Python for Analytics – Fundamentals

Track: Associate Enterprise Data Analyst (AEDA)
Python for Analytics – Fundamentals will enable students to get started with Python programming by learning and getting hands-on with the basic concepts. This is essential for becoming a productive data professional.

Python Programming Fundamentals for Data Professionals

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Python programming is one of the most frequently utilized programming tools used in data processing and data analytics. The open source community provides a rich ecosystem of tools and libraries that enable us to automate workflows and analyze data on a large scale and in smarter ways. Students can get started with Python programming by learning and getting hands on with the basic concepts such as data types, variables, lists, dictionaries, functions, if/else conditions, loops, reading/writing data and error handling.

Learning outcome:

Upon completion, participants should be able to demonstrate each of the following;
  1. Understand the advantages and possibilities of the Python programming language
  2. Comprehend the principles, techniques and practices relevant to Python programming
  3. Design and solve a simple data related problem by writing a workable program using the basics

Who should attend:

Analysts and business professionals who want to jumpstart their abilities in analyzing data

3 days of in depth learning

Face to face with experienced Data Scientist.

Course Methodology

This course will utilize a combination of Lectures and Hands- on Exercises.

CADS Certification​

Earn certification upon completion.

Pre-requisite:
N/A
Minimum Qualification:
Undergraduate Degree

Training Track

Associate Enterprise Data Analyst (AEDA)

Python for Analytics – Fundamentals is one of the modules under our Associate Enterprise Data Analyst (AEDA) programme. AEDA is a fifteen-day training program that provides analysts with the tools required for efficient Data Analysis.

Details of Subject

  1. Introduction to Python and Jupyter –  Participants will learn about what the Python programming language is and why it is useful for data analysis. The course materials are Jupyter notebooks, which are documents that contain live code, narrative text and visuals. Jupyter is a great way to create reproducible data analysis and the participants will learn how be productive in this environment using short-keys and best practices.
  2. Python Programming Fundamentals –  Participants will learn the fundamentals of Python programming, covering arithmetic operators, variables, data types, data structures, control flows, functions and loops. Participants will be able to write basic code in Python by understanding the basic syntax of Python.
  3. I/O in Python –  Participants will learn about the structures of JSON and CSV data, and to manipulate files in Python, specifically JSON and comma-separated values (CSV). Participants will be able to use common data formats in their data analysis.
  4. Debugging –  Participants will be able to handle and understand the techniques needed to handle and debug errors. Handling errors and will make the participants able to write clean, error-free codes.

Lead Instructor

Jan Sauer
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

EDP CADS Certified Python for Analytics

Python has become a key driver in the explosion of big data analytics and machine intelligence across most industries. Given its versatility and applicability to data analysis, it has become the tool of choice for most industries. Put your fluency in Python through its paces and watch it pay off long-term in spades. This certification will require knowledge in data types, collections, function and control structures, use of python libraries to manipulate and visualize data and writing of workable end-to-end code.

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Python for Analytics – Fundamentals