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Statistics with Python (In Progress)
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Quiz - Prerequisites
Untitled lesson
Module 1 Slides
1. Outline of topics in this course
2. Why learn statistics?
3. Examples of use of statistics
4. How exactly is statistics use?
5. Data literacy and its importance
6. What you will get out of this course
Module 2 Slides
1. Learning objective
2. Example of environmental impact assessment
3. Statistical studies: Observational and Experimental
4. Observational study
5. Experimental study
6. Cohort study
7. Institutional data and retrospective cohort study
8. Cross-sectional study
9. Ecological study
10. Summary: Statistical studies
11. Data collection: population and sample
12. Review of key concepts in data collection
Quiz (Module 2)
Module 3.1 Slides
Module 3 Google Colab Notebook
1. Module objective and motivation
2. Three things to consider for a graphical summary
3. Choosing appropriate graphical display
4. Displaying categorical data - bar plot
5. Displaying categorical data- comparative bar plot
6. Solutions: Tech layoff data
Quiz (Barplot): Did you notice the difference in two barplots?
7. Why is histogram so important?
8. Understanding histogram interactively
9. Barplot vs histogram
10. Clarifying the histogram of nosie-level example
11. Frequency, relative frequency and density histograms
12. Clearly understanding density histogram (TO BE POSTED)
13. Quick recap of histogram
14. Exercise: Draw and interpret a histogram
15.1 More on Density Histogram
15.2 Shape of a histogram
15.3 Concept of density (interactive app)
16.1 Scatterplot intuition through examples
16.2 What is a scatterplot
16.3 Scatterplot Matrix
17. Project on Scatterplot
18. Time Series plot (includes Exercise)
Module 3.2 Slides
Module 3 Google Colab Notebook
1. Module Intro (topics, goals)
2. Building intuition of location and spread of a distribution
3. Variability and its measures
4. Building intuition to measure variability
5. How to measure total variability
6. What central value to use?
7. How to calculate variance
8. Standard Deviation (SD)
9. Range as a messure of variability
10. Quantiles (Quartile, Percentile)
11. Five-number Summary (Box plot)
12. How does outlier affect measures of location
13. Use cases for Median and IQR
14. Question on Outlier removal (and my response)
15. Measures of relative standing (Z-score)
16. Z-score: Why do we need it
17. How Z-score is used in industry
Module 3.3 Slides
1. Understanding bivariate relationship
2. Modeling data using a linear equation
3. Measuring linear relationship
4. Quick summary so far
5. Industry perspective
6. Issues with correlated variables
Module 4.1 Slides
1. Module introduction and topics outline
2. Perception of Probability in our lives
3. Example: Identifying Misinformation
4. Interpreting probability: Law of large numbers
5. Calculating Probability
6. Events and related concepts
7. When outcomes are not equally likely
8. Probability for Complex Events
9. Complement, Intersection and Union of Events
10. Mutually Exclusive events
11. Independent events
12. Summary
13. Exercise: Sedan and SUV warranty
14.1 Conditional Probability Intuition
14.2 Conditional Probability: Live Questions and Answers
14.3 Conditional Probability: Things to remember
14.4 Conditional Probability: Applications and Examples
14.5 Conditional Probability and Independent Events
15. Use of Probability in the Industry
16. Hands On Exercise
Don't lie to me - its not random
I am about to fit
Normal distribution is not so normal- everyone lied :-(
Okay, I am getting it now
Love it or hate it: p-value is everywhere
Its so complicated!
I now understand it - p-value is easy peasy !!!
Why did everyone make it so complicated ??
p-value is the easiest thing to understand-- statistics is beautiful! OMG
Now I know why everyone uses regression
Simple Linear Regression
Multiple Linear Regression
Binary Logistic Regression
Once you're done here, move on to Machine Learning 360 course
START HERE
Quiz - Prerequisites
Untitled lesson
Module 1: Why Statistics?
