Introduction to Data Science

Enrollment in this course is by invitation only

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 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: Python Advance with OOP and API
Lesson 16: Numpy in Python
Lesson 17: Working with data and Pandas

DEVELOPMENT TEAM

COURSE DESIGNERS

M.S Vu Thuong Huyen

Data Scientist at FPT Software Company Limited – a subsidiary of FPT Corporation
Master of Software engineering, VNU University of Engineering and Technology
Bachelor of Engineering, School of Applied Mathematics and Informatics, Hanoi University of Science and Technology
Research fields: Machine learning, Deep learning, Reinforcement Learning, Natural Language Processing…
Profile online: https://www.linkedin.com/in/thuong-huyen-3969747a/

Ph.D. Tran Hong Viet

Ph.D. in Computer Science, Teacher in Faculty of Information Technology, University of Economics and Technical Industries since 2002
Master degree in Hanoi University of Science and Technology in 2006
Ph.D. dissertation defended in VNU University of Engineering and Technology in 2019, after years of study about Machine Translation, Natural Language Processing
Internship at National Institute of Informatic in 2014, Internship at Japan Advanced Institute of Science and Technology in 2017…

REVIEWERS

Course Reviewer

Ph.D. Dang Hoang Vu

FPT Science Director
Ph.D. in Mathematics, University of Cambridge
Core member of R&D activities in FPT Corporation
Main responsibility in analytics side of FPT’s Data Management Platform and data science research

Program Reviewers

Assoc. Prof. Tu Minh Phuong

Ph.D. Nguyen Van Vinh

Ph.D. Tran The Trung

Dean of IT Faculty, Posts and
Telecommunications Institute of Technology (PTIT)
Expert & technological consultant in AI & machine learning
Head of Machine Learning &
Application laboratory in PTIT

Lecturer & core member of AI Lab, University of Technology - VNU
AI expert & consultant for DPS, Fsoft
Ph.D. in Computer Science, Japan Advanced Institute of Science &
Technology
Bachelor’s degree in IT, University of Science, VNU

Director of FPT Technology
Research Institute, FPT University
Ph.D. in Computational Physics, UVSQ Université de Versailles Saint-Quentin-en-Yvelines
M.S. in Astrophysics, Pierre & Marie Curie University
B.S. in Theoretical & Mathematical Physics, University of Melbourne

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, edX Training, Udemy 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