Statistics For Data Science Course in English > Descriptive Statistics

Assignment : Descriptive Statistics

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4.7

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Reviews

A
A Ganga
5

Very happy

A
Abhishek Srivastava
5

Awesome

V
Vrushali Kandesar
5

This course is really nice, just have one question in empirical rule explanation , SD deviation example trainer is saying mean however mean (20+30+40+50+60+70/6) value is different kindly confirm than

P
Prabhat Yadav
5

Superb and amazing 😍🤩 enjoyable experience.

K
Kesavaraman Balakrishnan
5

wow... Teaching and voice is good

P
Prashant Dadhania
5

Good Course

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Course Content

  1. Introduction to Statistics For Data Analysis and Data Science
    5m 35s

  1. Fundamentals of Statistics
    15m 30s

  1. Basics of Descriptive Statistics
    12m 55s
  2. Measures of Frequency
    10m 43s
  3. Measures of Central Tendency - Mean
    9m 6s
  4. Measures of Central Tendency - Median
    4m 47s
  5. Measures of Central Tendency - Mode
    5m 37s
  6. Measures of Variability - Mean Absolute Deviation
    7m 33s
  7. Measures of Variability - Variance
    7m 53s
  8. Measures of Variability - Standard Deviation
    10m 20s
  9. Measures of Position
    16m 6s
  10. Measures of Shape - Skewness
    10m 15s
  11. Measures of Shape - Kurtosis, Box and Whisker Plot
    8m 31s
  12. Assignment : Descriptive Statistics
    3m 51s

  1. Basics of Inferential Statistics
    4m 53s
  2. Introduction to Probability
    17m 12s
  3. Basics of Probability
    22m 9s
  4. Probability Distribution
    7m 20s
  5. Discrete Probability Distribution
    18m 50s
  6. Continuous Probability Distribution
    12m 35s
  7. Uniform Distribution
    4m 16s
  8. Normal Distribution
    9m 55s
  9. Standard Normal Distribution
    9m 25s
  10. Log Normal Distribution
    3m 50s
  11. Exponential Distribution
    5m 17s
  12. Methods to Detect Outliers
    9m 18s
  13. Methods to Treat Outliers in Python
    14m 55s
  14. Feature Scaling - Normalization V/S Standardization
    10m 34s
  15. Sampling Methods
    12m 11s
  16. Sampling Distribution
    18m 20s
  17. Central Limit Theorem
    13m 50s
  18. Assignment : Inferential Statistics
    7m 3s

  1. Hypothesis Testing in Data Science
    7m 17s
  2. Null and Alternative Hypothesis
    9m
  3. Making a Decision - Reject or Fail to Reject Null Hypothesis
    9m 9s
  4. Type I & Type II Errors
    12m 55s
  5. Covariance and Correlation Coefficients
    11m 34s
  6. Types of Correlation Coefficients
    15m 45s
  7. Assignment : Hypothesis Testing
    3m 55s

  1. Types of Statistical Tests
    3m 7s
  2. Z-Test: Critical Value Method
    12m 57s
  3. Examples of Z-Test: Critical Value Method
    9m 48s
  4. Z-Test: P-Value Method
    9m 53s
  5. Examples of Z-Test: P-Value Method
    8m 55s
  6. T-Test: One Sample Mean Test
    15m 2s
  7. T-Test: Paired Two Sample Mean Test
    13m 58s
  8. T-Test : Unpaired Two-Sample Mean Test
    16m 53s
  9. Two Sample Proportion Test
    14m 37s
  10. Chi-Square Test: Independence Test
    13m 9s
  11. Chi-Square Test: Goodness of Fit
    6m 28s
  12. ANOVA Test or F-Test
    25m 26s
  13. Assignment : Types of Hypothesis Testing
    5m 40s

  1. Basics of Linear Regression Analysis
    24m 58s
  2. Assumptions of Linear Regression
    10m 7s
  3. Multiple Linear Regression Analysis
    14m 4s

  1. Course Summary
    4m 21s

  1. Interview Questions
    15m 18s

  1. Career Guidelines
    2m 10s
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