What you’ll learn
- Learn what is Data Science and how it is helping the modern world!
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What are the benefits of Data Science and Machine Learning
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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 |
5 STUDENTS ENROLLED