Data Science in Real Life from Coursera

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

What's inside

Syllabus

Traffic lights

Read about what's good

what should give you pause

and possible dealbreakers

Enriches managers who have data analysts reporting to them through genuine case examples

Helps managers understand complex data analysis processes, making decision-making more informed and efficient

Provides strategies for overseeing projects that handle large datasets, ensuring accurate analysis

Facilitates the communication of insights from data analysis to non-technical stakeholders, fostering collaboration

Strengthens problem-solving abilities, enabling managers to navigate data analysis challenges independently

Imparts a comprehensive understanding of data science concepts, empowering managers to make informed decisions

Reviews summary

Conceptual data science for managers

According to learners, this course provides a highly relevant conceptual overview of the challenges in real-life data science, particularly valuable for managers and non-technical professionals. Students appreciate that it focuses on the managerial aspects and avoids technical detail, helping them frame and address issues like data quality and bias. While many find the high-level approach ideal for their needs and praise the efficient one-week format, some more experienced learners feel it lacks depth and specific practical examples. Overall, it's seen as a solid introduction for its intended audience, though it may be too superficial for those seeking detailed, hands-on strategies for tackling messy data.

Conceptual and avoids deep technical detail.

"This was mostly a high-level theoretical discussion of potential issues."

"While it avoids technical detail, I felt it could have provided slightly more concrete examples or frameworks..."

"I appreciate that it's conceptual and avoids technical jargon, which is perfect for my role."

"The course is taught at a conceptual level for active managers."

Efficient and manageable one-week structure.

"Short, sweet, and highly relevant."

"The module structure worked well for a busy schedule."

"The one-week format is efficient."

"Our goal was to make this as convenient as possible for you."

Highly relevant for managers of data teams.

"As a project manager for a data team, this course hit all the right points... provides a good framework for thinking about managing the process."

"The non-technical approach was perfect for me as a non-data scientist leading a team."

"Exactly what I needed! A course that focuses on the managerial challenges of data science without getting lost in the code."

"Excellent course for anyone managing data teams. It frames the common challenges beautifully..."

Some found it too basic or lacking practical examples.

"Found this a bit basic... stays very surface level. If you already have some experience managing data projects, you might not learn much new."

"Not enough actionable advice for someone hands-on, even in a managerial role. Felt it promised 'real life' but delivered 'classroom hypotheticals'."

"Wish there were more case studies or examples of navigating real-life data messes..."

"I felt it could have provided slightly more concrete examples or frameworks for handling the 'messiness' it promises."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Data Science in Real Life with these activities:

Review basics of experimental design

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By reviewing the fundamentals of experimental design, you will strengthen your foundation in hypothesis formulation, data collection, and statistical analysis.

Browse courses on Experimental Design

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Read the introductory chapter of a textbook on experimental design.
Work through practice problems on designing experiments.
Review journal articles that use experimental design to support their findings.

Review concepts of bias and confounding

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Refreshing your understanding of bias and confounding will strengthen your ability to identify and address potential threats to the validity of your data analysis results.

Browse courses on Bias

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Reread lecture notes or textbooks on bias and confounding.
Work through practice problems on identifying and mitigating bias and confounding.
Discuss these concepts with classmates or colleagues.

Read "Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking"

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This book provides practical insights into the business applications of data science, helping you understand how to leverage data to drive informed decision-making.

View Data Science for Business: What You Need to... on Amazon

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Read each chapter thoroughly.
Summarize the key concepts and takeaways.
Apply the concepts to real-world business scenarios.

Five other activities

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Show all eight activities

Explore data analysis pipelines in R and Python

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Familiarizing yourself with data analysis pipelines will enhance your understanding of data manipulation, cleaning, and visualization techniques.

Browse courses on Data Analysis

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Follow online tutorials on building a basic data analysis pipeline in R or Python.
Practice implementing the pipeline on a small dataset.
Identify areas for improvement and optimize the pipeline.

Create a presentation on data management best practices

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Creating a presentation will not only deepen your understanding of data management best practices but also enhance your communication and presentation skills.

Browse courses on Data Management

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Research and gather information on data management best practices.
Develop a structured presentation outline.
Create visually engaging slides.
Practice delivering the presentation effectively.

Engage in peer discussions on data analysis pitfalls

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Engaging in peer discussions will allow you to share insights, learn from others' experiences, and identify potential blind spots in your data analysis approach.

Browse courses on Data Analysis

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Join or create a study group or online forum specifically focused on data analysis.
Participate actively in discussions and share your perspectives.
Seek feedback and constructive criticism on your analysis methods.

Develop a data analysis plan for a real-world problem

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Creating a data analysis plan will force you to think critically about problem formulation, data requirements, and analysis methods, improving your overall data analysis skills.

Browse courses on Problem Solving

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Identify a real-world problem that can be addressed through data analysis.
Define the research question and objectives.
Determine the data sources and collection methods.
Outline the data analysis methods you plan to use.

Attend a workshop on data visualization and communication

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Participating in a workshop will provide hands-on experience and expert guidance on effectively communicating data insights through visualization.

Browse courses on Data Visualization

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Search for and register for a workshop on data visualization and communication.
Actively participate in the workshop activities and discussions.
Implement the techniques learned in your own data analysis projects.

