Introduction to Data Science
ABOUT THE COURSE!
Data Science is the future of Artificial Intelligence (AI). Its growing importance in different fields of work has enabled enterprises and organizations to address and fight off global challenges with increasing impacts across countries. For this rationale, it is essential to develop a rich source of skillful and professional data scientists for burgeoning demand of the job market.
This first course of Introduction to Data Science 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 INFORMATION
Course code: | DSP301x |
Course name: | Data Science |
Credits: | 3 |
Estimated Time: | 6 weeks. Student should allocate at average of 2 hours/a day to complete the course. |
COURSE OBJECTIVES
- Understand the role of Data Science and Data Scientist. It is very important to understand what Data Science is and how it can add value to your business.
- Understand Data Science Methodology, how to apply a methodology that can be used within data science, to ensure that the data used in problem solving.
- Understand Statistics & Probability which are necessary for data science and data scientist.
- Practice with Python for data science, as well as programming in general.
COURSE STRUCTURE
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: Conditional probability
Module 4: Python for Data Science
- Lesson 14: Python Basics
- Lesson 15: Python Data Structures with List and Tuples
- Lesson 16: Python Data Structures with Sets and Dictionaries
- Lesson 17: Conditions, Branching and Loop
- Lesson 18: Reading Files, Writing Files and Padas in Python
- Lesson 19: Using Numpy in Python
- Lesson 20: Classes
- Lesson 21: Inheritance
DEVELOPMENT TEAM
COURSE DESIGNERS
M.S. Vu Thuong Huyen
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Ph.D. Tran Hong Viet
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COURSE REVIEWERS
Assoc. Prof. Tu Minh Phuong | Ph.D. Dang Hoang Vu | ||
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Ph.D. Nguyen Van Vinh | Ph.D. Tran The Trung | ||
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