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 nonData Scientist know about Data Science?
 What should a nonData 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
 Pvalue Calibration
 Concluding Remarks
Week 3: Statistics and Probability II
 Conditional Probability
 Bayes' Formula
 Studying Association: Twoway Table
 Studying Association: Chisquare Test of Independence
 Studying Association: Oneway 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 threepart 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.
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