 Data Science
 6 (Registered)

Detailed Coverage:
 Introduction to Business Analytics
 Objectives:This lesson will help the learners identify the need for Data Analytics and explain the concept of Data warehouse, data mining and statistical analysis
 Need of Business Analytics
 Introduction to Business Analytics
 Features of Business Analytics
 Types of Business Analytics
 Descriptive Analytics
 Predictive Analytics
 Prescriptive Analytics
 Supply Chain Analytics
 Health care Analytics
 Human Resource Analytics
 Web Analytics
 Business Decisions
 Business Intelligence (BI)
 Data Science
 Importance of Data Science
 Data Science as a Strategic Asset
 Big Data
 Analytical Tools
 Introduction to R programing
 Objectives – This lesson will help the learners to describe R progaming and learn its different operators to perform different kind of operations on the data.
 An Introduction to R
 Comprehensive R Archive Network (CRAN)
 Cons of R
 Companies Using R
 Installing R on Various Operating Systems
 Installing R on Windows from CRAN Website
 IDEs for R
 Installing RStudio on Various Operating Systems
 Steps in R Initiation
 Benefits of R Workspace
 Setting the Workplace
 Functions and Help in R
 R Packages
 Installing an R Package
 Operators in R
 Arithmetic Operators
 Relational Operators
 Logical Operators
 Assignment Operators
 Conditional Statements in R
 Ifelse() Function
 Loops in R
 Break Statement
 Next Statement
 Scan Function
 Running an R Script
 Running a Batch Script
 R Functions
 Data Structure and Apply functions in R
 Objectives: 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 RStudio
 Graphics in R
 Types of Graphics
 Bar Charts
 Creating Simple Bar Charts
 Editing a Simple Bar Chart
 Pie Charts
 Histograms
 Creating a Histogram
 Kernel Density Plots
 Creating a Kernel Density Plot
 Line Charts
 Creating a Line Chart
 Box Plots
 Creating a Box Plot
 Heat Maps
 Creating a Heat Map
 World Clouds
 Creating a Word Cloud
 File Formats for Graphic Outputs
 Saving a Graphic Output as a File
 Exporting Graphs in RStudio
 Exporting Graphs as PDFs in RStudio
 Introduction to Statistics
 Objectives: This lesson will help the learners to explain basic concept of Statistics. It will hep them explaining the two types of data,describing the types of measurements,list the steps of statistical investigation and discuss the importance of normal distribution in statistics. This lesson will also explain the various distance measures Explain the various distance measures and describe the various types of correlations.
 Recap
 Basics of Statistics
 Types of Data
 Qualitative vs. Quantitative Analysis
 Types of Measurements in Order
 Nominal Measurement
 Ordinal Measurement
 Interval Measurement
 Ratio Measurement
 Statistical Investigation
 Statistical Investigation Steps
 Normal Distribution
 Example of Normal Distribution
 Importance of Normal Distribution in Statistics
 Use of the Symmetry Property of Normal Distribution
 Standard Normal Distribution
 Distance Measures
 Distance Measures—A Comparison
 Euclidean Distance
 Example of Euclidean Distance
 Manhattan Distance
 Minkowski Distance
 Mahalanobis Distance
 Cosine Similarity
 Correlation
 Correlation Measures Explained Pearson Product Moment Correlation (PPMC)
 Pearson Correlation—Case Study
 Dist() Function in R
 Hypothesis Testing I
 Objectives: 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.
 Recap
 Hypothesis
 Need of Hypothesis Testing in Businesses
 Null Hypothesis
 Alternate Hypothesis
 Null vs. Alternate Hypothesis
 Chances of Errors in Sampling
 Types of Errors
 Contingency Table
 Decision Making
 Critical Region
 Level of Significance
 Confidence Coefficient
 β Risk
 Power of Test
 Factors Affecting the Power of Test
 Types of Statistical Hypothesis Tests
 An Example of Statistical Hypothesis Tests
 Upper Tail Test
 Upper Tail Test
 Upper Tail Test
 Test Statistic
 Factors Affecting Test Statistic
 Critical Value Using Normal Probability Table
 Hypothesis Testing II
 Objectives: This lesson will help the learners to explain the various parametric tests,discuss the types of null hypothesis tests and expalined them chisquare and ANOVA test.
