Contact for queries : What you’ll learn
• Learn what is Data Science and how it is helping the modern world!

• ### Able to Solve Data Science Related Problem with the Help of R Programming

• Why R is a Must Have for Data Science , AI and Machine Learning!
• Right Guidance of the Path if You want to be a Data Scientist + Data science Interview Preparation Guide
• How to switch career in Data Science?
• R Data Structure – Matrix, Array, Data Frame, Factor, List
• Work with R’s conditional statements, functions, and loops
• Systematically Explore data in R
• Data Science Package: Dplyr , GGPlot 2
• Index, slice, and Subset Data
• Get your data in and out of R – CSV, Excel, Database, Web, Text Data
• Data Visualization : plot different types of data & draw insights like: Line Chart, Bar Plot, Pie Chart, Histogram, Density Plot, Box Plot, 3D Plot, Mosaic Plot
• Data Manipulation – Apply function, mutate(), filter(), arrange (), summarise(), groupby(), date in R
• Statistics – A Must have for Data Sciecne
• Hypothesis Testing
• Have fun with real Life Data Sets

### Course Curriculum

 1. Meet Your Instructor 1. Meet Your Instructor 00:00:00 2. Course Curriculum Overview 00:00:00 2. INTRODUCTION TO DATA SCIENCE 1. Introduction to Business Analytics 00:00:00 3. Introduction to Machine Learning 00:00:00 5. Introduction To Data Scientist 00:00:00 7. How to switch your career into ML 00:00:00 9. How to switch your career into ML 00:00:00 3. Course Curriculum Overview 1. What We are Going to Discuss Over the Course 00:00:00 4. INTRODUCTION TO R 1. Introduction to R 00:00:00 3. Setting up R 00:00:00 5. R Programming 2. R Conditional Statement & Loop 00:00:00 4. R Programming – R Function 00:00:00 5. R Programming 00:00:00 6. R Programming – R Function #2 00:00:00 8. R Programming – R Function #3 00:00:00 6. R Data Structure 1. R Data Structure – Vector 00:00:00 4. Matrix, Array and Data Frame 00:00:00 8. A Deep Drive to R Data Frame 00:00:00 10. R Data Structure – Factor 00:00:00 13. R Data Structure – List 00:00:00 7. Import and Export in R 1. Import CSV Data in R 00:00:00 4. Import Text Data in R 00:00:00 7. Import Excel, Web Data in R 00:00:00 9. Export Data in R – Text 00:00:00 11. Export Data in R – CSV & Excel 00:00:00 Data 00:00:00 8. Data Manipulation 1. Data Manipulation – Apply Function 00:00:00 3. Data Manipulation – select 00:00:00 4. Data Manipulation – mutate 00:00:00 5. Data Manipulation – filter 00:00:00 6. Data Manipulation – arrange 00:00:00 8. Data Manipulation – Pipe Operator 00:00:00 10. Data Manipulation – group by 00:00:00 12. Data Manipulation – Date 00:00:00 9. Data Visualization 1. Introduction to Data Visualization & Scatter Plot 00:00:00 3. Data Visualization – mfrow 00:00:00 5. Data Visualization – Color 00:00:00 7. Data Visualization – pch 00:00:00 9. Data Visualization – Line Chart 00:00:00 11. Data Visualization – Bar Plot 00:00:00 13. Data Visualization – Pie Chart 00:00:00 15. Data Visualization – Histogram 00:00:00 17. Data Visualization – Density Plot 00:00:00 19. Data Visualization – Box Plot 00:00:00 21. Data Visualization – Mosaic Plot and Heat Map 00:00:00 23. Data Visualization – 3D Plot 00:00:00 25. Correlation Plot and Word Cloud 00:00:00 27. Data Visualization – ggplot2 Part 1 00:00:00 28. Data Visualization – ggplot2 Part 2 00:00:00 30. Data Visualization – ggplot2 Part 3 00:00:00 Data 00:00:00 10. Introduction To Statistics 1. Intro To Stat – Part 1 00:00:00 3. Intro To Stat – Part 2 00:00:00 5. Intro To Stat – Part 3 00:00:00 7. Intro To Stat – Part 4 00:00:00 9. Intro To Stat – Part 5 00:00:00 11. Intro To Stat – Part 6 00:00:00 12. Intro To Stat – Part 7 00:00:00 14. Intro To Stat – Part 8 00:00:00 16. Intro To Stat – Part 9 00:00:00 18. Intro To Stat – Part 10 00:00:00 20. Intro To Stat – Part 11 00:00:00 Data 00:00:00 11. HYPOTHESIS Testing -1 1. Hypothesys Testing – Part 1 00:00:00 3. Hypothesys Testing – Part 2 00:00:00 5. Hypothesys Testing – Part 3 00:00:00 6. Hypothesys Testing – Part 4 00:00:00 12. Hypothesis Testing in Practice 1. Hypothesys Testing in Practice – Part 1 00:00:00 3. Hypothesys Testing in Practice – Part 2 00:00:00 4. Hypothesys Testing in Practice – Part 3 00:00:00 6. Hypothesys Testing in Practice – Part 4 00:00:00 7. Hypothesys Testing in Practice – Part 5 