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Muhammad Saad uddin
In this 2-hour long project-based course, you will learn some important statistical concepts with examples & visuals, concepts that are most commonly mistaken in data analysis and how to ensure you don’t fall for them.
Register for this course and see more details by visiting:
OpenCourser.com/course/fr3c6k/avoiding
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Taught by Muhammad Saad uddin, who is recognized for their work in statistical concepts
Develops core statistical concepts in data analysis
Suitable for learners starting out in data analysis and seeking a strong foundation
Provides hands-on exercises and examples to solidify understanding
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Save Avoiding Data Science Pitfalls to your list so you can find it easily later:
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Reviews summary
Data science pitfalls not avoided
While a few students appreciated some of the content in this 2-hour course, most students found this course to be lacking in substance. There were problems with the learning material and the delivery of that material.
Instructor's delivery ineffective
"Much of the video showed slow typing of code without explaining what it did or why"
Problematic learning material
"The material didn't work..."
Lecture content not substantial
"...explained little."
"...Descriptions of pitfalls were lacking..."
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 Avoiding Data Science Pitfalls with these
activities:
Review Basic Statistics
Show steps
Begin this course with a quick review of basic statistics concepts, including measures of central tendency, dispersion, and probability.
Browse courses on
Statistics
Show steps
-
Review notes or textbooks on descriptive statistics, probability, and hypothesis testing.
-
Watch online tutorials or videos to reinforce concepts.
-
Complete practice problems or quizzes to test comprehension.
Create Infographics on Statistical Concepts
Show steps
Enhance your understanding and engage your audience by creating visually appealing infographics that explain statistical concepts clearly.
Browse courses on
Data Visualization
Show steps
-
Choose a statistical concept or topic to illustrate.
-
Design and create an infographic using design tools or software.
-
Share your infographic on social media or online platforms.
Host a Statistics Study Jam
Show steps
Reinforce your understanding and help others by organizing a study session where you collectively tackle difficult statistical concepts.
Browse courses on
Collaborative Learning
Show steps
-
Invite classmates or peers to participate in the study jam.
-
Prepare a lesson plan or discussion topics based on course material.
-
Facilitate the session, encourage participation, and provide guidance.
-
Document key takeaways and share them with participants.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Practice Data Analysis Techniques
Show steps
Enhance your data analysis skills by completing regular practice drills that involve applying statistical techniques to real-world datasets.
Browse courses on
Data Exploration
Show steps
-
Utilize online resources like Kaggle or DataCamp for practice datasets and exercises.
-
Join a study group or online forum to discuss solutions and learn from others.
-
Create your data analysis portfolio by showcasing your work on GitHub or a personal website.
Read 'Statistical Inference' by George Casella and Roger L. Berger
Show steps
Gain a deeper understanding of statistical inference concepts by reading and engaging with this comprehensive textbook.
View
Statistical Inference (Chapman & Hall/CRC Texts...
on Amazon
Show steps
-
Read assigned chapters and make notes on important concepts.
-
Complete end-of-chapter exercises to test your comprehension.
-
Discuss key insights and questions with classmates or online forums.
Develop a Data Analysis Project
Show steps
Solidify your learning by working on a project that involves data collection, analysis, and presentation of findings.
Browse courses on
Project Management
Show steps
-
Identify a research question and gather relevant data.
-
Apply appropriate statistical techniques to analyze the data.
-
Create data visualizations and dashboards to present your findings.
-
Write a project report or deliver a presentation to showcase your work.
Attend a Machine Learning Workshop
Show steps
Expand your knowledge and gain hands-on experience by attending workshops focused on advanced statistical techniques, such as machine learning.
Show steps
-
Research and identify relevant workshops.
-
Attend the workshop and actively participate in discussions and activities.
-
Network with professionals and experts in the field.
-
Follow up after the workshop to apply what you learned.
Review Basic Statistics
Show steps
Begin this course with a quick review of basic statistics concepts, including measures of central tendency, dispersion, and probability.
Browse courses on
Statistics
Show steps
Show steps
Show steps
- Review notes or textbooks on descriptive statistics, probability, and hypothesis testing.
- Watch online tutorials or videos to reinforce concepts.
- Complete practice problems or quizzes to test comprehension.
Create Infographics on Statistical Concepts
Show steps
Enhance your understanding and engage your audience by creating visually appealing infographics that explain statistical concepts clearly.
Browse courses on
Data Visualization
Show steps
Show steps
Show steps
- Choose a statistical concept or topic to illustrate.
- Design and create an infographic using design tools or software.
- Share your infographic on social media or online platforms.
Host a Statistics Study Jam
Show steps
Reinforce your understanding and help others by organizing a study session where you collectively tackle difficult statistical concepts.
