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Data Science in Real Life

Approx. 7 hours to complete

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Course Summary

Real Life Data Science is a course that provides a hands-on experience in solving real-world problems using data science techniques.

Key Learning Points

Learn how to approach and solve data science problems in a practical way
Gain experience in working with real data sets
Acquire skills in data cleaning, data analysis, and data visualization

Learning Outcomes

Understand the practical applications of data science techniques
Gain experience in real-world problem solving
Master data cleaning, data analysis, and data visualization skills

Prerequisites or good to have knowledge before taking this course

Basic knowledge of Python programming language
Familiarity with statistics and linear algebra

Course Difficulty Level

Intermediate

Course Format

Online self-paced course
Hands-on experience with real data sets
Video lectures with quizzes and assignments

Similar Courses

Applied Data Science with Python
Data Science Essentials
Data Science Methodology

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Description

Have you ever had the perfect data science experience? The data pull went perfectly. There were no merging errors or missing data. Hypotheses were clearly defined prior to analyses. Randomization was performed for the treatment of interest. The analytic plan was outlined prior to analysis and followed exactly. The conclusions were clear and actionable decisions were obvious. Has that every happened to you? Of course not. Data analysis in real life is messy. How does one manage a team facing real data analyses? In this one-week course, we contrast the ideal with what happens in real life. By contrasting the ideal, you will learn key concepts that will help you manage real life analyses.

This is a focused course designed to rapidly get you up to speed on doing data science in real life. Our goal was to make this as convenient as possible for you without sacrificing any essential content. We've left the technical information aside so that you can focus on managing your team and moving it forward. After completing this course you will know how to: 1, Describe the “perfect” data science experience 2. Identify strengths and weaknesses in experimental designs 3. Describe possible pitfalls when pulling / assembling data and learn solutions for managing data pulls. 4. Challenge statistical modeling assumptions and drive feedback to data analysts 5. Describe common pitfalls in communicating data analyses 6. Get a glimpse into a day in the life of a data analysis manager. The course will be taught at a conceptual level for active managers of data scientists and statisticians. Some key concepts being discussed include: 1. Experimental design, randomization, A/B testing 2. Causal inference, counterfactuals, 3. Strategies for managing data quality. 4. Bias and confounding 5. Contrasting machine learning versus classical statistical inference Course promo: https://www.youtube.com/watch?v=9BIYmw5wnBI Course cover image by Jonathan Gross. Creative Commons BY-ND https://flic.kr/p/q1vudb

Knowledge

Identify strengths and weaknesses in experimental designs
Learn novel solutions for managing data pulls
Describe common pitfalls in communicating data analyses
Understand a typical day in the life of a data analysis manager

Outline

Introduction, the perfect data science experience
Just for fun, course promotional video
Data science in the ideal versus real life Part 1
Data science in the ideal versus real life Part 2
Examples
Machine Learning vs. Traditional Statistics Part 1
Machine Learning vs. Traditional Statistics Part 2
Managing the Data Pull
Experimental design and observational analysis
Causality part 1
Causality Part 2
What Can Go Wrong?: Confounding
A/B Testing
Sampling bias and random sampling
Blocking and adjustment
Multiplicity
Effect size, significance, & modeling
Comparison with benchmark effects
Negative controls
Non-significance
Estimation Target is Relevant
Report writing
Version control
Pre-Course Survey
Course structure
Grading
The data pull is clean
The experiment is carefully designed
The experiment is carefully designed, things to do
Results of analyses are clear
The decision is obvious
The analysis product is awesome
Post-Course Survey
The Data Pull is Clean
The experiment is carefully designed principles
The experiment is carefully designed, things to do
Results of analyses are clear
The Decision is Obvious
The analysis product is awesome

Summary of User Reviews

Real-Life Data Science course on Coursera has received positive reviews from users. Many users have found the course to be comprehensive and practical. It covers a range of topics that are relevant to real-world data science, including data cleaning, visualization, and machine learning.

Key Aspect Users Liked About This Course

Comprehensive and practical course content

Pros from User Reviews

The course provides hands-on experience with real-life datasets
The instructors are knowledgeable and engaging
The course covers a wide range of topics in data science
The course is well-structured and easy to follow

Cons from User Reviews

Some users have reported technical issues with the course platform
The course may be too basic for experienced data scientists
The course may require a significant time commitment
Some users have found the course to be too theoretical

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