00:00:00 9. Hypothesys Testing in Practice – Part 6 00:00:00 10. Chi Square -Part 1 00:00:00 12. Chi Square -Part 2 00:00:00 14. ANOVA – Part 1 00:00:00 16. ANOVA – Part 2 00:00:00 17. What we discussed in the chapter so far – Summary of the Chapter 00:00:00 13. Machine Learning Toolbox 1. Machine Learning Toolbox – Part 1 00:00:00 2. Machine Learning Toolbox – Part 2 00:00:00 14. Business Use Case Understaing 1. Business Case Understanding 00:00:00 15. Data Pre-Processing 1. Data Pre-Processing 1 00:00:00 3. Data Pre-Processing 2 00:00:00 5. Data Pre-Processing 3 00:00:00 7. Data Pre-Processing 4 00:00:00 8. Data Pre-Processing 5 00:00:00 10. Data Pre-Processing 6 00:00:00 11. Data Pre-Processing 7 00:00:00 Data 00:00:00 16. SUPERVISED LEARNING REGRESSION 1. Linear Regression 1 00:00:00 2. Linear Regression 2 00:00:00 3. Linear Regression 3 00:00:00 4. Linear Regression 4 00:00:00 5. Linear Regression 5 00:00:00 6. Linear Regression 6 00:00:00 7. Linear Regression 7 – Correlation Part 1 00:00:00 8. Linear Regression 7 – Correlation Part 2 00:00:00 9. Linear Regression 8 – Stepwise Regression 00:00:00 10. Linear Regression 9 – Stepwise Regression 00:00:00 11. Linear Regression 10 – Dummy Variable 00:00:00 12. Linear Regression 11 – Non Linear 00:00:00 12. Linear Regression 11 – Non Linearss 00:00:00 Data 00:00:00 17. Logistic Regression 1. Logistics Regression Intuition 00:00:00 2. R Code Implementation -Part1 00:00:00 3. R Code Implementation -Part2 00:00:00 5. Model Evaluation 00:00:00 7. Telecom Churn Case Study 00:00:00 9. Summary 00:00:00 Data 00:00:00 18. K-NN 1. K-NN Intuition 00:00:00 2. K-NN R Code Implementation 00:00:00 4. K-NN Case Study 00:00:00 Data 00:00:00 19. SVM 1. SVM – Intuition 00:00:00 2. SVM – R Code Implementation 00:00:00 4. SVM – Model Tuning 00:00:00 6. SVM – Telecom Case Study 00:00:00 8. SVM – Non Separable Case 00:00:00 9. SVM – Pros and Cons 00:00:00 20. Naive Bayes 1. Naive Bayes – Intuition 00:00:00 2. Naive Bayes – R Code Implementation 00:00:00 3. Naive Bayes – Case Study 00:00:00 Data 00:00:00 21. Decision Tree 1. Decision Tree Intuition 00:00:00 2. Decision Tree -How it works 00:00:00 3. Decision Tree – R Code Implementation 00:00:00 Data 00:00:00 22. Random Forest 1. Random Forest – Intuition 00:00:00 2. Random Forest -R Code Implementation 00:00:00 3. Random Forest – Case Study 00:00:00 Data 00:00:00 23. Capstone Project - Titanic Survival 1. Capstone Project -Introduction 00:00:00 2. Capstone Project – Data Understanding 00:00:00 3. Capstone Project – Lazy Predictor 00:00:00 4. Capstone Project – Data Preparation 00:00:00 5. Capstone Project – Data Exploration 00:00:00 6. Capstone Project – Feature Engineering 00:00:00 24. K-Mean Clustering 1. Unsupervised Learning Introduction 00:00:00 2. K-Mean Clustering Intuition 00:00:00 3. K-Mean Clustering R Code Implementation 00:00:00 4. K-Mean Clustering Case Study 00:00:00 5.1 All Codes K-Mean Clustering.zip 00:00:00 25. Hierarchical Clustering 1. Hierarchical Clustering Intuition 00:00:00 2. Hierarchical Clustering R Code Implementation 00:00:00 3. Hierarchical Clustering Case Study 00:00:00 Data 00:00:00 26. DBScan Clustering 1. DBScan Clustering -Intuition and R Code 00:00:00 2. DBScan Clustering – Case Study 00:00:00 Data 00:00:00 27. Principal Component Analysis (PCA) 1. PCA – Intuition 00:00:00 2. PCA – R Code Implementation 00:00:00 3. PCA – Case Study 00:00:00 Data 00:00:00 28. Association Rule Mining 1. Association Rule Mining -Introduction 00:00:00 2. Association Rule Mining -R Code Implementation 00:00:00 3. Association Rule Mining – Pre-Processing 00:00:00 4. Association Rule Mining – Case Study 00:00:00 Data 00:00:00 29. Capstone Project - Big Mart Sell 1. Big Mart Sale – Data Structure 00:00:00 2. Big Mart Sale – Univariate Analysis 00:00:00 3. Big Mart Sale – Bi-Variate Analysis 00:00:00 4. Big Mart Sale – Fetature Engineering 00:00:00 5. Big Mart Sale – Pre-Processing 00:00:00 6. Big Mart Sale – Model Building & Evaluation 00:00:00 Data 00:00:00 30. Model Deployment 1. Model Deployment – Workflow 00:00:00 2. Model Deployment – Pre Requisite 00:00:00 3. Model Deployment – Steps To Follow 00:00:00 4. Model Deployment – Azure ML DEMO 00:00:00
• FREE
• 10 Days
5 STUDENTS ENROLLED
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