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Data Science & AI
Software Development
Computer Science
Data Science & AI
Software Development
Computer science

Data Science – Beginner Level

This course introduces learners to the fundamental understanding of data science – a rapidly growing field in Computer Science and engineering in recent years. Through the theoretical lectures and hands-on practices, students will learn how to scrape data from public sources, visualize and analyze data, and apply statistical modeling and machine learning algorithms to predict future outcomes of academic and business data.

Difficulty

Learning outcomes

  • Knowledge of basic data science pipeline
  • Ability to collect data from the Internet
  • Ability to read, write, analyze a simple dataset.
  • Ability to detect and handle missing data and outliers.
  • Ability to present data science research in an easy-to-consume way.

Requirements

  • Python programming

Upcoming Courses/Workshops

*The below course structure and cost is intended for individuals. To know the details of our course offering for companies, please contact us.

Register for a FREE Trial Class Register for a 1-on-1 class Course for companies

FEATURED MENTOR

Nghia Truong

Head of AI Product Development
  • Machine Learning consultant and mentor at Tokyo Techies
  • 6+ years of experience working at software service development companies in Japan.
  • 3rd place Vietnam National Olympiad in Informatics 2011
  • Rank 27 at ACM/ICPC Asia Regional contest 2012
  • Champion at multiple Hackathons in Japan and Vietnam
Lesson 1

a. Introduction to Data Science

b. Data science pipeline

  • Data collection
  • Data cleansing
  • Analysis
  • Presentation

c. Data science toolkit

  • Python
  • Anaconda
  • Jupyter notebook

d. Exercises: Basic Python programming exercises

Lesson 2

a. Presentation on Jupyter Notebook with Markdown and LaTeX

  • Mention: RISE

b. Practice: Markdown, LaTeX

Lesson 3

a. Mathematical computation with Numpy

b. Practice exercises

Lesson 4

a. Tabular data manipulation with Pandas

b. Practice exercises

Lesson 5

a. Visualization with Matplotlib

b. Practice exercises

Lesson 6

Mini project:

a. Pick a dataset out of 3 themes: Sport, Real Estate, Academics

b. Exploring and Analyzing the dataset using data visualization techniques:

  1. Scatter Plot
  2. Line Plot
  3. Bar Plot
  4. Bubble Plot
  5. Box Plot
  6. Histogram

c. Summarize insights from the dataset

Lesson 7

a. Data collection

b. Practice exercises

Lesson 8

a. Basic data cleansing

b. Practice exercises

Lesson 9

a. Drawing insights from data

  1. Lurking variable
  2. Correlation
  3. Outlier

b. Practice exercises

Lesson 10

a. Introduction to Association Rule  - Market Basket Analysis

b. Metrics in Association Rule:

  1. Lift
  2. Support
  3. Confidence

c. Implementation of Association Rule with Python/R

d. Practice exercises

Lesson 11

a. Making a scientific presentation

b. Practice

Lesson 12

Completion project

a. Introduction to Kaggle

b. Join a competition

Recommended after this course

Data Science – Intermediate Level

Learn programming with Python like a Data Scientist. Acquire fundamental skills to read, write, analyze, and apply simple machine learning in dataset.

Difficulty
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