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Python For Data Science

Date: On Demand

Duration: 2 Days

Apply Python as a data science tool for programming and business analysis. Learn the best practices of data mining and analytics with this course in Singapore.

Course Overview

In an age where data is ubiquitous, it is critical to be well-versed in tools that will allow us to extract useful insights, decisions and products from the data that we collect. Python, with its wide array of libraries streamlining each part of the data science process, is an essential part of our quantitative toolkit. Building upon a review of basic Python syntax, this course focuses on how we can better work with, and make use of, data using Python, from cleaning messy datasets, exploring our data by way of visualisations, and setting up machine learning models. It should also be mentioned that Python is the no 1 programing language for DS.

Course Objectives

New libraries for data manipulation, visualization and data modeling have made Python an increasingly exciting alternative to R as a data science language.
This programme aims to quickly bring up to speed a programmer or business analyst who already knows how to programme in Python to begin using Python as a data science tool.
The programme will define data science and explore the first two things a data scientist must do – cleaning and visualizing data. It will then cover the Data Science Workflow – training models and testing them through the application of machine learning models to various industry-relevant data science problems. The tools used will be including but not limited to Pandas, Scikit-learn and Keras.

Learning Outcomes

At the end of the course, participants will be able to:

  • Use Python for basic data munging to aggregate, clean and process data from local files, databases, and online
  • Create visualisation with Matplotlib, Pandas.plot, and Seaborn
  • Create basic to intermediate analytics models with Python/Sckit-learn
  • Using the above tools within the context of solving essential data science problems
  • Applying Python tools to import data from various sources, explore them, analyse them, learn from them, visualise them, and share them

Topics

  • Python Basics (I): Python Environments
    • Python statement and operation
    • Variable Assignment
    • Functions and Classes
  • Python Basics (II)
    • Lists and Dictionaries
    • Conditional and looping statement
    • File Input/Output
    • Managing Python Environments and Packages
  • Working with Data Sources
    • Reading CSV
    • Web Scraping
    • Interacting with local and remote databases (ODBC)
    • Reading from HTML
  • Mini-Project: Making a Data Product with Python and Jupyter
  • Data Exploration and Wrangling
    • Series/Data frame
    • Data cleaning
    • Data analytics e.g., Descriptive statistics using Python
  • Data Visualization with the matplotlib
    • Basic visualization technique
    • Creating visualization tools using matplotlib
  • Introduction to key Data Science
    • Data analytics process: Supervised and Unsupervised Learning
    • Regression and Classification using Sci-kit Learn
  • Mini-Project (and/or) Recap: Creating data visualization and data analytics product

Who Should Attend

Business/Data Analysts, Programmers, Executives

Prerequisites

Must be familiar with the Python programming language, or have attended the Introduction to Python training and statistics 101 at a pre-university level. 

Software Application

Anaconda for Windows / MacOS.

Course Convener

(Click their photos to view their short biographies)

ccccc Danny Poo

Assoc Prof Danny PooAssoc Prof Danny Poo

ddddd Ai Xin

Dr Ai XinDr Ai Xin

ddddd Edmund Low

Dr Edmund LowDr Edmund Low

FAQ

Qns: Is there a preferred platform, and what software do I need to install?
Ans: Both Windows and MacOS are fine. We will be using Anaconda. Installation instructions will provided in the course materials ahead of the online class.

Qns: What background is required for the course?
Ans: Some knowledge of simple programming concepts, e.g. variables, loops, will be preferable. Part of the course will recap the basics of Python, so participants without prior knowledge of the language will be able to take it as well.

Qns: What assessment is expected for the course?
Ans: Participants will complete a short project involving some Python coding for successful completion

Course Fees

Singapore Citizens
39 years old or younger
40 years old or older
Singapore PRs
Enhanced Training Support for SMEs
International Participants

Total Nett Programme Fee Payable, Including GST, after additional funding from the various funding schemes

Participants must fulfill at least 75% attendance and pass all assessment components to be eligible for SSG funding.

NTUC members enjoy 50% unfunded course fee support for up to $250 each year (or up to $500 for NTUC members aged 40 years old and above) when you sign up for courses supported under UTAP (Union Training Assistance Programme). Please visit e2i’s website to find out more.

To enquire, email soc-ace@nus.edu.sg

To register, click Register

Course Code:
TSG 2020501975 (Classroom Learning)
TSG-2021006841 (Synchronous e-learning)

Course Fee Breakdown

Singapore Citizens

Singapore Citizens

39 years old or younger

Singapore Citizen

40 years old or older
Singapore PRs
Enhanced Training Support for SMEs
International Participants