In this course we will get to know how to use R in Data Science and Data Analytics Platform
Installing and configuring essential library for Statistical Analysis and for Predictive Model
Using Different Libraries, Creating Function and different type of R code
How to use different graphical chart in R
This lesson will explain the learners about basics of statistics which are used in Data Science
Introduction of course and setting expectation for pre-requisites
What is Statistics
Population and Sample
Types of Data
Measurements
Scale of Measurement – Nominal, Ordinal, Interval and Ratio
Descriptive and Inferential Statistics
Frequency Distributions
Measures of Central Tendency
Comparisons of different measures of Tendency
Measures of Dispersion
Comparisons of different measures of Dispersion
Coefficient of Variation
Moments and Quantiles
Skewness and Kurtosis
Correlation and Regression
Distribution
This lesson will help the learners identify the need for Data Analytics and explain the concept of Data warehouse, data mining and statistical analysis
Coke Case study
Introduction to Business Analytics
Features of Business Analytics
Types of Business Analytics
Application of Business Analytics
Business Analytics and Data Science
This lesson will help the learners to describe R programing and learn its different operators to perform different kind of operations on the data
Supermarket Case Study
An Introduction to R
Features of R
Importance of R for Data Analytics
Installing R on Windows from CRAN Website
Lab Demo for R
Data Types and variables in R
Operators in R
Conditional Statements in R
Loops in R
Features of R Script
Functions and Help in R
Explain the different data structures used in R it will Discuss the elements of the different data structures in R. This lesson will also explain the acceptable formats to import and export data in R
Identifying Data Structures
Types of Data Structures – Atomic Vectors
Matrix
Arrays
Factors
Data Frames
Lists
Assigning Values to Data Structures – Importing Data
Assigning Values to Data Structures – Exporting Data
Data Manipulation
Types of apply functions
DPLYR package
This lesson will help the learners to explain the various types of graphics available in R,List the possible file formats of graphic outputs,Describe the methods to save graphics as files and Describe the procedure to export graphs in R
Introduction to Data Visualization
Data Visualization in R
Bar Chart
Pie chart
Histogram
Kernel Density Plot
Line chart
Box Plot
Heat map
Word Cloud
GGPLOT2
File formats of Graphic Outputs
This lesson will help the learners to explain the discuss the need of hypothesis testing in businesses. This lesson will also help them differentiate between null and alternate hypotheses, Interpret the confidence level, significance level, and power of a test and explain the types of hypothesis tests
Introduction to Hypothesis
Types of Hypothesis
Data Sampling
Types of Errors
Confidence levels
Critical Region
Decision Making
Level of Significance
Confidence Coefficient
Critical Region Deviation
B risk
Power of Test
Factors affecting the Power of Test
This lesson will help the learners to explain the various parametric tests,discuss the types of null hypothesis tests and expalined them chi-square and ANOVA test
Parametric Test
Types of Parametric Test
Z-Test
T-Test
ANOVA
One way ANOVA
Two way ANOVA
Non-Parametric Tests
Types of Non-Parametric Tests
Chi-square test
Types of Chi-square test
Hypothesis Test about Mean
Hypothesis Test about Variance
Hypothesis Test about Proportions
This lesson will help the learners to explain regression analysis,describe them the different types of regression analysis models. This lesson will also help them list the functions to covert non-linear models to linear models
Introduction to Regression Analysis
Types of Regression Analysis Model
Simple
Multiple
Linear
Non-linear
Types of Linear Regression Model
Types of Non-linear Regression Model
Cross Validation
Non-linear Model to Linear Model
Measures of Regression Models
Principal Component Analysis
Factor Analysis of Dimensionality Reduction
Demo Project on Regression (Project 1)
This lesson will help the learners to explain classification,describe the classification system and process. This lesson will also help them list the various issues related to classification and prediction and explain the various classification techniques
Bank Loan Case study
Introduction to Classification
Types of Classification Algorithm
Logistic Regression
Support Vector Machine
KNN(k-Nearest Neighbors)
Naive Bayes Classifier
Detecting Spam
Decision Tree Classification
Random Forest Classification
Evaluating Classifier Models
Confusion Matrix
AUC-ROC Curve
Bias Variance Trade-off
K-fold Cross Validation
Demo Project on Classification (Project 2)
This lesson will help the learners to Explain clustering, describe clustering use cases and discuss clustering models.
Introduction to Clustering
Example of Clustering
Clustering Methods
Prototype based Clustering
Hierarchical Clustering
DBSCAN (Density based Clustering)
Demo Project on Clustering (Project 3)
This lesson will explain association rule mining, the parameters of interesting relationships,the strength measures of association rules and the Apriori algorithm