### Course Syllabus

Week 1: Introduction to Data Science

1. Introduction to Data Science
2. What is Data Science?
3. What questions can Data Science answer?
4. Why is there an explosion of data?
5. What role does data visualization play in Data Science?
6. How did you become interested in Data Science?
7. What do you predict will happen in Data Science in 5 years?
8. What are the most important skills for a Data Scientist?
9. What should a non-Data Scientist know about Data Science?
10. What should a non-Data Scientist know about Data Science?

Week 2: Statistics and Probability I

1. Statistical Thinking for Data Science
2. Numerical Data 1 Simple Visualization and Summaries
3. Numerical Data 2 Simple Visualization and Summaries
4. Numerical Data 3 Association
5. Data Collection - Sampling
6. Introduction to Probability
7. Statistical Inference - Confidence Intervals
8. Statistical Inference - Significance tests
9. Status of Current Observational Health Studies
10. Statistical Terms Explained
11. Unknown Characteristics of Observational Health Studies
12. Lessons Learnt from OMOP Experiments
13. P-value Calibration
14. Concluding Remarks

Week 3: Statistics and Probability II

1. Conditional Probability
2. Bayes' Formula
3. Studying Association: Two-way Table
4. Studying Association: Chi-square Test of Independence
5. Studying Association: One-way Analysis of Variance
6. Regression Analysis 1 and 2
7. Regression Analysis 3 and 4
8. Regression Analysis 4 and Concluding Remarks
9. Types of Data Analytics
10. Clustering Text
11. Topic Modeling
12. Metrics for Label Description
13. Concluding Remarks

Week 4: Exploratory Data Analysis and Visualization

1. Graphs Are Comparisons
2. Use Data To Answer Questions
3. A Case Example
4. Decision Making Process of Data Visualization 1
5. Decision Making Process of Data Visualization 2
6. Decision Making Process Main Worked Example
7. Why Visualize Data Worked Example 1
8. Why Visualize Data Worked Example 2
9. Dashboards
10. Dashboards Worked Example 1
11. Dashboards Worked Example 2

Week 5: Introduction to Bayesian Modeling

1. Introduction to Bayesian Modeling
2. Probability Calibration
3. Probability As Measurement of Uncertainty
4. Bayesian Inference
5. How To Use Prior Information
6. Bayesian Modeling in Practice
7. Business Applications in Bayesian Statistics Introduction
8. Data Collection and Model Building 1
9. Data Collection and Model Building 2
10. Model Building Review
11. Model Insights 1
12. Model Insights 2
13. Example Modeling Museum Membership Renewal
14. Example Modeling User Behavior on a Deals Website

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.

#### _________________________________________________________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.

For self-paced courses, certificates are available on-demand, which means that once a student passes the course, they can request their certificate through their Progress page.