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Module 1: Why Statistics?
7 Lessons -
Module 2: Getting Data
14 Lessons-
StartModule 2 Slides
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Start1. Learning objective
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Start2. Example of environmental impact assessment
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Start3. Statistical studies: Observational and Experimental
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Start4. Observational study
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Start5. Experimental study
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Start6. Cohort study
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Start7. Institutional data and retrospective cohort study
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Start8. Cross-sectional study
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Start9. Ecological study
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Start10. Summary: Statistical studies
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Start11. Data collection: population and sample
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Start12. Review of key concepts in data collection
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StartQuiz (Module 2)
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Module 3.1: Describing Data Distribution: Graphical Methods
25 Lessons-
StartModule 3.1 Slides
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StartModule 3 Google Colab Notebook
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Start1. Module objective and motivation
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Start2. Three things to consider for a graphical summary
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Start3. Choosing appropriate graphical display
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Start4. Displaying categorical data - bar plot
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Start5. Displaying categorical data- comparative bar plot
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Start6. Solutions: Tech layoff data
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StartQuiz (Barplot): Did you notice the difference in two barplots?
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Start7. Why is histogram so important?
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Start8. Understanding histogram interactively
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Start9. Barplot vs histogram
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Start10. Clarifying the histogram of nosie-level example
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Start11. Frequency, relative frequency and density histograms
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Start12. Clearly understanding density histogram (TO BE POSTED)
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Start13. Quick recap of histogram
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Start14. Exercise: Draw and interpret a histogram
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Start15.1 More on Density Histogram
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Start15.2 Shape of a histogram
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Start15.3 Concept of density (interactive app)
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Start16.1 Scatterplot intuition through examples
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Start16.2 What is a scatterplot
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Start16.3 Scatterplot Matrix
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Start17. Project on Scatterplot
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Start18. Time Series plot (includes Exercise)
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Module 3.2: Describing Data Distribution - Numerical Methods
19 Lessons-
StartModule 3.2 Slides
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StartModule 3 Google Colab Notebook
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Start1. Module Intro (topics, goals)
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Start2. Building intuition of location and spread of a distribution
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Start3. Variability and its measures
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Start4. Building intuition to measure variability
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Start5. How to measure total variability
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Start6. What central value to use?
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Start7. How to calculate variance
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Start8. Standard Deviation (SD)
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Start9. Range as a messure of variability
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Start10. Quantiles (Quartile, Percentile)
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Start11. Five-number Summary (Box plot)
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Start12. How does outlier affect measures of location
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Start13. Use cases for Median and IQR
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Start14. Question on Outlier removal (and my response)
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Start15. Measures of relative standing (Z-score)
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Start16. Z-score: Why do we need it
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Start17. How Z-score is used in industry
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Module 3.3 Relationship between two Variables
7 Lessons -
Module 4.1 Crying with Probability
21 Lessons-
StartModule 4.1 Slides
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Start1. Module introduction and topics outline
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Start2. Perception of Probability in our lives
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Start3. Example: Identifying Misinformation
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Start4. Interpreting probability: Law of large numbers
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Start5. Calculating Probability
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Start6. Events and related concepts
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Start7. When outcomes are not equally likely
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Start8. Probability for Complex Events
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Start9. Complement, Intersection and Union of Events
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Start10. Mutually Exclusive events
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Start11. Independent events
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Start12. Summary
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Start13. Exercise: Sedan and SUV warranty
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Start14.1 Conditional Probability Intuition
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Start14.2 Conditional Probability: Live Questions and Answers
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Start14.3 Conditional Probability: Things to remember
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Start14.4 Conditional Probability: Applications and Examples
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Start14.5 Conditional Probability and Independent Events
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Start15. Use of Probability in the Industry
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Start16. Hands On Exercise
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Module 4.2 OMG - Random Variables!
11 Lessons-
StartModule 4 Colab Notebook
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StartModule 4.2 slides
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Start1. Motivation for this module
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Start2. Don't lie to me - its not random
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Start3. Key difference between ordinary and random variables
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Start4. Multiplication principle
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Start5. Connection between random variable and probability
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Start6. Formal definition of random variable explained
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Start7. Examples of functions
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Start8. Putting it all together plus real world examples
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Start9. Probability distribution of a random variable
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Module 4.3 Discrete Probability Distribution
9 Lessons-
StartModule 4 Colab Notebook (in progress)
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StartModule 4.3 slides
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Start1. Module intro - outline of topics
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Start2. Discrete and Continuous random variables
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Start3. Example scenarios where we want to calculate probability
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Start4. Emergence of probability distribution
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Start5. Cumulative probability and probability mass function
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Start6. Example: Probability calculation
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Start7. Example: Guessing in MCQ exam
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Getting totally lost with Hypothesis testing (Not yet started)
5 Lessons -
Next Course --> Machine Learning
1 Lesson-
StartOnce you're done here, move on to Machine Learning 360 course
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This course is not open for enrollment
Statistics with Python (In Progress)
Discover applied statistics like never before
This course is part of a bundle. Please visit the link below for registration and to learn more
https://www.dataskool.org/courses/pystats
Course at a Glance
- Learn statistics hands-on with examples without overloading your brain
- Pre-recorded video lectures in Bangla language
- Live office hours with the instructor for course/career counseling
- Direct help from the instructor via private Facebook group
- Unlimited access to videos and all course materials
- Access to all future updates
Course Curriculum
About the Instructor
Enayetur Raheem, Ph.D.
is an accomplished data scientist with over 20 years of analytic experience in academia and industry combined
He has taught graduate level statistics courses at Dhaka University and several universities in the US prior to transitioning to industry
Currently he is a Principal Data Scientist at an AI startup in healthcare domain in US
He is a lifelong learner and passionate about teaching online
He is the founder and principal instructor at dataskool.org
Testimonials from Students
The course materials are very helpful and well organized. I have learned a lot about statistical analysis with R for research purposes. It is very effective for those who want to learn statistical analysis with R in the Bengali language like me.
-- Student from Statistical Analysis with R for Reproducible Research course
Reviews
3.3
Top Rated
Hasibul Karim
Intuitive
The course content is very intuitive and resource. People who have some prior understanding of statistics can enjoy a lot, because the way some concepts are explained is unique. Waiting for the remaining sections of the course.
OMAR FAROQUE
Very effective
I have successfully completed all the uploaded lectures and waiting for the in progress lectures. I think, not only I but also others who is going to do this course will be thankful if you provide a shareable certificate after complete the course.
Md Aminul islam
Certificate
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