop Functions in R
• String Manipulation & Regular Expression in R
• Working with Data in R
• Data Visualization in R
• Case Study

Hands-on: 

• Know how to install R, R Studio and other libraries
• Write R Code to understand and implement R Data Structures
• Write R Code to implement loop and control structures in R
• Write R Code to read and write data from/to R
• Read data not only from CSV files but also using direct connection to various databases
• Write R Code to implement ggplot for data visualization
• Complex Real-Life Data Manipulation, Preparation & Exploratory Data Analysis case study

Learning Objectives:

This module explores basics like mean (expected value), median and mode. You will understand the distribution of data in terms of variance, standard deviation and interquartile range and get basic summaries about data and its measures, together with simple graphics analysis.

Through daily life examples, you will understand the basics of probability, marginal probability and its importance with respect to data science. Learn Baye’s theorem and conditional probability, and alternate and null hypothesis including Type1 error, Type2 error, power of the test, and p-value.

Topics Covered:

• Measures of Central Tendency
• Measures of Dispersion
• Descriptive Statistics
• Probability Basics
• Marginal Probability
• Bayes Theorem
• Probability Distributions
• Hypothesis Testing

Hands-on:

Formulate Hypothesis and perform Hypothesis Testing on a real production plant scenario.

Learning Objectives: 

This module analyses Variance and its practical use, covering strong concepts, model building, evaluating model parameters, measuring performance metrics on Test and Validation set. You will use Linear Regression with Ordinary Least Square Estimate to predict a continuous variable. Further you will learn to enhance model performance by means of various steps like feature engineering & regularization.

Along the way, you will learn about Dimensionality Reduction Technique with Principal Component Analysis and Factor Analysis, including methods to find the optimum number of components/factors using scree plot, one-eigenvalue criterion. You will be able to cement the concepts learnt through real life case studies with Linear Regression and PCA & FA.

Topics Covered:

• ANOVA
• Linear Regression (OLS)
• Case Study: Linear Regression
• Principal Component Analysis
• Factor Analysis
• Case Study: PCA/FA

Hands-on:

• With attributes describing various aspect of residential homes, you are required to build a regression model to predict the property prices. 
• Reduce Data Dimensionality for a House Attribute Dataset for more insights & better modeling

Learning Objectives:

In this module you will explore Binomial Logistic Regression for Binomial Classification Problems, including evaluation of model parameters, model performance using various metrics like sensitivity, specificity, precision, recall, ROC Curve, AUC, KS-Statistics, and Kappa Value. You will work with a real-life case study with Binomial Logistic Regression.

Next, you will learn about KNN Algorithm for Classification Problem, including techniques that are used to find the optimum value for K. You will see a real-life case study with KNN Decision Trees, to help you understand regression & classification problems. At the end of this module you will have working knowledge on Entropy, Information Gain, Standard Deviation reduction, Gini Index, and CHAID, among others.

Topics Covered:

• Logistic Regression
• Case Study: Logistic Regression
• K-Nearest Neighbor Algorithm
• Case Study: K-Nearest Neighbor Algorithm
• Decision Tree
• Case Study: Decision Tree

Hands-on: 

• With various customer attributes describing customer characteristics, build a classification model to predict which customer is likely to default a credit card payment next month. This can help the bank be proactive in collecting dues.
• Predict if a patient is likely to get any chronic kidney disease depending on the health metrics.
• Wine comes in various types. With the ingredient composition known, we can build a model to predict the the Wine Quality using Decision Tree (Regression Trees).

Learning Objectives:

In this module, you will understand Time Series Data and its components like Level Data, Trend Data and Seasonal Data; also work with the Exponential Smoothing Model and know when to use the same. You will know how to use Holt's model when your data has Constant Data, Trend Data and Seasonal Data and learn how to select the right smoothing constants for each set of circumstances.Finally, you will use Autoregressive Integrated Moving Average Model for building a Time Series Model and carry out a real-life case study with ARIMA.

Topics Covered:

• Understand Time Series Data
• Visualizing TIme Series Components
• Exponential Smoothing
• Holt's Model
• Holt-Winter's Model
• ARIMA
• Case Study: Time Series Modeling on Stock Price

Hands-on: 

• Write R code to Understand Time Series Data and its components like Level Data, Trend Data and Seasonal Data
• Write R code to Use Holt's model when your data has Constant Data, Trend Data and Seasonal Data.
• How to select the right smoothing constants.
• Write R code to Use Auto Regressive Integrated Moving Average Model for building Time Series Model
• Dataset including features such as symbol, date, close, adj_close, volume of a stock. This data will exhibit characteristics of a time series data. We will use ARIMA to predict the stock prices.

Learning Objectives:

You will work on an industry mentor guided group project to handle a real-life project, the same way you would execute a data science project in any business problem.

Topics Covered:

• Industry relevant capstone project under experienced industry-expert mentor

Hands-on: 

Project to be selected by candidates.