Python For Data Science
Date: 25 Apr, 26 Apr 2023 | 9am-5.30pm | Online
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)
Assoc Prof Danny Poo
Assoc Prof Danny Poo
Assoc Prof Danny Poo brings with him 35 years of Software Engineering and Information Technology and Management experience. A graduate from the University of Manchester Institute of Science and Technology (UMIST), England, Dr Poo is currently an Associate Professor at the Department of Information Systems and Analytics, National University of Singapore. Prior to joining the University, Dr Poo was with the System Operations at DBSBank, Singapore.
A Steering Committee member of the Asia-Pacific Software Engineering Conference, Dr Poo is actively involved in Information Management and Healthcare Analytics research. A well-known speaker in seminars, Dr Poo has conducted numerous in-house training and consultancy for organizations, both locally and regionally. Dr Poo is the author of 5 books on Object-Oriented Software Engineering, Java Programming language and Enterprise JavaBeans.
Dr Poo notable teaching credentials include:
- Data Strategy
- Data StoryTelling
- Data Visualisation
- Data Analytics
- Machine Learning
- Data Management
- Data Governance
- Data Architecture
- Capstone Projects for Business Analytics
- Software Engineering
- Server-side Systems Design and Development
- Information Technology Project Management
- Health Informatics
- Healthcare Analytics
- Health Informatics Leadership.
Industry Credentials
- Deutsche Bank
- Gemplus
- Micron
- NCR
- PIL
- PSA
- Rhode-Schwarz
- Standard Chartered Bank
- ST Electronic
- Monetary Authority of Singapore
- Infocomm Development Authority
- National Library Board
- Ministry of Manpower
- Nanyang Technological University
- Nanyang Polytechnic
- National University Hospital.
Dr Ai Xin
Dr Ai Xin
Dr Ai Xin is currently a Lecturer with the School of Computing at the National University of Singapore (NUS). She has many years’ experience on teaching Artificial Intelligence and Data Science courses, e.g. machine learning, deep learning, data mining and etc.
She graduated from NUS with a PhD degree on Electrical and Computer Engineering. Her research focused on Game Theoretical Modelling, Optimization Methods, Algorithm Design and Wireless Networks.
She worked in BHP Billiton Marketing Asia for eight years and gained a lot of industry experience through different functions, e.g. risk management, supply chain management, sales and marketing planning and etc.
Dr Edmund Low
Dr Edmund Low
Dr Edmund Low is currently Senior Lecturer with the NUS College at the National University of Singapore.
He has nearly 20 years of academic and professional experience in the use of data-driven tools to answer questions in public health and the environment. His past projects include applying AI techniques and machine learning models for environmental modelling and impact assessment. He currently heads the quantitative reasoning domain at USP, and teaches courses on statistical methods, data science and machine learning. As an educator, Edmund is a multiple recipient of both the USP Teaching Excellence Award, as well as the NUS Annual Teaching Excellence Award. Edmund holds a PhD in Environmental Engineering from Yale University.
FAQs
Qns: Is there a preferred platform and what type of software do I need to install?
Ans: You can use Windows or MacOS as we will be using Anaconda. Installation instructions will be provided in the course materials ahead of the class.
Qns: Does the course require any technical background?
Ans: Some knowledge of simple programming concepts, e.g. variables, loops, will be preferable. As part of the course will cover the basics of Python, participants without prior knowledge of the programming language can attend as well.
Qns: Is there an assessment at the end of the course?
Ans: Yes, participant is required to complete a short project using Python.
Course Fees
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 S$250 each year (or up to S$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
39 years old or youngerSingapore Citizen
40 years old or older