Python for Analytics

Track: Associate Enterprise Data Analyst (AEDA)
This module will empower students to write a workable program to solve data related problem using Python’s powerful data processing libraries. This is hallmark of advanced ability for professionals.

Tame Your Data With Python

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Students will learn how to leverage the rich ecosystem of libraries provided by the open source community that enables automation and analysis of data on a large scale. In this module, the students will become versed in Python libraries to transform data in the form of both numeric tables and text, visualize data and how to write an end-to- end program.

Learning outcome:

Upon completion, participants should be able to demonstrate each of the following;
  1. Understand the advantages and possibilities of Python’s data processing libraries
  2. Comprehend the principles, techniques and practices relevant to Python programming using the libraries relevant to data processing
  3. Design and solve more advanced data- related problems by writing a workable program using Python libraries

Who should attend:

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

5 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 is one of the modules under our Associate Enterprise Data Analyst (AEDA) programme. AEDA is a 13 to 18-day training program that provides analysts with the tools required for efficient Data Analysis.

Details of Subject

  1. Data Wrangling with Numpy & Pandas –  Numpy and Pandas are the cornerstone of doing data wrangling in Python. Participants will first learn how to manipulate data in Numpy arrays, an efficient data structure for numerical computation. Subsequently, students will learn how to manipulate data using Pandas, a library built on top of Numpy. Learning Pandas will enable students to perform various data wrangling tasks such as loading, sub-setting and transforming data using its sophisticated data structure. This will equip students with the knowledge to use the essential tool for data preparation.
  2. Data Visualization with Matplotlib & Seaborn –  Participants will learn how to create various charts and modify the chart components and objects using the Matplotlib package. Having acquired some basic understandings of Matplotlib, students will be taught how to use the Seaborn library, a package built on top of Matplotlib framework to quickly create beautiful charts.
  3. Running Python from Command Line –  Participants will learn how to execute Python scripts from command line. This entails getting familiar with the typical structure and arguments of Python scripts. Students will also work on an exercise of writing a Python script that performs data cleaning and running it from the command line.

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

AEDA 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