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This statistics and data analysis course will pave the statistical foundation for discussion on data science. You’ll learn how data scientists exercise statistical thinking in designing data collection, derive insights from visualizing data, obtain supporting evidence for data-based decisions and construct models for predicting future trends from data. If you are interested in this field you’ll be benefitted from this course.

### Assessment

This course does not involve any written exams. Students need to answer 5 assignment questions to complete the course, the answers will be in the form of written work in pdf or word. Students can write the answers in their own time. Each answer needs to be 200 words (1 Page). Once the answers are submitted, the tutor will check and assess the work.

### Certification

Edukite courses are free to study. To successfully complete a course you must submit all the assignment of the course as part of the assessment. Upon successful completion of a course, you can choose to make your achievement formal by obtaining your Certificate at a cost of £49.

Having an Official Edukite Certification is a great way to celebrate and share your success. You can:

• Show it to prove your success

Course Credit: Columbia University

### Course Curriculum

 Module: 1 1.1 Lecture: What is Data Science? 00:06:00 1.2 Lecture: What questions can Data Science answer? 00:07:00 1.3 Lecture: Why is there an explosion of data? 00:06:00 1.4 Lecture: What role does data visualization play in Data Science? 00:06:00 1.5 Lecture: How did you become interested in Data Science? 00:10:00 1.6 Lecture: What do you predict will happen in Data Science in 5 years? 00:07:00 1.7 Lecture: What skills does a Data Scientist need to be successful? 00:08:00 1.8 Lecture: What should a non-Data Scientist know about Data Science? 00:07:00 1.9 Assignment 00:40:00 1.10 Assignment 00:05:00 Module: 2 2.1 Lecture: Introduction 00:02:00 2.1 Lecture: Statistical Thinking for Data Science 00:15:00 2.2 Numerical Data 1 Simple Visualization (Part 1) 00:08:00 2.2 Numerical Data 1 Simple Visualization (Part 2) 00:07:00 2.2 Numerical Data 1 Simple Visualization (Part 3) 00:06:00 2.3 Lecture: Numerical Data 2 Simple Visualization and Summaries (Part 1) 00:10:00 2.3 Lecture: Numerical Data 2 Simple Visualization and Summaries (Part 2) 00:08:00 2.4 Lecture: Numerical Data 3 Association Part 1 00:11:00 2.4 Lecture: Numerical Data 3 Association Part 2 00:08:00 2.4 Lecture: Numerical Data 3 Association Part 3 00:14:00 2.5 Lecture: Data Collection – Sampling 00:16:00 2.6 Lecture: Introduction to Probability (Part 1) 00:06:00 2.6 Lecture: Introduction to Probability (Part 2) 00:09:00 2.6 Lecture: Introduction to Probability (Part 3) 00:11:00 2.6 Lecture: Introduction to Probability (Part 4) 00:06:00 2.7 Lecture: Statistical Inference – Confidence Intervals (Part 1) 00:14:00 2.7 Lecture: Statistical Inference – Confidence Intervals (Part 2) 00:06:00 2.8 Lecture: Statistical Inference – Significance tests (Part 1) 00:15:00 2.8 Lecture: Statistical Inference – Significance Tests (Part 2) 00:04:00 2.9 Lecture: Introduction of Health Care Database 00:01:00 2.9 Lecture: Status of Current Observational Health Studies (Part 1) 00:12:00 2.9 Lecture: Status of Current Observational Health Studies (Part 2) 00:07:00 2.10 Lecture: Statistical Terms Explained 00:14:00 2.11 Lecture: Unknown Characteristics of Observational Health Studies (Part 1) 00:07:00 2.11 Lecture: Unknown