Coursera

Data Science in Real Life (Coursera)

Offered by Johns Hopkins University,

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.

Class Deals by MOOC List - Click here and see Coursera's Active Discounts, Deals, and Promo Codes.

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.
Course 4 of 5 in the Executive Data Science Specialization.
After completing this course you will know how to:

Describe the “perfect” data science experience
Identify strengths and weaknesses in experimental designs
Describe possible pitfalls when pulling / assembling data and learn solutions for managing data pulls.
Challenge statistical modeling assumptions and drive feedback to data analysts
Describe common pitfalls in communicating data analyses
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:

Experimental design, randomization, A/B testing
Causal inference, counterfactuals,
Strategies for managing data quality.
Bias and confounding
Contrasting machine learning versus classical statistical inference

What You Will Learn

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

Syllabus

WEEK 1
Introduction, the perfect data science experience
This course is one module, intended to be taken in one week. Please do the course roughly in the order presented. Each lecture has reading and videos. Except for the introductory lecture, every lecture has a 5 question quiz; get 4 out of 5 or better on the quiz.

Go to Class

MOOC List is learner-supported. When you buy through links on our site, we may earn an affiliate commission.

Related Courses

Coursera

University of Colorado System

Inclusive Leadership: The Power of Workplace Diversity (Coursera)

Social Sciences Business

This course will equip and empower you to be a highly inclusive leader. You will learn principles, perspectives and practices that help to reap the power of workplace diversity. Workplace diversity has expanded beyond traditional demographics such as gender, race, and ethnicity. Those categories always will matter. In today’s workplace, diversity also encompasses nationality, religious background, sexual orientation, age, ability, experience, and diversity of thought, among other characteristics. Leaders will need to consider all of these aspects of diversity – among all stakeholders. That includes current and prospective employees, customers, clients , students, peers, and anyone else with whom a leader interacts.

Nov 16th 2026

4 Weeks

Leadership Leader Inclusive Leadership

Coursera

University of Edinburgh

Football: More than a Game (Coursera)

Education

Explore the world of football (soccer), the money, the rivalries, the trends, the past, the present, the men’s game, the women's game and the real issues. Whether you love it, hate it or try to ignore it – join us as we go behind the scenes to examine why football is more than just a game.

Nov 16th 2026

5-12 Weeks

Sports Data Analysis Football

Coursera

University of Toronto

Bioinformatic Methods I (Coursera)

Health & Society Statistics & Data Analysis

Large-scale biology projects such as the sequencing of the human genome and gene expression surveys using RNA-seq, microarrays and other technologies have created a wealth of data for biologists. However, the challenge facing scientists is analyzing and even accessing these data to extract useful information pertaining to the system being studied. This course focuses on employing existing bioinformatic resources – mainly web-based programs and databases – to access the wealth of data to answer questions relevant to the average biologist, and is highly hands-on.

Nov 16th 2026

5-12 Weeks

Biology Bioinformatics Molecular Biology

Coursera

Yonsei University

Deep Learning for Business (Coursera)

Management & Leadership

Your smartphone, smartwatch, and automobile (if it is a newer model) have AI (Artificial Intelligence) inside serving you every day. In the near future, more advanced “self-learning” capable DL (Deep Learning) and ML (Machine Learning) technology will be used in almost every aspect of your business and industry. So now is the right time to learn what DL and ML is and how to use it in advantage of your company. This course has three parts, where the first part focuses on DL and ML technology based future business strategy including details on new state-of-the-art products/services and open source DL software, which are the future enablers.

Nov 16th 2026

5-12 Weeks

Business ML Artificial Intelligence

Coursera

University of Geneva

Precision Medicine (Coursera)

Sci: Biology & Life Sciences Health & Society

This course will provide you with the key knowledge and tools to understand the fundamentals and practical implications of precision medicine, its opportunities and challenges. It will address precision-medicine era diagnostics, treatment selection, genetic counseling, public health interventions, and biomedical research. It will also deal with data science and ethical issues. From genomic analysis and genetic counseling to cancer biomarkers, from risk assessment of chronic diseases to the understanding of gene-environment interactions, from pharmacogenomics to multi-omics data integration, experts will walk you through the many aspects of precision medicine, from the bench to the bedside and to population health.

