Data Pre-processing and EDA

What you'll be able to do

• What is Data Preprocessing and Why is it needed?
• Understand what is EDA and how to approach
• Impute missing values with multiple approaches
• Understand Outlier Detection Algorithms
• Perform statistical significance tests and interpret
• Gain knowledge of Feature encoding and scaling techniques
• Create insightful plots for data analysis
• End to end EDA for Regression and Classification

1. 01Introduction to Univariate Analysis8 lessons · 28 min

   1. Download Resources1:00
   2. How to Get Queries Resolved1:00
   3. Measures of Central Tendency7:24
   4. When to Use Geometric and Harmonic Means8:42
   5. Measures of Dispersion - Part 15:49
   6. Measures of Dispersion - Part 23:35
   7. AssignmentAssignment
   8. Assignment SolutionAssignment
2. 02Significance Tests8 lessons · 57 min

   1. Chi Squared Test Theory and MathsPreview17:47
   2. Chi Square Test and Odds Ratio DemoPreview9:10
   3. ANOVA (Maths)Preview12:41
   4. ANOVA Concept (Intuition)Preview10:07
   5. ANOVA DemoPreview6:31
   6. Course Review1:00
   7. AssignmentAssignment
   8. Assignment SolutionAssignment
3. 03Data Imputation Methods15 lessons · 58 min

   1. Representing Missing Values2:38
   2. Types of Missing Values4:02
   3. Identifying Missing Values4:38
   4. Visualize Missing Values with missingno4:06
   5. When to Drop Rows and Columns3:54
   6. Approaches to Filling in Missing Data7:07
   7. Implementing Imputation7:49
   8. Interpolation6:14
   9. MICE Predictions - Part 14:28
   10. MICE Predictions - Part 22:28
   11. MICE Predictions - Part 34:59
   12. Validating Missing Value Imputations4:34
   13. Course Review1:00
   14. AssignmentAssignment
   15. Assignment SolutionAssignment
4. 04Outlier Detection Approaches18 lessons · 1 h 21 min

   1. What are Outliers and Why They Matter6:39
   2. Detecting Outliers with Box and Whiskers Plot8:01
   3. Detecting Outliers with Z Score4:22
   4. Ways to Treat Outliiers3:05
   5. Mahalanobis Distance - Part 11:20
   6. Mahalanobis Distance - Part 28:59
   7. Cooks Distance9:19
   8. Isolation Forest Algorithm - Part 16:17
   9. Isolation Forest Algorithm - Part 210:08
   10. Isolation Forest Algorithm - Part 33:49
   11. LOF Part 1 Introduction1:36
   12. LOF Part 2 Problem with simple density5:15
   13. LOF Part 3 K Distance and Local Reachability Density3:35
   14. LOF Part 4 Main Concept2:52
   15. LOF Part 53:26
   16. Local Outlier Factor Algorithm - Part 61:50
   17. AssignmentAssignment
   18. Assignment SolutionAssignment
5. 05Feature Encoding Approaches15 lessons · 1 h 5 min

   1. Label Encoding3:20
   2. Need for Feature Encoding3:44
   3. Label and Ordinal9:04
   4. One Hot Encoding6:25
   5. Frequency Encoding3:18
   6. Target Encoding - Part 14:42
   7. Target Encoding - Part 25:32
   8. Blending Method- Part 110:21
   9. Blending Method- Part 22:44
   10. Leave One Out Encoding1:38
   11. Weights of Evidence and Information Value6:32
   12. Weights of Evidence for Continuous Dependent7:07
   13. Course Review1:00
   14. AssignmentAssignment
   15. Assignment SolutionAssignment
6. 06Feature Transformations5 lessons · 10 min

   1. Need For scaling2:37
   2. Standardardization and Normalization4:18
   3. Robust Scaling3:05
   4. AssignmentAssignment
   5. Assignment SolutionAssignment
7. 07Plotting for Data Analysis23 lessons · 1 h 32 min

   1. Getting started with MatplotlibPreview1:11
   2. Making your first plot- Part 1Preview3:11
   3. Color, Marker and Style- Part 2Preview3:18
   4. Controlling plot components- Part 3Preview1:38
   5. Plotting two sets of points- Part 42:06
   6. Axis Ticks Positions- Part 54:07
   7. Anatomy of a Matplotlib Plot- Part 69:25
   8. Annotations- Part 72:56
   9. Analysing relationships between numeric variablesPreview11:14
   10. Analyzing Distributions Histogram- Part 1Preview4:39
   11. Bar ChartsPreview5:26
   12. Analysing Distributions Boxplots- Part 2Preview2:42
   13. Numeric vs Cat Overview- Part 15:09
   14. Numeric vs Cat Multi Box Plots- Part 21:21
   15. Numeric vs Cat Bubble Plots- Part 31:18
   16. Numeric vs Cat Pairs and Trellis- Part 46:33
   17. Categorical vs Categorical2:32
   18. How is Analyzing Time Series different5:02
   19. Line PlotPreview2:19
   20. Dual Axis Time Series PlotPreview4:32
   21. Interpreting ACF and PACF PlotsPreview11:25
   22. AssignmentAssignment
   23. Assignment SolutionAssignment
8. 08Project and Assignment7 lessons · 37 min

   1. EDA Project - Classification Part - 110:07
   2. EDA Project - Classification Part - 25:31
   3. EDA Project - Classification Part - 39:29
   4. EDA Project - Classification Part - 48:00
   5. EDA Project - Classification Part - 53:55
   6. AssignmentAssignment
   7. Assignment SolutionAssignment

What you need

• Basics of Python
• Foundational knowledge of Data Science
• High school maths

Who this course is for

• Data Science Aspirants
• Data Science Professionals
• Software/Data engineers interested in quantitative analysis
• Professionals working with large datasets
• Data analysts, economists, researchers

Your instructor

Prabhakaran S

Principal Data Scientist

My name is Selva, and I am super excited to mentor you on this course!

I head the Data Science team for a global Fortune 500 company and over the last 10 years of my data science experience I've deployed 20+ global products. I'm also the Founder and Chief Author of Machine Learning Plus, which has over 4M annual readers.

I specialize in covering the in-depth intuition and maths of any concept or algorithm. And based on my existing student requests, I've put up the series of courses and projects with detailed explanations, just like on-the-job experience. Hope you love it!

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