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What you’ll learn
  • Learn what is Data Science and how it is helping the modern world!
  • What are the benefits of Data Science and Machine Learning

  • 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
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  • 10 Days
5 STUDENTS ENROLLED

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