Data science - Intermediate Level

Difficulty: ⭐️⭐️⭐️☆☆

Description

This course is a fast track to practical data science – a rapidly growing field in computer science and engineering in recent years. The course focus on realizing and understanding some technical skills to analyze a typical dataset. 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.

Learning outcomes:

By the end of this course, students will achieve:

  • Understanding of data structure.
  • Intermediate programming with Python for Data Scientist.
  • Skills to read, write, analyze any dataset.
  • Skills to detect and handle missing data and outliers. Skills to visualize dataset for report, analysis and communication.
  • Skills to apply simple machine learning model in dataset

Requirements

Curriculum For This Course

Lesson 1:

      1. Introduction to data science
      2. Basic programming in Python.
        1. Comment
        2. print() command
        3. Basic data types
        4. Basic operators
        5. Basic control flow: if statement
        6. Variables and Variable Types
        7. Basic String manipulation
      3. Basic control flow: for loop
      4. Practice exercises

Lesson 2:

      1. Complex data structures in Python:
        1. List
        2. Dictionary
        3. Tuple
      2. Advanced data structure in Python:
        1. Numpy Array
        2. Pandas DataFrame.
      3. Practice exercises

Lesson 3:

      1. Basic Data Visualization
      2. Matplotlib library
        1. Figure function
        2. Plot function
        3. Show function
      3. Scatter Plot, Line Plot, Bar Plot
      4. Implementation of those plots with Python/R
      5. Practice exercises

Lesson 4:

      1. Intermediate Data Visualization
      2. Bubble Plot, Box plot, Histogram, Multiple Plots
      3. Implementation of those plots with Python/R
      4. Practice exercises

Lesson 5:

      1. Advanced Data Visualization
      2. 3D plots
      3. Animated plots
      4. Practice exercises

Mini project:

      1. Pick a dataset out of 3 themes: Sport, Real Estate, Academics
      2. 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
      3. Summarize insights from the dataset

Lesson 7:

      1. Introduction to Data Preprocessing
      2. Detection of Missing Data, Outliers
      3. How to treat missing data and outliers
      4. Implementation of data preprocessing with Python/R
      5. Practice exercises

Lesson 8:

      1. Introduction to Data Collection
      2. Example of famous and study datasets.
      3. Introduction to basic data scraping.
      4. Implementation of data scraping with Python
      5. Practice exercises

Lesson 9:

      1. Introduction to Text Mining
      2. Introduction to Sentiment Analysis
      3. API to download from social media
      4. Processing text data to predict Sentiment
      5. Implementation of downloading social media data with Python/R
      6. Implementation of sentiment analysis with Python/R
      7. Practice exercises

Lesson 10:

      1. Introduction to clustering method
      2. K-means clustering algorithm
      3. Implementation of k-means clustering algorithm from scratch
      4. Implementation of k-means clustering with sklearn library
      5. Practice exercises

Lesson 11:

      1. Introduction to Association Rule  – Market Basket Analysis
      2. Metrics in Association Rule:
        1. Lift
        2. Support
        3. Confidence
      3. Implementation of Association Rule with Python/R
      4. Practice exercises

Completion project

        1. Pick a dataset from our dataset bank
        2. Using learned knowledge to perform analysis on the dataset.
        3. Write a report for insights found.

UPCOMING COURSES/WORKSHOPS

Group name Start date Session duration Number of sessions Standard price

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FEATURED MENTOR

Tokyo Techies Lecturer

Phong Nguyen

Head of Artificial Intelligence and Data Science Department

  • Data Science and Research Mentor at Tokyo Techies
  • Master Graduate from Carnegie Mellon University.
  • Researcher in an AI lab at a big global corporation.
  • Global working experience in Singapore, Australia, US, Vietnam and Japan.
  • A results-oriented Researcher and Data Guru with vast managerial and technical experience in financial management, marketing, business planning.
  • Adapt at programming in multiple different languages, such as Java, Python and R.