Data Science Fundamentals
  • 4.8 (12Reviews)
 

Data Science Fundamentals

Learn the foundations of data science and how it’s applied to a range of fields.

  • 4.8 (12)
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Data Science Fundamentals

Learn the foundations of data science and how it’s applied to a range of fields.

  • 4.8 (12 Reviews)
 
  • 30-day money-back guarantee

    Not satisfied?

    Reach out to our customer support team within 30 days to receive a full refund.

  • Unlimited access, anywhere, anytime

  • Learn from hand-vetted instructors, experts in their field

This badge will appear on your Fiverr profile upon completion, showcasing your expertise

Course Syllabus

Chapter 1
Chapter 1: Defining Data Science
Chapter 2
Chapter 2: Data Science Life Cycle
  • 3. What is the Data Science Life Cycle? (0:55)
  • Chapter 2 Quiz
Chapter 3
Chapter 3: Data Design
  • 4. Probability Sampling (3:49)
  • Chapter 3 Quiz
Chapter 4
Chapter 4:Computational Tools
  • 5. Python vs. R (2:15)
  • 6. Set Up Environment: Jupyter (3:44)
  • Chapter 4 Quiz
Chapter 5
Chapter 5: Tabular Data
  • 7. What Is Tabular Data? (2:41)
  • 8. Reading Tabular Data (10:45)
  • 9. Gathering Insights (6:06)
  • 10. Answering Specific Questions (2:52)
  • Chapter 5 Quiz
Chapter 6
Chapter 6: Exploratory Data Analysis
  • 11.What is Exploratory Data Analysis? (0:52)
  • 12. Statistical Data Types (3:08)
  • 13. Properties Data (6:22)
  • Chapter 6 Quiz
Chapter 7
Chapter 7: Data Cleaning
  • 14. What Is Data Cleaning? (1:26)
  • 15. Questions To Ask Before Cleaning (6:24)
  • Chapter 7 Quiz
Chapter 8
Chapter 8: Data Visualization
  • 16. What Is Data Visualization? (1:00)
  • 17. Visualize Quantitative Data (5:44)
  • 18. Visualize Qualitative Data (8:14)
  • Chapter 8
Chapter 9
Chapter 9: Inference
  • 19. What Is Inference? (0:51)
  • 20. Design A Hypothesis Test (6:38)
  • 21. Conduct A Permutation Test (13:25)
  • 22. Bootstrap A Confidence Interval (9:50)
  • Chapter 9 Quiz
Chapter 10
Chapter 10: Classification
  • 23. What Is Classification? (2:18)
  • 24. Intro To K-Nearest Neighbor Algorithm (2:55)
  • Chapter 10 Quiz
Chapter 11
Chapter 11: Conclusion
  • 25. Next Steps
Chapter 12
Final Quiz
  • Final Quiz
Lavanya Vijayan

Coding instructor at the Coder School in Berkeley, who is passionate about STEM education and diversity

After Completing This Course You Will Be Able To
  • Understand exactly what data science is and its real world applications
  • Identify each stage of the data science life cycle and understand the main goals of each stage
  • Understand data design and know how sampling reduces bias
  • Know the key differences between Python and R
  • Familiarize yourself with and set up Jupyter on your computer
  • Interact confidently with and read in tabular data
  • Understand what exploratory data analysis is
  • Understand what data cleaning is and how it is used as well as the main questions to ask before cleaning
  • Understand what data visualization is and how its used in data science as well as be prepared to visualize quantitative data
  • Solve complex questions by bootstrapping your confidence interval.
  • Know what the k-Nearest Neighbor algorithm is and how to use it to classify data
About This Course

Data sets in the right hands can help predict and shape the future. One of the fastest-growing fields, data science has vast and powerful applications in a range of different areas. In this course, you’ll learn essential definitions and gain an understanding of the tools that are fundamental to data science.

Advance your career by enrolling now to grab hold of the myriad of opportunities that mastering data science offers.

This course was created by Madecraft. We are pleased to offer this course in our library.

About This Course

Data sets in the right hands can help predict and shape the future. One of the fastest-growing fields, data science has vast and powerful applications in a range of different areas. In this course, you’ll learn essential definitions and gain an understanding of the tools that are fundamental to data science.