Module 1 Slides
1. Outline of topics in this course
Preview
2. Why learn statistics?
Preview
3. Examples of use of statistics
Preview
4. How exactly is statistics use?
Preview
5. Data literacy and its importance
Preview
6. What you will get out of this course
Preview
Module 2: Getting Data
Module 2 Slides
1. Learning objective
2. Example of environmental impact assessment
3. Statistical studies: Observational and Experimental
4. Observational study
5. Experimental study
6. Cohort study
7. Institutional data and retrospective cohort study
8. Cross-sectional study
9. Ecological study
10. Summary: Statistical studies
11. Data collection: population and sample
12. Review of key concepts in data collection
Quiz (Module 2)
Module 3.1: Describing Data Distribution: Graphical Methods
Module 3.1 Slides
Module 3 Google Colab Notebook
1. Module objective and motivation
2. Three things to consider for a graphical summary
3. Choosing appropriate graphical display
4. Displaying categorical data - bar plot
5. Displaying categorical data- comparative bar plot
6. Solutions: Tech layoff data
Quiz (Barplot): Did you notice the difference in two barplots?
7. Why is histogram so important?
8. Understanding histogram interactively
9. Barplot vs histogram
10. Clarifying the histogram of nosie-level example
11. Frequency, relative frequency and density histograms
12. Clearly understanding density histogram (TO BE POSTED)
13. Quick recap of histogram
14. Exercise: Draw and interpret a histogram
15.1 More on Density Histogram
15.2 Shape of a histogram
15.3 Concept of density (interactive app)
16.1 Scatterplot intuition through examples
16.2 What is a scatterplot
16.3 Scatterplot Matrix
17. Project on Scatterplot
18. Time Series plot (includes Exercise)
Module 3.2: Describing Data Distribution - Numerical Methods
Module 3.2 Slides
Module 3 Google Colab Notebook
1. Module Intro (topics, goals)
2. Building intuition of location and spread of a distribution
3. Variability and its measures
4. Building intuition to measure variability
5. How to measure total variability
6. What central value to use?
7. How to calculate variance
8. Standard Deviation (SD)
9. Range as a messure of variability
10. Quantiles (Quartile, Percentile)
11. Five-number Summary (Box plot)
12. How does outlier affect measures of location
13. Use cases for Median and IQR
14. Question on Outlier removal (and my response)
15. Measures of relative standing (Z-score)
16. Z-score: Why do we need it
17. How Z-score is used in industry
Module 3.3 Relationship between two Variables
Module 3.3 Slides
1. Understanding bivariate relationship
2. Modeling data using a linear equation
3. Measuring linear relationship
4. Quick summary so far
5. Industry perspective
6. Issues with correlated variables
Module 4.1 Crying with Probability
Module 4.1 Slides
1. Module introduction and topics outline
2. Perception of Probability in our lives
3. Example: Identifying Misinformation
4. Interpreting probability: Law of large numbers
5. Calculating Probability
6. Events and related concepts
7. When outcomes are not equally likely
8. Probability for Complex Events
9. Complement, Intersection and Union of Events
10. Mutually Exclusive events
11. Independent events
12. Summary
13. Exercise: Sedan and SUV warranty
14.1 Conditional Probability Intuition
14.2 Conditional Probability: Live Questions and Answers
14.3 Conditional Probability: Things to remember
14.4 Conditional Probability: Applications and Examples
14.5 Conditional Probability and Independent Events
15. Use of Probability in the Industry
16. Hands On Exercise
Module 4.2 OMG - Random Variables! (Coming up next)
Don't lie to me - its not random
Pulling my hair - Probability Distribution (Not yet started)
I am about to fit
Normal distribution is not so normal- everyone lied :-(
Okay, I am getting it now
Getting totally lost with Hypothesis testing (Not yet started)
Love it or hate it: p-value is everywhere
Its so complicated!
I now understand it - p-value is easy peasy !!!
Why did everyone make it so complicated ??
p-value is the easiest thing to understand-- statistics is beautiful! OMG
Relationship between Many Variables (Not yet started)
Now I know why everyone uses regression
Simple Linear Regression
Multiple Linear Regression
Binary Logistic Regression
Next Course --> Machine Learning
Once you're done here, move on to Machine Learning 360 course
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