ABOUT THE COURSE!
This Introduction to Data Science course aims at providing learners with an overview of Data Science and its core concepts. Particularly, Data Science professionals will introduce definition and functions of Data Science as well as its tools and algorithm applied on our daily basis. Learners also have a chance to explore what skills they need to master to pursue a career in this field. Learners will learn about qualities that distinguish Data Science from other professionals. More importantly, learners will learn about analytics and vital roles of data scientists in this process as well as about story-telling and the importance of an effective final deliverable.
To begin the course, let's take a few minutes to explore the course site. Review the material we’ll cover each week, and preview the assignments/projects/quizzes you’ll need to complete to pass the course.
Main concepts are delivered through videos, demos and hands-on exercises.
|Course name:||Data Science|
|Estimated Time:||6 weeks. Student should allocate at average of 2 hours/a day to complete the course.|
- Understand the basic concepts of Data Science
- Interpret Data Science Topics
- Acknowledge the application of Data Science
- Comprehend and Practice with tool for data science
- Understand the methodology used in data science, steps to solve data science problems from the problem, collecting and analyzing data, building algorithms and understanding feedback after the algorithm is installed put and use
- Understand the basic concepts of descriptive statistics and probability
Module 1: What is Data Science?
- Lesson 1: Defining Data Science and What Data Scientists Do
- Lesson 2: Data Science Topics
- Lesson 3: Data Science in Business
- Lesson 4: Introducing Jupyter Notebooks
Module 2: Data Science Methodology
- Lesson 5: From Problem to Approach
- Lesson 6: From Requirements to Collection
- Lesson 7: From Understanding to Preparation
- Lesson 8: From Modeling to Evaluation
- Lesson 9: From Deployment to Feedback
Module 3: Statistics & Probability
- Lesson 10: Descriptive statistics
- Lesson 11: Correlation and Regression
- Lesson 12: Probability
- Lesson 13: Probability Distributions
Module 4: Python for Data Science
- Lesson 14: Python Basics with Data Structures
- Lesson 15: Python Advance with OOP and API
- Lesson 16: Numpy in Python
- Lesson 17: Working with data and Pandas
M.S Vu Thuong Huyen
Ph.D. Tran Hong Viet
Ph.D. Dang Hoang Vu
Assoc. Prof. Tu Minh Phuong
Ph.D. Nguyen Van Vinh
Ph.D. Tran The Trung