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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 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.


  • 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 4Python 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

  • 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: 

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…


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 &
  • 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: CourseratutorialspointedX TrainingUdemy or Standford.

     Feedback channel

    FUNiX is ready to receive and discuss all comments and feedback related to learning materials via email