Handle Missing Data - Part 2

1.1: Importance of Math and Stats in Data Science Career
1.2: How Is This Course Different from Other Courses on the Internet?
1.3: What Support Do You Provide If I Have Questions?
1.4: Who Should Take This Course?
1.5: Pre-requisites
1.6: System Requirements
1.7: Course Overview

2.1: Join Discord for doubt clearing support & networking
2.2: Setting Video Resolution and Video Speed Globally

3.1: Types of Data
3.2: Pie Chart and Bar Chart
3.3: Quiz
3.4: Histograms and Line Chart
3.5: Quiz
3.6: Scatter and Bubble Plot
3.7: Quiz
3.8: Univariate vs. Bivariate vs. Multivariate Analysis
3.9: Chapter Summary

4.1: Introduction and Benefits
4.2: Basic Operations
4.3: Matrix Operations
4.4: Slicing, Stacking
4.5: Quiz
4.6: Exercise

5.1: Pandas Introduction and Installation
5.2: Dataframe Basics
5.3: Read, Write Excel and CSV Files
5.4: Handle Missing Data - Part 1
5.5: Handle Missing Data - Part 2
5.6: Grouping Data
5.7: Data Concatenation and Merging
5.8: Data Visualization with Matplotlib and Seaborn

6.1: Descriptive vs. Inferential Statistics
6.2: Measures of Central Tendency: Mean, Median, Mode
6.3: Percentile
6.4: Analysis: Shoe Sales (Using Mean, Median, Percentile)
6.5: Quiz
6.6: Exercise
6.7: Measures of Dispersion: Range, IQR
6.8: Box or Whisker Plot
6.9: Outlier Treatment Using IQR and Box Plot
6.10: Quiz
6.11: Exercise
6.12: Measures of Dispersion: Variance and Standard Deviation
6.13: Analysis: Stock Returns Volatility (Using Variance and Std Dev)
6.14: Correlation
6.15: Correlation vs Causation
6.16: Quiz
6.17: Exercise
6.18: Chapter Summary

7.1: Probability Basics
7.2: Quiz
7.3: Addition and Multiplication Rule
7.4: Quiz
7.5: Conditional Probability and Bayes Theorem
7.6: Quiz
7.7: Chapter Summary

8.1: What Is a Distribution?
8.2: Skewness
8.3: Normal Distribution
8.4: Detect Outliers Using Normal Distribution
8.5: Quiz
8.6: Exercise
8.7: Z Score
8.8: Standard Normal Distribution (SND)
8.9: I Need a Favour
8.10: Quiz
8.11: Exercise
8.12: Chapter Summary

9.1: Chapter Overview
9.2: Bashneer Grower’s Bank Venture
9.3: AtliQo Credit Card Launch: Problem Statement
9.4: Project Kick-off Meeting
9.5: Project Management Using Kanban
9.6: Tasks Breakdown and JIRA Board Setup
9.7: Meeting: Tony, Peter, Natasha

10.1: Data Validation Of Acquired Data
10.2: Data Understanding, MySQL Setup
10.3: Data Import in Jupyter Notebook
10.4: Data Cleaning: Handle NULL Values (Annual Income)
10.5: Data Cleaning: Treat Outliers (Annual Income)
10.6: Data Visualization: Annual Income
10.7: Exercise: Treat Outliers in Age Column
10.8: Exercise Solution: Treat Outliers in Age Column
10.9: Data Visualization: Age, Gender, Location
10.10: Peter’s Nightmare
10.11: Data Cleaning: Credit Score Table - Part 1
10.12: Data Cleaning: Credit Score Table - Part 2
10.13: Correlation among Credit Profile Variables
10.14: Exercise: Handle NULL Values in Transactions Table
10.15: Exercise Solution: Handle NULL Values in Transactions Table
10.16: Peter’s Confusion: IQR or Std Dev?
10.17: Data Cleaning: Treat Outliers using IQR (Transaction Amount)
10.18: Data Visualization: Transactions Table
10.19: Finalize the Target Group
10.20: Phase 1 Feedback Meeting With Stakeholders
10.21: Get Ready For Phase 2

11.1: Random Sampling & Sample Bias
11.2: The Law of Large Numbers
11.3: Central Limit Theorem, Sampling Distribution
11.4: Case Study: Solar Panels
11.5: Standard Error
11.6: Quiz
11.7: Z Score Table (Z-Table)
11.8: Quiz
11.9: Confidence Interval
11.10: Confidence Interval: Estimate Car Miles
11.11: Exercise
11.12: Chapter Summary

