Introduction to Data Science
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 the definition and functions of Data Science as well as its tools and algorithms 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 storytelling 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 handson 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 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
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: Probability Distributions
Module 4: Python for Data Science
 Lesson 14: Python Basics with Data Structures
 Lesson 15: Numpy in Python
 Lesson 16: Working with data and Pandas
DEVELOPMENT TEAM
COURSE DESIGNERS
M.S Vu Thuong Huyen

Ph.D. Tran Hong Viet

REVIEWERS
Course Reviewer
Ph.D. Dang Hoang Vu 

Assoc. Prof. Tu Minh Phuong 
Ph.D. Nguyen Van Vinh 
Ph.D. Tran The Trung 



Learning resources
In modern times, each subject has numerous relevant studying materials including printed and online books. FUNiX Way does not provide a specific learning resource but offers recommendation for students to choose the most appropriate source to them. In the process of studying from many different sources based on that personal choice, students will be timely connected to a mentor to respond to their questions. All the assessments including multiple choice questions, exercises, projects and oral exams are designed, developed and conducted by FUNiX.
Learners are under no obligation to choose a fixed learning material. They are encouraged to actively find and study from any appropriate sources including printed textbooks, MOOCs or websites. Students are on their own responsibilities in using these learning sources and ensuring full compliance with the source owners’ policies; except for the case in which they have an official cooperation with FUNiX. For further support, feel free to contact FUNiX Academic Department for detailed instructions.
Learning resources are recommended below. It should be noted that listing these learning sources does not necessarily imply that FUNiX has an official partnership with the source’s owner: Coursera, tutorialspoint, Training, or Standford.
Feedback channel
FUNiX is ready to receive and discuss all comments and feedback related to learning materials via email program@funix.edu.vn