Browse courses on
Collaborative Learning
Show steps
Show steps
Show steps
- Invite classmates or peers to participate in the study jam.
- Prepare a lesson plan or discussion topics based on course material.
- Facilitate the session, encourage participation, and provide guidance.
- Document key takeaways and share them with participants.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Practice Data Analysis Techniques
Show steps
Enhance your data analysis skills by completing regular practice drills that involve applying statistical techniques to real-world datasets.
Browse courses on
Data Exploration
Show steps
Show steps
Show steps
- Utilize online resources like Kaggle or DataCamp for practice datasets and exercises.
- Join a study group or online forum to discuss solutions and learn from others.
- Create your data analysis portfolio by showcasing your work on GitHub or a personal website.
Read 'Statistical Inference' by George Casella and Roger L. Berger
Show steps
Gain a deeper understanding of statistical inference concepts by reading and engaging with this comprehensive textbook.
View
Statistical Inference (Chapman & Hall/CRC Texts...
on Amazon
Show steps
Show steps
Show steps
- Read assigned chapters and make notes on important concepts.
- Complete end-of-chapter exercises to test your comprehension.
- Discuss key insights and questions with classmates or online forums.
Develop a Data Analysis Project
Show steps
Solidify your learning by working on a project that involves data collection, analysis, and presentation of findings.
Browse courses on
Project Management
Show steps
Show steps
Show steps
- Identify a research question and gather relevant data.
- Apply appropriate statistical techniques to analyze the data.
- Create data visualizations and dashboards to present your findings.
- Write a project report or deliver a presentation to showcase your work.
Attend a Machine Learning Workshop
Show steps
Expand your knowledge and gain hands-on experience by attending workshops focused on advanced statistical techniques, such as machine learning.
Show steps
Show steps
Show steps
- Research and identify relevant workshops.
- Attend the workshop and actively participate in discussions and activities.
- Network with professionals and experts in the field.
- Follow up after the workshop to apply what you learned.
Career center
Learners who complete Avoiding Data Science Pitfalls will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
Data Scientists need to be able to avoid data science pitfalls in order to be successful. This course will teach you some of the most common statistical mistakes to avoid. It will also teach you how to ensure that your data is accurate and reliable. With this knowledge, you can build a strong foundation for your career as a Data Scientist and help your clients make better decisions.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers need to be able to avoid data science pitfalls in order to be successful. This course will teach you some of the most common statistical mistakes to avoid. It will also teach you how to ensure that your data is accurate and reliable. With this knowledge, you can build a strong foundation for your career as a Machine Learning Engineer and help your clients make better decisions.
Business Analyst
Business Analyst
Business Analysts need to be able to avoid data science pitfalls in order to be successful. This course will teach you some of the most common statistical mistakes to avoid. It will also teach you how to ensure that your data is accurate and reliable. With this knowledge, you can build a strong foundation for your career as a Business Analyst and help your clients make better decisions.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts need to be able to avoid data science pitfalls in order to be successful. This course will teach you some of the most common statistical mistakes to avoid. It will also teach you how to ensure that your data is accurate and reliable. With this knowledge, you can build a strong foundation for your career as an Operations Research Analyst and help your clients make better decisions.
Statistician
Statistician
Statisticians need to be able to avoid data science pitfalls in order to be successful. This course will teach you some of the most common statistical mistakes to avoid. It will also teach you how to ensure that your data is accurate and reliable. With this knowledge, you can build a strong foundation for your career as a Statistician and help your clients make better decisions.
Market Researcher
Market Researcher
Market Researchers need to be able to avoid data science pitfalls in order to be successful. This course will teach you some of the most common statistical mistakes to avoid. It will also teach you how to ensure that your data is accurate and reliable. With this knowledge, you can build a strong foundation for your career as a Market Researcher and help your clients make better decisions.
Research Analyst
Research Analyst
Making informed, data-driven decisions is essential for Research Analysts. Avoiding Data Science Pitfalls will help you make better data-informed decisions. The course will teach you some of the most common statistical mistakes to avoid. It will also teach you how to ensure that your data is accurate and reliable. With this knowledge, you can build a strong foundation for your career as a Research Analyst and help your clients make better decisions.
Data Analyst
Data Analyst
Data Analysts need to be able to avoid data science pitfalls in order to be successful. This course will teach you some of the most common statistical mistakes to avoid. It will also teach you how to ensure that your data is accurate and reliable. With this knowledge, you can build a strong foundation for your career as a Data Analyst and help your clients make better decisions.