12.1: Null vs Alternate Hypothesis
12.2: Z Test, Rejection Region
12.3: Housing Inflation Test: Rejection Region
12.4: Quiz
12.5: Exercise
12.6: p-Value
12.7: Housing Inflation Test: p-Value
12.8: Quiz
12.9: Exercise
12.10: One-Tailed vs Two-Tailed Test
12.11: Type 1 and Type 2 Errors
12.12: Quiz
12.13: Statistical Power & Effect Size
12.14: A/B Testing
12.15: A/B Testing Using Z Test
12.16: A/B Testing: Drug Trial
12.17: Quiz
12.18: Exercise
12.19: Chapter Summary

13.1: Campaign Planning
13.2: Hypothesis Testing and Decision Making
13.3: Final Stakeholder Meeting

14.1: T-test and Student’s t-distribution
14.2: Case Study - Exam Score
14.3: Chi-squared Distribution
14.4: Chi-squared Test of Goodness of Fit
14.5: Chi-squared Test of Independence

15.1: Final Quiz

16.1: Interview Questions

17.1: I’ve Finished The Course. What is the Next Step Now?

Handle Missing Data - Part 2

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Handle Missing Data - Part 2

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One way of treating NA on invalid values is to replace them with an appropriate value. In this lecture, we will cover pandas replace function that allows you to do this

Contents

1: Welcome to Math and Statistics Experience

1.1: Importance of Math and Stats in Data Science Career

1.2: How Is This Course Different from Other Courses on the Internet?

1.3: What Support Do You Provide If I Have Questions?

1.4: Who Should Take This Course?

1.5: Pre-requisites

1.6: System Requirements

1.7: Course Overview

2: Enhance your Learning Experience

2.1: Join Discord for doubt clearing support & networking

2.2: Setting Video Resolution and Video Speed Globally

3: Data & Visualization Basics

3.1: Types of Data

3.2: Pie Chart and Bar Chart

3.3: Quiz

3.4: Histograms and Line Chart

3.5: Quiz

3.6: Scatter and Bubble Plot

3.7: Quiz

3.8: Univariate vs. Bivariate vs. Multivariate Analysis

3.9: Chapter Summary

4: NumPy New

4.1: Introduction and Benefits

4.2: Basic Operations

4.3: Matrix Operations

4.4: Slicing, Stacking

4.5: Quiz

4.6: Exercise

5: Pandas, Matplotlib and Seaborn Basics

5.1: Pandas Introduction and Installation

5.2: Dataframe Basics

5.3: Read, Write Excel and CSV Files

5.4: Handle Missing Data - Part 1

5.5: Handle Missing Data - Part 2

5.6: Grouping Data

5.7: Data Concatenation and Merging

5.8: Data Visualization with Matplotlib and Seaborn

6: Measures Of Central Tendency and Dispersion

6.1: Descriptive vs. Inferential Statistics

6.2: Measures of Central Tendency: Mean, Median, Mode

6.3: Percentile

6.4: Analysis: Shoe Sales (Using Mean, Median, Percentile)

6.5: Quiz

6.6: Exercise

6.7: Measures of Dispersion: Range, IQR

6.8: Box or Whisker Plot

6.9: Outlier Treatment Using IQR and Box Plot

6.10: Quiz

6.11: Exercise

6.12: Measures of Dispersion: Variance and Standard Deviation

6.13: Analysis: Stock Returns Volatility (Using Variance and Std Dev)

6.14: Correlation

6.15: Correlation vs Causation

6.16: Quiz

6.17: Exercise

6.18: Chapter Summary

7: Probability Theory

7.1: Probability Basics

7.2: Quiz

7.3: Addition and Multiplication Rule

7.4: Quiz

7.5: Conditional Probability and Bayes Theorem

7.6: Quiz

7.7: Chapter Summary

8: Distributions

8.1: What Is a Distribution?

8.2: Skewness

8.3: Normal Distribution

8.4: Detect Outliers Using Normal Distribution

8.5: Quiz

8.6: Exercise

8.7: Z Score

8.8: Standard Normal Distribution (SND)

8.9: I Need a Favour

8.10: Quiz

8.11: Exercise

8.12: Chapter Summary

9: AtliQo Bank Project

9.1: Chapter Overview