Nov 16th 2026

5-12 Weeks

Medicine Health Cancer

Coursera

SAS

Statistical Thinking for Industrial Problem Solving, presented by JMP (Coursera)

Statistics & Data Analysis

Statistical Thinking for Industrial Problem Solving is an applied statistics course for scientists and engineers offered by JMP, a division of SAS. By completing this course, students will understand the importance of statistical thinking, and will be able to use data and basic statistical methods to solve many real-world problems.

Nov 16th 2026

5-12 Weeks

Data Analysis Exploratory Data Analysis Problem Solving

Coursera

University of Illinois at Urbana-Champaign

Machine Learning for Accounting with Python (Coursera)

Data Science

This course, Machine Learning for Accounting with Python, introduces machine learning algorithms (models) and their applications in accounting problems. It covers classification, regression, clustering, text analysis, time series analysis. It also discusses model evaluation and model optimization. This course provides an entry point for students to be able to apply proper machine learning models on business related datasets with Python to solve various problems.

Nov 16th 2026

5-12 Weeks

Algorithms Machine Learning Clustering

Coursera

Google Cloud

Attention Mechanism (Coursera)

CS: Information & Technology

This course will introduce you to the attention mechanism, a powerful technique that allows neural networks to focus on specific parts of an input sequence. You will learn how attention works, and how it can be used to improve the performance of a variety of machine learning tasks, including machine translation, text summarization, and question answering.

Nov 16th 2026

1 Week

Machine Learning Neural Networks Attention

Coursera

University of California, San Diego

Design Principles: an Introduction (Coursera)

CS: Software Engineering CS: Design & Product

What makes an interface intuitive? How can I tell whether one design works better than another? This course will teach you fundamental principles of design and how to effectively evaluate your work with users. You'll learn fundamental principles of visual design so that you can effectively organize and present information with your interfaces. You'll learn principles of perception and cognition that inform effective interaction design. And you'll learn how to perform and analyze controlled experiments online. In many cases, we'll use Web design as the anchoring domain.

Nov 16th 2026

3 Weeks

User Experience Information Design Interaction Design

Coursera

IBM

SQL: A Practical Introduction for Querying Databases (Coursera)

Statistics & Data Analysis Data Science

Much of the world's data lives in databases. SQL (or Structured Query Language) is a powerful programming language that is used for communicating with and manipulating data in databases. A working knowledge of databases and SQL is a must for anyone who wants to start a career in Data Engineering, Data Warehousing, Data Analytics, Data Science or Business Intelligence. The purpose of this course is to help you learn and apply foundational and intermediate knowledge of the SQL language, and become familiar with many relational database (RDBMS) concepts along the way.

Nov 16th 2026

5-12 Weeks

Databases SQL Data Analysis

Coursera

Google Cloud

Preparing for the Google Cloud Professional Data Engineer Exam (Coursera)

CS: Information & Technology Computer Science

From the course: "The best way to prepare for the exam is to be competent in the skills required of the job." This course uses a top-down approach to recognize knowledge and skills already known, and to surface information and skill areas for additional preparation. You can use this course to help create your own custom preparation plan. It helps you distinguish what you know from what you don't know. And it helps you develop and practice skills required of practitioners who perform this job.

Nov 16th 2026

5-12 Weeks

Machine Learning Data Engineer Google Cloud

Coursera

Sungkyunkwan University - SKKU

Using R for Regression and Machine Learning in Investment (Coursera)

Economics & Finance Business

In this course, the instructor will discuss various uses of regression in investment problems, and she will extend the discussion to logistic, Lasso, and Ridge regressions. At the same time, the instructor will introduce various concepts of machine learning. You can consider this course as the first step toward using machine learning methodologies in solving investment problems. The course will cover investment analysis topics, but at the same time, make you practice it using R programming. This course's focus is to train you to use various regression methodologies for investment management that you might need to do in your job every day and make you ready for more advanced topics in machine learning.

Nov 16th 2026

2 Weeks

Machine Learning Regression Investment