Advance your career by enrolling now to grab hold of the myriad of opportunities that mastering data science offers.

This course was created by Madecraft. We are pleased to offer this course in our library.

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What You Will Learn

  • The difference between data scientists, data engineers, statisticians and business analysts
  • About the impact of data science on business and industry
  • The workings of each stage of the data science life cycle including formulating a question, acquiring and cleaning data, conducting exploratory analyses and drawing conclusions
  • About two of the most popular computing languages for data science - Python and R
  • The different statistical data types that exist
  • How to gather insights from your dataset
  • How to evaluate what questions you want to answer and what types of questions are ideal for your scenario
  • How to Describe what inference is, how it's used and be ready to tackle hypothesis testing and permutation testing
  • How to describe classification and explain how it is used

Why learn with Lavanya Vijayan?

An instructor at the Coder School in Berkeley and former instructor of First Code Academy, she is an officer of the Society of Women Engineers and teaches students in under-resourced communities the skills they need to achieve their dreams.

Who Is This Course For?

  • People interested in becoming data scientists or wanting an introduction to the world of data science
  • Freelancers wanting to learn the foundations of data science to improve their career opportunities
  • Business owners wanting to learn how to gather insights from their dataset
  • Students thinking of pursuing an education in data science
  • Employees considering becoming freelancers in the data science arena
  • Anyone wanting to gain insight into the data science life cycle
  • Beginners

Requirements

  • Access to Internet
  • Computer / Laptop / Mobile Device

What Is Included?

  • Immediate unlimited access to course materials
  • 30-day money-back guarantee
  • Exercises and quizzes to help you put theory into practice
  • English Closed Captions
  • Suitable for mobile or desktop
  • A badge to showcase your expertise on your profile page upon completion
12 Reviews
  • 4.8

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Course Syllabus

Chapter 1
Chapter 1: Defining Data Science
Chapter 2
Chapter 2: Data Science Life Cycle
  • 3. What is the Data Science Life Cycle? (0:55)
  • Chapter 2 Quiz
Chapter 3
Chapter 3: Data Design
  • 4. Probability Sampling (3:49)
  • Chapter 3 Quiz
Chapter 4
Chapter 4:Computational Tools
  • 5. Python vs. R (2:15)
  • 6. Set Up Environment: Jupyter (3:44)
  • Chapter 4 Quiz
Chapter 5
Chapter 5: Tabular Data
  • 7. What Is Tabular Data? (2:41)
  • 8. Reading Tabular Data (10:45)
  • 9. Gathering Insights (6:06)
  • 10. Answering Specific Questions (2:52)
  • Chapter 5 Quiz
Chapter 6
Chapter 6: Exploratory Data Analysis
  • 11.What is Exploratory Data Analysis? (0:52)
  • 12. Statistical Data Types (3:08)
  • 13. Properties Data (6:22)
  • Chapter 6 Quiz
Chapter 7
Chapter 7: Data Cleaning
  • 14. What Is Data Cleaning? (1:26)
  • 15. Questions To Ask Before Cleaning (6:24)
  • Chapter 7 Quiz
Chapter 8
Chapter 8: Data Visualization
  • 16. What Is Data Visualization? (1:00)
  • 17. Visualize Quantitative Data (5:44)
  • 18. Visualize Qualitative Data (8:14)
  • Chapter 8
Chapter 9
Chapter 9: Inference
  • 19. What Is Inference? (0:51)
  • 20. Design A Hypothesis Test (6:38)
  • 21. Conduct A Permutation Test (13:25)
  • 22. Bootstrap A Confidence Interval (9:50)
  • Chapter 9 Quiz
Chapter 10
Chapter 10: Classification
  • 23. What Is Classification? (2:18)
  • 24. Intro To K-Nearest Neighbor Algorithm (2:55)
  • Chapter 10 Quiz
Chapter 11
Chapter 11: Conclusion
  • 25. Next Steps
Chapter 12
Final Quiz
  • Final Quiz
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Data Science Fundamentals
4.8 (12)

Sample lesson

2. Why Data Science? (1:43)
This lesson can help you know the course from the inside.