Prerequisites:

All participants should have a basic knowledge of programming in any language (Java, C, C++, Pascal, Fortran, Javascript, PHP, Python, etc.)

Who Should Attend:

This workshop is intended for individuals who are interested in learning Data Science, or who want to begin their career as a data scientist.

Exam Format:

The Certified Python for Data Science certification exam duration is 2 hours, consisting of 50 Multiple-Choice Questions, with a passing score of 70%. You will receive a professional Certified Python for Data Science certification upon passing the exam.

Learning Outcomes:

Upon completion of this course, you will be able to:

• Recognise the meaning of the terms "Data Science" and "Machine Learning".
• Understand the basics of Python.
• Develop and write code easily in Python.
• Deal easily with files and file systems.
• Deal with different sources of data.
• Analyse and visualise data to gain new insights.

Day 1:

Introduction to Programming

• What is Algorithm?
• What is Programming?
• The Natural Language of the Computer
• Machine Language
• Programming Language Levels
• Translators

Python Basics

• Identifiers, Lists, and Tuples
• Dictionaries, Sets, Strings, Operators, Control Structures, Loops

Day 2:

Jupyter Notebook

• Installing and Running Jupyter
• User Interface
• Checkpoints

Functions

• Functions
• Lambda and Map Functions
• Globals and Locals

Pythonic Programming

• List Comprehension
• Generator Expressions
• Exceptions Handling

Modules and Packages

• Modules
• Documentation
• Packages and Namespaces

Working with Files

• Create, Read, Update, Delete (CRUD) a File

Day 3:

Object-Oriented Programming

• OOP in General
• Classes
• Objects
• Constructors
• Instance/Class Data
• Instance/Class Method
• Inheritance

OS Module

• Working with File Systems
• Walking Directory Trees
• Paths
• Filenames
• Directories

Working with Files

• Creating a File
• Reading a File
• Updating a File
• Deleting a File

Working with JSON Data

• What is JSON and Why Is It Important?
• Module, Serialisation and Deserialisation

Web Scraping (BeautifulSoup)

• What is Web Scraping?
• HTML Tags
• BeautifulSoup Module
• Webpage Scraping Phase

Day 4:

Introduction to Matrix Processing (NumPy)

• What is NumPy?
• Ndarray Object, Data Types
• Array Attributes, Array Creation Routines
• Indexing and Slicing
• Array Manipulation
• Mathematical Functions

Data Analysis (Pandas)

• What is Pandas?
• Series
• DataFrame
• Data Importing
• Data Pre-Processing
• Data Grouping

Data Visualisation (Matplotlib)

• What is Matplotlib?
• Line Graphs
• Bar Graphs
• Pie Charts
• Histograms
• Scatter Plots
• Graph Attributes
• Text Annotation

Day 5:

Introduction to Applied Machine Learning (Scikit-learn)

• What is Machine Learning?
• Machine Learning Algorithm Types
• Main Steps in Machine Learning Projects
• Introduction to Scikit-learn Module

Capstone Project

Final Evaluation (Exam)

Payment can be made by:

1. Cash or Credit Card or Bank Cheque payable to "สำนักงานพัฒนาวิทยาศาสตร์และเทคโนโลยีแห่งชาติ" (a post-dated cheque is not accepted) on the first day of the service or within the last day of the service.

2. Account transfer and send the proof of the payment (the deposit slip) to email ttd@swpark.or.th

• Withholding tax (3%) is exempt.
• Should you need to withdraw, you must send the notice of the withdrawal in writing no later than 7 working days before the commencement date. The cancellation less than 7 days will be subject to a fine of 40% of the fee.
• Software Park Thailand reserves the rights to cancel courses due to unforeseen circumstances.