Characteristics of Observational Health Studies (Part 2) 00:09:00 2.12 Lecture: Lessons Learnt from OMOP Experiments 00:16:00 2.13 Lecture: P-value Calibration 00:10:00 2.14 Lecture: Concluding Remarks 00:08:00 Module: 3 3.1 Lecture: Conditional Probability 00:14:00 3.2 Lecture: Bayes’ formula (Part 1) 00:06:00 3.2 Lecture: Bayes’ formula (Part 2) 00:10:00 3.3 Lecture: Studying Association: Two-way Table 00:14:00 3.4 Lecture: Studying Association: Chi-square Test of Independence 00:15:00 3.5 Lecture: Association: One-way Analysis of Variance (Part 1) 00:12:00 3.5 Lecture: Association: One-way Analysis of Variance (Part 2) 00:07:00 3.6 Lecture: Regression Analysis 1 00:10:00 3.6 Lecture: Regression Analysis 2 00:13:00 3.7 Lecture: Regression Analysis 3 00:10:00 3.7 Lecture: Regression Analysis 4 00:13:00 3.8 Regression Analysis 4 and Concluding Remarks 00:11:00 3.9 Lecture: Types of Data Analytics 00:11:00 3.10 Lecture: Clustering Text (Part 1) 00:13:00 3.10 Lecture: Clustering Text (Part 2) 00:15:00 3.11 Lecture: Topic Modeling (Part 2) 00:13:00 3.11 Lecture: Topic Modeling (Part 3) 00:07:00 3.12 Lecture: Metrics for Label Description (Part 1) 00:12:00 3.12 Lecture: Metrics for Label Description (Part 2) 00:14:00 3.13 Lecture: Concluding Remarks 00:02:00 Module: 4 Different goals, different looks 00:30:00 4.1 Readings pdf 2 00:05:00 4.1 Readings pdf 3 00:05:00 4.1 Readings pdf 4 00:05:00 4.1 Lecture: Graphs Are Comparisons (Part 1) 00:07:00 4.1 Lecture: Graphs Are Comparisons (Part 2) 00:10:00 4.1 Lecture: Graphs Are Comparisons (Part 3) 00:04:00 4.2 Lecture: Use Data To Answer Questions (Part 1) 00:09:00 4.2 Lecture: Use Data To Answer Questions (Part 2) 00:08:00 4.3 Lecture: A Case Example (Part 1) 00:09:00 4.3 Lecture: A Case Example (Part 2) 00:08:00 4.4 Lecture: Decision Making Process of Data Visualization 1 00:13:00 4.5 Lecture: Decision Making Process of Data Visualization 2 00:08:00 4.6 Lecture: Decision Making Process Main Worked Example 00:13:00 4.7 Why Visualize Data Worked Example 1 00:15:00 4.8 Lecture: Why Visualize Data Worked Example 2 (Part 1) 00:08:00 4.8 Lecture: Why Visualize Data Worked Example 2 (Part 2) 00:09:00 4.9 Lecture: Dashboards 00:15:00 4.10 Lecture: Dashboards Worked Example 1 (Part 1) 00:13:00 4.10 Lecture: Dashboards worked Example 1 (Part 2) 00:11:00 4.11 Dashboards Worked Example 2 00:10:00 Module: 5 5.1 Introduction to Bayesian Modeling 00:03:00 5.2 Lecture: Probability Calibration 00:14:00 5.3 Lecture: Probability As Measurement of Uncertainty 00:12:00 5.4 Lecturer: Bayesian Inference 00:10:00 5.5 Lecture: How To Use Prior Information 00:12:00 5.6 Lecture: Bayesian Modeling in Practice (Part 1) 00:15:00 5.6 Lecture: Bayesian Modeling in Practice (Part 2) 00:11:00 5.7 Lecture: Business Applications in Bayesian Statistics Introduction 00:04:00 5.8 Lecture: Data Collection and Model Building 1 00:09:00 5.9 Lecture: Data Collection and Model Building 2 00:11:00 5.10 Lecture: Model Building Review 00:09:00 5.11 Lecture: Model Insights 1 00:09:00 5.12 Lecture: Model Insights 2 00:17:00 5.13 Lecture: Example Modeling Museum Membership Renewal (Part 1) 00:15:00 5.13 Lecture: Example Modeling Museum Membership Renewal (Part 2) 00:05:00 5.14 Lecture: Example Modeling User Behavior on a Deals Website (Part 1) 00:17:00 5.14 Lecture: Example Modeling User Behavior on a Deals Website (Part 2) 00:07:00 Week 5 Recommended Readings 00:20:00 Assessment Submit Your Assignment 00:00:00 Certification 00:00:00

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