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Brian Roehm
What is artificial intelligence, machine learning, and big data? How do they impact my life and society? Explore these topics with a beginner-friendly introduction to data science, covering use cases, and problems solving solutions.
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Artificial Intelligence
Machine Learning
Big Data
Data Science Use Cases
Data Science Problem Solving
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OpenCourser.com/course/b7j1hx/data
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Traffic lights
Read about what's good
what should give you pause and
possible dealbreakers
Explores artificial intelligence, machine learning, and big data, which are foundational concepts in industry today
Examines applications of data science that are relevant to various fields and industries
Suitable for learners with no prior experience in data science
Taught by recognized industry expert Brian Roehm
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Reviews summary
Beginner-friendly data science overview
According to learners, this course is a highly effective and beginner-friendly introduction to data science. Students frequently praise its ability to demystify complex concepts like AI, Machine Learning, and Big Data through clear explanations and engaging real-world examples. While many found it an excellent starting point for understanding foundational concepts and their societal impact, some learners seeking deeper technical knowledge or hands-on coding found it too high-level or superficial for direct career application. It serves as a strong conceptual foundation, often encouraging students to explore further specialized studies.
Serves as an introduction, requires further study for skill development.
"If you already have some tech background, this might feel a bit too superficial."
"It's okay as a very basic introduction. I was hoping for a bit more depth in the practical applications."
"I definitely needed to seek additional resources for practical skills. Good for a general overview, not for becoming proficient."
Instructor facilitates clear and engaging learning experience.
"The instructor's delivery was engaging, and I felt I gained a solid foundation."
"The instructor was fantastic, making complex topics approachable."
"I found the teaching style incredibly helpful, really breaking down intimidating concepts into understandable parts."
Utilizes practical examples and discusses the societal impact of data science.
"I particularly enjoyed the real-world examples and use cases, which made abstract ideas very tangible."
"It successfully helps you explore how these topics impact my life and society."
"The impact on society discussions were particularly insightful, offering valuable perspective."
Offers a solid grasp of core data science concepts and their impact.
"The concepts of AI, ML, and Big Data were explained with such clarity. I felt I gained a solid foundation."
"It helped me understand how these technologies impact daily life. I feel much more confident discussing these topics now."
"This course truly demystifies data science. I had zero background and now I feel I have a strong grasp of the fundamentals."
Provides a clear and accessible introduction for new learners.
"Absolutely fantastic! This course provides a truly beginner-friendly introduction to data science."
"Excellent course for someone with no prior experience. The way they break down complex topics... is brilliant."
"As a complete beginner, this course was a lifesaver! I loved the clear explanations and definitions."
Insufficient hands-on coding or detailed technical application.
"I wish there were more hands-on coding exercises to really cement the concepts, but for an introduction, it's solid."
"It provides a good conceptual understanding, but lacks the technical depth I was looking for to start a career."
"Not enough coding or practical exercises. Might be okay for someone who knows absolutely nothing..."
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 Basics with these
activities:
Read a book on data science fundamentals
Show steps
Gain a comprehensive understanding of data science concepts through a structured and in-depth resource.
View
Artificial Intelligence For Dummies (For...
on Amazon
Show steps
-
Purchase or borrow the book.
-
Set aside time each day to read the book.
-
Take notes as you read.
-
Complete the exercises in the book.
-
Discuss the book with a friend or colleague.
Solve sample problems on elementary data types and operations
Show steps
Practice writing code to reinforce your understanding of basic data science concepts.
Browse courses on
Data Types
Show steps
-
Find a set of sample problems online or in a textbook.
-
Read and understand the problem statement.
-
Write code to solve the problem.
-
Test your code to make sure it works.
-
Review your code and identify any areas for improvement.
Follow a tutorial on using a data science library
Show steps
Get hands-on experience with a data science library to enhance your understanding of its functionality.
Browse courses on
Data Science Libraries
Show steps
-
Choose a data science library that you want to learn.
-
Find a tutorial on using the library.
-
Follow the tutorial step-by-step.
-
Complete the exercises in the tutorial.
-
Apply what you have learned to a small data science project.
Six other activities
Expand to see all activities and additional details
Show all nine activities
Explore Practical Use Cases of AI
Show steps
Explore practical examples of AI applications to reinforce understanding and identify potential career opportunities.
Show steps
-
Identify industries using AI
-
Research case studies in different domains (e.g., healthcare, finance)
-
Analyze how AI solves real-world problems
Attend a data science meetup
Show steps
Connect with other data scientists to learn about new trends and opportunities in the field.
Browse courses on
Networking
Show steps
-
Find a data science meetup in your area.
-
Register for the meetup.
-
Attend the meetup.
-
Network with other data scientists.
-
Learn about new trends and opportunities in the field.
Build a Simple Machine Learning Model
Show steps
Gain hands-on experience in implementing a machine learning model to strengthen your understanding of concepts.
Browse courses on
Machine Learning Algorithms
Show steps
-
Choose a dataset and define the problem
-
Select a suitable algorithm (e.g., linear regression, decision tree)
-
Train and evaluate the model
Participate in a data science workshop
Show steps
Enhance your data science skills through hands-on learning in a structured environment.
Show steps
-
Find a data science workshop that you are interested in.
-
Register for the workshop.
-
Attend the workshop.
-
Participate in the workshop activities.
-
Learn new data science skills.
Build a simple data visualization using a data science library
Show steps
Create a visual representation of data to improve your understanding of data analysis and communication.
Browse courses on
Data Visualization
Show steps
-
Choose a dataset that you want to visualize.
-
Select a data visualization library that you want to use.
-
Create a data visualization using the library.
-
Interpret the data visualization.
-
Share your data visualization with others.