Financial Analyst
Financial Analyst
Financial Analysts need to be able to avoid data science pitfalls in order to be successful. This course will teach you some of the most common statistical mistakes to avoid. It will also teach you how to ensure that your data is accurate and reliable. With this knowledge, you can build a strong foundation for your career as a Financial Analyst and help your clients make better decisions.
Risk Analyst
Risk Analyst
Risk Analysts need to be able to avoid data science pitfalls in order to be successful. This course will teach you some of the most common statistical mistakes to avoid. It will also teach you how to ensure that your data is accurate and reliable. With this knowledge, you can build a strong foundation for your career as a Risk Analyst and help your clients make better decisions.
Data Engineer
Data Engineer
Data Engineers need to be able to avoid data science pitfalls in order to be successful. This course will teach you some of the most common statistical mistakes to avoid. It will also teach you how to ensure that your data is accurate and reliable. With this knowledge, you can build a strong foundation for your career as a Data Engineer and help your clients make better decisions.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts need to be able to avoid data science pitfalls in order to be successful. This course will teach you some of the most common statistical mistakes to avoid. It will also teach you how to ensure that your data is accurate and reliable. With this knowledge, you can build a strong foundation for your career as a Quantitative Analyst and help your clients make better decisions.
Business Intelligence Analyst
Business Intelligence Analyst
Business Intelligence Analysts need to be able to avoid data science pitfalls in order to be successful. This course will teach you some of the most common statistical mistakes to avoid. It will also teach you how to ensure that your data is accurate and reliable. With this knowledge, you can build a strong foundation for your career as a Business Intelligence Analyst and help your clients make better decisions.
Software Engineer
Software Engineer
Software Engineers who work with data may benefit from taking this course to avoid data science pitfalls. The course will teach you some of the most common statistical mistakes to avoid. It will also teach you how to ensure that your data is accurate and reliable. With this knowledge, you can build a strong foundation for your career as a Software Engineer.
Database Administrator
Database Administrator
Database Administrators who work with data may benefit from taking this course to avoid data science pitfalls. The course will teach you some of the most common statistical mistakes to avoid. It will also teach you how to ensure that your data is accurate and reliable. With this knowledge, you can build a strong foundation for your career as a Database Administrator.
For more career information including salaries, visit:
OpenCourser.com/course/fr3c6k/avoiding
Reading list
We've selected 11 books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Avoiding Data Science Pitfalls.
Covers the theoretical underpinnings of classical statistical methods and provides a conceptual framework for data analysis. It introduces basic statistical tools used for data understanding and exploratory data analysis.
Provides a practical guide to a range of statistical techniques, with a focus on data analysis and interpretation. It offers clear explanations and examples, making it an accessible resource for both beginners and practitioners.
Provides a clear and concise introduction to time series analysis, covering topics such as stationarity, forecasting, and spectral analysis. It complements the course by offering a practical guide to handling time-dependent data.
Explores techniques for making machine learning models more interpretable and understandable. It complements the course by providing practical guidance on how to evaluate and improve the interpretability of data science models.
Offers a hands-on introduction to data science, covering topics such as data cleaning, feature engineering, and model building. It provides a valuable resource for learners who seek to gain practical experience with data science techniques.
Provides a comprehensive guide to data science using Python. It covers topics such as data manipulation, visualization, and machine learning, offering a practical reference for learners who seek to apply data science techniques using Python.
Offers a comprehensive guide to the design and implementation of data science projects. It covers topics such as data collection, feature engineering, and model evaluation, providing practical guidance for learners who seek to develop robust and effective data science solutions.
Provides a comprehensive guide to data analysis using Python. It covers topics such as data manipulation, visualization, and machine learning, offering a practical reference for learners who seek to analyze and interpret data using Python.
Save
Provides a practical introduction to data science using R. It covers topics such as data manipulation, visualization, and machine learning, offering a valuable resource for learners who seek to apply data science techniques using R.
Offers a practical introduction to machine learning for non-technical audiences. It covers topics such as data preprocessing, model training, and evaluation, providing a valuable resource for learners who seek to gain a basic understanding of machine learning concepts.
Offers a practical introduction to Python programming, with a focus on automating tasks. It covers topics such as data manipulation, web scraping, and scripting, providing a valuable resource for learners who seek to enhance their productivity and automate repetitive tasks.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/fr3c6k/avoiding
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Time
2 hours
Via
Coursera
Institution
Coursera Project Network
Instructor
Muhammad Saad uddin
Good to know
Taught by Muhammad Saad uddin, who is recognized for their work in statistical concepts
Develops core statistical concepts in data analysis
Suitable for learners starting out in data analysis and seeking a strong foundation
Provides hands-on exercises and examples to solidify understanding
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