Introduction

The aim of this course is to teach how to use the Python language practically and efficiently to summarize, analyze, and visualize data sets. Using real cases, students will acquire the skills to extract knowledge from data and guide business decisions based on them.

Objectives

At the end of the course, students will be able to use the Python language to:

• Load and export data from different sources.
• Exploit datasets and extract information from them.
• Choose the proper analysis for each situation.
• Visualize complex data effectively.
• Create interactive visualizations.

Student profile

This course is designed for all types of professionals who wish to learn how to implement business solutions based on data analytics or data summarization, and plan to use Python programming language.

Prerequisites

It is recommended that students have a basic knowledge of Python or a good level of other programming languages such as Java, R, C++…

Course Materials

Students will receive a copy of the documentation prepared by BIT by Netmind.

Methodology

This is an active and participatory course through demonstrations, practical exercises and user analysis of all theoretical topics taught by the trainer in order to address real cases of the related product.

Certification

The evaluation is continuous, based on group and individual activities. The trainer will give constant feedback to each participant.

During the course, participants will complete an evaluation test that they must pass with more than 70% of correct answers. They will have 30 minutes for its completion.

The conditions for additional certification services are subject to the terms of the license owner or the approved certification authority.

Accreditation

In addition, a Certificate of Attendance will be issued only to students with an attendance of more than 75% and a Diploma of Achievement if they also pass the evaluation test.

Data Analytics with Python

1. Introduction to Business Intelligence

• Objectives of data analytics and business intelligence
• Description vs. prediction
• Data sources
• Import and export

2. Basic data analytics

• Types of data
• Fundamental concepts of statistics
• Decision making and value generation
• Learning to read complex data

3. Data exploitation

• Transformations and manipulation of data
• Data summaries
• Groupings and house functions
• Data cleansing

4. Data visualization

• Introduction to matplotlib package (graphics)
• Types of charts
• Customizing and exporting charts
• Creating charts that add value

5. Advanced visualization

• Introduction to the plotly package (interactive graphics)
• Formatting interactive graphics
• Viewing maps
• Adding controls and buttons

JDB 210 | JDB210 | JDB-210