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Introduction to Data Science

Enrollment in this course is by invitation only

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 4Python 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

  • 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, Lecturer in Faculty of Information Technology, University of Economics and Technical Industries since 2002
  • Master degree in Hanoi University of Technology in 2006
  • Ph.D. dissertation defended in UET, VNU Hanoi 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 REVIEWERS

Assoc. Prof. Tu Minh Phuong   Ph.D. Dang Hoang Vu  
  • 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
  • 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

 

Ph.D. Nguyen Van Vinh   Ph.D. Tran The Trung  
  • Lecturer & core member of AI Lab,
    University of Technology, Vietnam
    National University (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 and
    Marie Curie University
  • B.S. in Theoretical & Mathematical
    Physics, University of Melbourne




NGUỒN HỌC LIỆU

Trong thời đại hiện nay, mỗi môn học đều có nhiều nguồn tài liệu liên quan kể cả sách in và online, FUNiX Way không quy định một nguồn học liệu cụ thể mà khuyến cáo để học viên chọn được nguồn phù hợp nhất cho mình. Trong quá trình học từ nhiều nguồn khác nhau theo lựa chọn cá nhân đó, khi sinh viên phát sinh câu hỏi thì sẽ được kết nối nhanh nhất với mentor để được giải đáp. Toàn bộ phần đánh giá bao gồm các câu hỏi trắc nghiệm, bài tập, dự án và thi vấn đáp do FUNiX thiết kế, xây dựng và thực hiện.

Các môn học của FUNiX không quy định bắt buộc tài liệu học tập, sinh viên có thể chủ động tìm và học từ bất kỳ nguồn nào phù hợp, kể cả sách in hay nguồn học liệu online (MOOC) hay các website. Việc sử dụng các nguồn đó do học viên chịu trách nghiệm và đảm bảo tuân thủ các chính sách của chủ sở hữu nguồn, trừ trường hợp họ có sự hợp tác chính thức với FUNiX. Nếu cần hỗ trợ, học viên có thể liên hệ phòng đào tạo FUNiX để được hướng dẫn.

Dưới đây là một số nguồn học liệu của môn học mà học viên có thể tham khảo sử dụng. Việc liệt kê nguồn dưới đây không nhất thiết hàm ý rằng FUNiX có sự hợp tác chính thức với chủ sở hữu của nguồn: Coursera.org


Kênh phản hồi

FUNiX sẵn sàng đón nhận và trao đổi về mọi ý kiến góp ý, phản hồi liên quan đến học liệu qua email program@funix.edu.vn