Python Programming I

Track: Enterprise Data Scientist (EDS)
Participants will learn the basic introduction to programming with Python. Design and solve a data- related problem by writing a workable program. Topics covered in this module are data types, data structures, and program control flow.

Design and Solve Data-Related Problem

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Python programming is one of the most frequently utilized programming tools used in data processing, data modelling and data analytics. This subject will equip students with the necessary knowledge to get started with Python programming.

Learning outcome:

Upon completion, participants should be able to demonstrate each of the following;
  1. Comprehend the principles, techniques and practices relevant to the Python programming using libraries applicable to data processing
  2. Design and solve a data-related problem by writing a workable program

Who should attend:

Professionals that work with data

5 days of in depth learning

Face to face with experienced Data Scientist.

Course Methodology

This course will utilize a combination of Presentations and Workshops.

Pre-requisite:
Introduction to Programming

HRDF Claimable

HDRF claimable. EPP option available for selected credit card.

CADS Certification​

Earn certification upon completion.

Minimum Qualification:
Undergraduate Degree

Training Track

Enterprise Data Scientist (EDS)

Python Programming I is one of the modules under our Enterprise Data
Scientist (EDS) program. EDS is a 42-day training program that provides
participants with the tools to be key leaders and contributors of a data science team and be able to analyze data to drive informed business decisions.

Details of Subject

  1. Introduction –
    • Basic concepts of Python programming
    • What is a program? What is Python?
    • Python script, iPython, Jupyter notebook
  2. Basic Data Types –
    • Arithmetic, integer, float
    • Variable
    • Strings
    • Slicing on strings
  3. Data Types –
    • Variables, Assignments, Strings, List, Dictionary, Tuple
  4. Functions –
    • Procedural abstraction
    • Syntax for defining a function
    • Boolean operations
    • If/else condition
    • Loops
    • Local and global variables
  5. Input/Output –
    • Read & Write files
    • Handling files, appending to files, copying files
  6. Error Handling in Python –
    • Different types of exceptions
    • Stack traceback

Lead Instructor

Jan Sauer
A Data Scientist at The Center of Applied Data Science (CADS), Jan Sauer was a biostatistician in the field of deep learning and image/pattern recognition. He has a master’s degree in physics and have extensive experience as a software developer. Throughout his career, he has been involved in different areas of data science, ranging from automated data collection and data analysis, data pipeline and database design, and advanced machine learning where he uses Tensorflow extensively in image processing.

CADS Certification

EDS CADS Certified Enterprise Data Scientist

Certification information for this module & track will be made available soon.

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Python Programming I