 Recap
 Parametric Tests
 ZTest
 ZTest in R—Case Study
 TTest
 TTest in R—Case Study
 Testing Null Hypothesis
 Testing Null Hypothesis
 Testing Null Hypothesis
 Testing Null Hypothesis
 Testing Null Hypothesis
 Testing Null Hypothesis
 Objectives of Null Hypothesis Test
 Three Types of Hypothesis Tests
 Hypothesis Tests About Population Means
 Decision Rules
 Hypothesis Tests About Population Means—Case Study 1
 Hypothesis Tests About Population Means—Case Study 2
 Hypothesis Tests About Population Proportions
 Hypothesis Tests About Population Proportions—Case Study 1
 ChiSquare Test
 Steps of ChiSquare Test
 Degree of Freedom
 ChiSquare Test for Independence
 ChiSquare Test for Goodness of Fit
 ChiSquare Test for Independence—Case Study
 ChiSquare Test in R—Case Study
 Introduction to ANOVA Test
 OneWay ANOVA Test
 The FDistribution and FRatio
 FRatio Test
 FRatio Test in R—Example
 OneWay ANOVA Test—Case Study
 OneWay ANOVA Test in R—Case Study
 Regression Analysis
 Objectives: 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 nonlinear models to linear models
 Recap
 Introduction to Regression Analysis
 Use of Regression Analysis—Examples
 Types Regression Analysis
 Simple Regression Analysis
 Multiple Regression Models Simple Linear Regression Model
 Simple Linear Regression Model Explained
 Correlation
 Correlation Between X and Y
 Method of Least Squares Regression Model
 Coefficient of Multiple Determination Regression Model
 Standard Error of the Estimate Regression Model
 Dummy Variable Regression Model
 Interaction Regression Model
 NonLinear Regression
 NonLinear Regression Models
 NonLinear Models to Linear Models
 Algorithms for Complex NonLinear Model
 Classification
 Objectives: 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 .
 Recap
 Introduction to Classification
 Examples of Classification
 Classification vs. Prediction
 Classification System
 Classification Process
 Classification Process—Model Construction
 Classification Process—Model Usage in Prediction
 Issues Regarding Classification and Prediction
 Data Preparation Issues
 Evaluating Classification Methods Issues
 Decision Tree
 Decision Tree—Dataset
 Classification Rules of Trees
 Overfitting in Classification
 Tips to Find the Final Tree Size
 Basic Algorithm for a Decision Tree
 Statistical Measure—Information Gain
 Calculating Information Gain—Example
 Calculating Information Gain for ContinuousValue Attributes
 Enhancing a Basic Tree Decision Trees in Data Mining
 Naive Bayes Classifier Model
 Features of Naive Bayes Classifier Model
 Bayesian Theorem
 Naive Bayes Classifier
 Bayesian Theorem
 Applying Naive Bayes Classifier—Example
 Naive Bayes Classifier—Advantages and Disadvantage
 Nearest Neighbor Classifiers
 Computing Distance and Determining Class
 Choosing the Value of K
 Scaling Issues in Nearest Neighbor Classification
 Support Vector Machines
 Advantages of Support Vector Machines
 Geometric Margin in SVMs
 Linear SVMs
 NonLinear SVMs
 Clustering & Association
 Objectives This lesson will help the learners to Explain clustering, describe clustering use cases and discuss clustering models. This lesson will also explain association rule mining, the parameters of interesting relationships, the strength measures of association rules and the Apriori algorithm
 Recap
 Introduction to Clustering
 Clustering vs. Classification
 Use Cases of Clustering
 Clustering Models
 Kmeans Clustering
 Kmeans Clustering Algorithm
 Pseudo Code of Kmeans
 Kmeans Clustering Using R
 Kmeans Clustering—Case Study
 Hierarchical Clustering
 Hierarchical Clustering Algorithms
 Requirements of Hierarchical Clustering Algorithms
 Agglomerative Clustering Process
 Hierarchical Clustering—Case Study
 DBSCAN Clustering
 Concepts of DBSCAN
 DBSCAN Clustering Algorithm
 DBSCAN Clustering—Case Study
 Association Rule Mining
 Application Areas of Association Rule Mining
 Parameters of Interesting Relationships
 Association Rules
 Association Rule Strength Measures
 Limitations of Support and Confidence
 Apriori Algorithm
 Apriori Algorithm—Example
 Applying Apriori Algorithm
 Step 1—Mine All Frequent Item Sets
 Algorithm to Find Frequent Item Set
 Finding Frequent Item Set—Example
 Ordering Items
 Candidate Generation
 Candidate Generation—Example
 Step 2—Generate Rules from Frequent Item Sets
 Generate Rules from Frequent Item Sets—Example
 Problems with Association Mining
Course Content
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