Write a blog post or article on a data science topic
Show steps
Enhance your understanding of data science concepts by explaining them to others through writing.
Browse courses on
Data Science
Show steps
-
Choose a data science topic that you are interested in.
-
Research the topic.
-
Write a blog post or article on the topic.
-
Publish your blog post or article.
-
Promote your blog post or article.
Read a book on data science fundamentals
Show steps
Gain a comprehensive understanding of data science concepts through a structured and in-depth resource.
View
Artificial Intelligence For Dummies (For...
on Amazon
Show steps
Show steps
Show steps
- Purchase or borrow the book.
- Set aside time each day to read the book.
- Take notes as you read.
- Complete the exercises in the book.
- Discuss the book with a friend or colleague.
Solve sample problems on elementary data types and operations
Show steps
Practice writing code to reinforce your understanding of basic data science concepts.
Browse courses on
Data Types
Show steps
Show steps
Show steps
- Find a set of sample problems online or in a textbook.
- Read and understand the problem statement.
- Write code to solve the problem.
- Test your code to make sure it works.
- Review your code and identify any areas for improvement.
Follow a tutorial on using a data science library
Show steps
Get hands-on experience with a data science library to enhance your understanding of its functionality.
Browse courses on
Data Science Libraries
Show steps
Show steps
Show steps
- Choose a data science library that you want to learn.
- Find a tutorial on using the library.
- Follow the tutorial step-by-step.
- Complete the exercises in the tutorial.
- Apply what you have learned to a small data science project.
Six other activities
Expand to see all activities and additional details
Show all nine activities
Explore Practical Use Cases of AI
Show steps
Explore practical examples of AI applications to reinforce understanding and identify potential career opportunities.
Show steps
Show steps
Show steps
- Identify industries using AI
- Research case studies in different domains (e.g., healthcare, finance)
- Analyze how AI solves real-world problems
Attend a data science meetup
Show steps
Connect with other data scientists to learn about new trends and opportunities in the field.
Browse courses on
Networking
Show steps
Show steps
Show steps
- Find a data science meetup in your area.
- Register for the meetup.
- Attend the meetup.
- Network with other data scientists.
- Learn about new trends and opportunities in the field.
Build a Simple Machine Learning Model
Show steps
Gain hands-on experience in implementing a machine learning model to strengthen your understanding of concepts.
Browse courses on
Machine Learning Algorithms
Show steps
Show steps
Show steps
- Choose a dataset and define the problem
- Select a suitable algorithm (e.g., linear regression, decision tree)
- Train and evaluate the model
Participate in a data science workshop
Show steps
Enhance your data science skills through hands-on learning in a structured environment.
Show steps
Show steps
Show steps
- Find a data science workshop that you are interested in.
- Register for the workshop.
- Attend the workshop.
- Participate in the workshop activities.
- Learn new data science skills.
Build a simple data visualization using a data science library
Show steps
Create a visual representation of data to improve your understanding of data analysis and communication.
Browse courses on
Data Visualization
Show steps
Show steps
Show steps
- Choose a dataset that you want to visualize.
- Select a data visualization library that you want to use.
- Create a data visualization using the library.
- Interpret the data visualization.
- Share your data visualization with others.
Write a blog post or article on a data science topic
Show steps
Enhance your understanding of data science concepts by explaining them to others through writing.
Browse courses on
Data Science
Show steps
Show steps
Show steps
- Choose a data science topic that you are interested in.
- Research the topic.
- Write a blog post or article on the topic.
- Publish your blog post or article.
- Promote your blog post or article.
Career center
Learners who complete Data Science Basics will develop knowledge and skills
that may be useful to these careers:
For more career information including salaries, visit:
OpenCourser.com/course/b7j1hx/data
Reading list
We've selected ten 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
Data Science Basics.
This engaging and accessible book provides a comprehensive overview of machine learning, from its historical roots to its current state-of-the-art techniques. It is written by a leading expert in the field and offers valuable insights into the challenges and opportunities of machine learning.
This advanced textbook provides a comprehensive overview of statistical learning methods. It covers a wide range of topics, including linear and nonlinear regression, classification, and clustering. It is highly regarded in academic and professional circles and is suitable for advanced learners and practitioners.
Save
This beginner-friendly guide provides a comprehensive overview of data science, from its history and foundational concepts to practical applications and tools. It offers a solid foundation for understanding the field and its potential impact on various aspects of life and society.
This practical guide provides a comprehensive overview of deep learning using Python. It covers essential concepts, architectures, and applications, making it a valuable resource for learners who want to build a strong foundation in deep learning.
Save
This comprehensive textbook covers the fundamental concepts and algorithms of deep learning, a powerful technique used in AI and machine learning. It provides a rigorous understanding of the field and is suitable for advanced learners and professionals.
This comprehensive textbook provides a deep dive into Bayesian data analysis, a powerful statistical approach that incorporates uncertainty into its models. It covers foundational concepts, algorithms, and applications, making it a valuable resource for advanced learners and practitioners in various fields.
This practical guide provides a hands-on introduction to machine learning using popular Python libraries such as Scikit-Learn, Keras, and TensorFlow. It covers a wide range of topics, including data preprocessing, feature engineering, model selection, and evaluation.
This practical guide provides a comprehensive overview of Python, a popular programming language widely used in data science. It covers essential concepts, libraries, and tools, enabling learners to apply Python to real-world data analysis tasks.
This practical guide explores the concept of interpretable machine learning, which aims to make machine learning models more understandable and actionable. It covers techniques and best practices for building interpretable models and assessing their performance.
Save
This visually engaging guide provides a comprehensive overview of data visualization, covering principles, techniques, and best practices. It offers practical insights into how to effectively communicate data and insights through visual representations.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/b7j1hx/data
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