Statistical Thinking for Data Science and Analytics: DS101X
Course Syllabus
Week 1: Introduction to Data Science
- Introduction to Data Science
- What is Data Science?
- What questions can Data Science answer?
- Why is there an explosion of data?
- What role does data visualization play in Data Science?
- How did you become interested in Data Science?
- What do you predict will happen in Data Science in 5 years?
- What are the most important skills for a Data Scientist?
- What should a non-Data Scientist know about Data Science?
- What should a non-Data Scientist know about Data Science?
Week 2: Statistics and Probability I
- Statistical Thinking for Data Science
- Numerical Data 1 Simple Visualization and Summaries
- Numerical Data 2 Simple Visualization and Summaries
- Numerical Data 3 Association
- Data Collection - Sampling
- Introduction to Probability
- Statistical Inference - Confidence Intervals
- Statistical Inference - Significance tests
- Status of Current Observational Health Studies
- Statistical Terms Explained
- Unknown Characteristics of Observational Health Studies
- Lessons Learnt from OMOP Experiments
- P-value Calibration
- Concluding Remarks
Week 3: Statistics and Probability II
- Conditional Probability
- Bayes' Formula
- Studying Association: Two-way Table
- Studying Association: Chi-square Test of Independence
- Studying Association: One-way Analysis of Variance
- Regression Analysis 1 and 2
- Regression Analysis 3 and 4
- Regression Analysis 4 and Concluding Remarks
- Types of Data Analytics
- Clustering Text
- Topic Modeling
- Metrics for Label Description
- Concluding Remarks
Week 4: Exploratory Data Analysis and Visualization
- Graphs Are Comparisons
- Use Data To Answer Questions
- A Case Example
- Decision Making Process of Data Visualization 1
- Decision Making Process of Data Visualization 2
- Decision Making Process Main Worked Example
- Why Visualize Data Worked Example 1
- Why Visualize Data Worked Example 2
- Dashboards
- Dashboards Worked Example 1
- Dashboards Worked Example 2
Week 5: Introduction to Bayesian Modeling
- Introduction to Bayesian Modeling
- Probability Calibration
- Probability As Measurement of Uncertainty
- Bayesian Inference
- How To Use Prior Information
- Bayesian Modeling in Practice
- Business Applications in Bayesian Statistics Introduction
- Data Collection and Model Building 1
- Data Collection and Model Building 2
- Model Building Review
- Model Insights 1
- Model Insights 2
- Example Modeling Museum Membership Renewal
- Example Modeling User Behavior on a Deals Website
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Grading Policy
Assignments count towards 100% of the grade. There will be no midterm exam and no final exam.
Two lowest assignments will be dropped.
Students have 2 attempts to complete assignments, unless otherwise noted in the assignment description.
Passing grade for the course is 60% or higher.
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Certificate Information
This course is part of a three-part Data Science and Analytics in Context XSeries from ColumbiaX. If you earn a passing grade in all three courses in this series for a verified certificate, you will also receive an XSeries certificate for the series.
To receive your certificate at any time after you qualify, follow these steps.
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On the course Progress page, select Request Certificate.
After you request your certificate, the certificate creation process may take up to 48 hours. When your certificate is ready, a “Your certificate is available” message appears on your Progress page.
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Download your certificate from the Progress page or from your dashboard.
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