Career center

Learners who complete Data Science in Real Life will develop knowledge and skills that may be useful to these careers:

Machine Learning Engineer

Machine Learning Engineers play a critical role in the development and deployment of machine learning models. They work with data scientists to design and implement models, and then work with software engineers to integrate models into production systems. This course would be particularly relevant to Machine Learning Engineers, as it provides a foundation in the principles of data science and experimental design. The course would also help Machine Learning Engineers to develop the skills necessary to manage and communicate data analysis projects.

See salaries and explore the career path for Machine Learning Engineer

Data Analyst

Data Analysts play a critical role in the collection, analysis, and interpretation of data. They work with businesses to identify and solve problems using data-driven insights. This course would be particularly relevant to Data Analysts, as it provides a foundation in the principles of data science and experimental design. The course would also help Data Analysts to develop the skills necessary to manage and communicate data analysis projects.

See salaries and explore the career path for Data Analyst

Data Scientist

Data Scientists play a critical role in the development and deployment of data-driven solutions. They work with businesses to identify and solve problems using data-driven insights. This course would be particularly relevant to Data Scientists, as it provides a foundation in the principles of data science and experimental design. The course would also help Data Scientists to develop the skills necessary to manage and communicate data analysis projects.

See salaries and explore the career path for Data Scientist

Statistician

Statisticians play a critical role in the collection, analysis, and interpretation of data. They work with businesses and researchers to design and conduct studies, and then analyze and interpret the results. This course would be particularly relevant to Statisticians, as it provides a foundation in the principles of data science and experimental design. The course would also help Statisticians to develop the skills necessary to manage and communicate data analysis projects.

See salaries and explore the career path for Statistician

Quantitative Analyst

Quantitative Analysts play a critical role in the development and deployment of quantitative models. They work with businesses and investors to identify and solve problems using data-driven insights. This course would be particularly relevant to Quantitative Analysts, as it provides a foundation in the principles of data science and experimental design. The course would also help Quantitative Analysts to develop the skills necessary to manage and communicate data analysis projects.

See salaries and explore the career path for Quantitative Analyst

Business Analyst

Business Analysts play a critical role in the identification and solution of business problems. They work with businesses to understand their needs and then develop and implement solutions. This course would be particularly relevant to Business Analysts, as it provides a foundation in the principles of data science and experimental design. The course would also help Business Analysts to develop the skills necessary to manage and communicate data analysis projects.

See salaries and explore the career path for Business Analyst

Operations Research Analyst

Operations Research Analysts play a critical role in the design and implementation of operations research solutions. They work with businesses to improve efficiency and productivity. This course would be particularly relevant to Operations Research Analysts, as it provides a foundation in the principles of data science and experimental design. The course would also help Operations Research Analysts to develop the skills necessary to manage and communicate data analysis projects.

See salaries and explore the career path for Operations Research Analyst

Management Consultant

Management Consultants play a critical role in the development and implementation of management solutions. They work with businesses to improve efficiency and productivity. This course would be particularly relevant to Management Consultants, as it provides a foundation in the principles of data science and experimental design. The course would also help Management Consultants to develop the skills necessary to manage and communicate data analysis projects.

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Financial Analyst

Financial Analysts play a critical role in the analysis and interpretation of financial data. They work with businesses and investors to make informed decisions. This course would be particularly relevant to Financial Analysts, as it provides a foundation in the principles of data science and experimental design. The course would also help Financial Analysts to develop the skills necessary to manage and communicate data analysis projects.

See salaries and explore the career path for Financial Analyst

Actuary

Actuaries play a critical role in the development and implementation of actuarial solutions. They work with businesses and individuals to assess and manage risk. This course would be particularly relevant to Actuaries, as it provides a foundation in the principles of data science and experimental design. The course would also help Actuaries to develop the skills necessary to manage and communicate data analysis projects.

See salaries and explore the career path for Actuary

Epidemiologist

Epidemiologists play a critical role in the investigation and control of diseases. They work with communities and governments to identify and prevent outbreaks. This course would be particularly relevant to Epidemiologists, as it provides a foundation in the principles of data science and experimental design. The course would also help Epidemiologists to develop the skills necessary to manage and communicate data analysis projects.

See salaries and explore the career path for Epidemiologist

Biostatistician

Biostatisticians play a critical role in the design and analysis of biomedical studies. They work with researchers and clinicians to develop and implement statistical methods. This course would be particularly relevant to Biostatisticians, as it provides a foundation in the principles of data science and experimental design. The course would also help Biostatisticians to develop the skills necessary to manage and communicate data analysis projects.

See salaries and explore the career path for Biostatistician

Data Engineer

Data Engineers play a critical role in the design and implementation of data pipelines. They work with businesses and data scientists to collect, store, and process data. This course would be particularly relevant to Data Engineers, as it provides a foundation in the principles of data science and experimental design. The course would also help Data Engineers to develop the skills necessary to manage and communicate data analysis projects.

See salaries and explore the career path for Data Engineer

Software Engineer

Software Engineers play a critical role in the design and implementation of software applications. They work with businesses and users to develop and maintain software solutions. This course may be useful to Software Engineers, as it provides a foundation in the principles of data science and experimental design. The course may also help Software Engineers to develop the skills necessary to manage and communicate data analysis projects.

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Product Manager

Product Managers play a critical role in the development and management of products. They work with businesses and customers to identify and meet product needs. This course may be useful to Product Managers, as it provides a foundation in the principles of data science and experimental design. The course may also help Product Managers to develop the skills necessary to manage and communicate data analysis projects.

See salaries and explore the career path for Product Manager