International Workshop on Quantitative Data Analysis and Visualization using Python

v

Course Title: International Workshop on Quantitative Data Analysis and Visualization using Python

Course venue: Pretoria, S/Africa

Course dates

07/08/2023 - 11/08/2023 : http://bit.ly/3lghYlm

Contacts: +254 707 053 111

E-mail: info@ vitalextralearning.com

Description

Python has been one of the premier, flexible, and powerful open-source language that is easy to learn, easy to use, and has powerful libraries for data manipulation and analysis. This training is a step -by-step guide to Python and Statistical Data Analysis with extensive hands on. The course is delivered with several activity problems, assignments and scenarios that help participants gain practical experience in data handling, analysis, interpretation as well as reporting. This course starts by exploring basic statistics such as mean, median and mode and commence to advanced exploratory features such as groups comparisons, regression, test of relationships, classification, clustering, just to mention a few.

Learning outcomes

By the end of this course, the participants will be able to:

• Easily read and write files of various types in to a Python program.

• Identify and fix errors in datasets.

• Work with Python 'modules' and use them for data analysis tasks

• Use libraries like pandas, numpy, matplotlib, scikit, and master the concepts like Python machine learning, scripts, and sequence.

• Gain high-level skills on statistical results interpretation and report writing.

Who should enroll?

The course is useful for professionals who use data as part of their work and who need to make decisions from data analysis. Those with prior understanding of programming and statistics finds it easier to take this course.

Why train with us

Vital Extra Learning guarantees our clients:

• State-of-the-art facilities and training infrastructure

• Extended tradition of hand-holding during post engagement

• Service delivery through highly seasoned industry experts.

• Value for money