Introduction

The objective of the course is to introduce students to the skills necessary to perform data analysis using Python as a programming language and serve as a starting point for further development as a programming professional.

Objectives

At the end of the course, students will know how to implement Python code solutions that allow them to:

• Know the fundamentals of Python language syntax.
• Use different types of variables, lists and dictionaries.
• Create and use functions and import packages.
• Use the Numpy package to extract information.
• Create and customize graphs on data.
• Exploit data using Pandas data frames.

Depending on the modules selected, you will be able to:

• Organize and develop new projects based on code solutions.
• Know and apply the basics of Machine Learning (Classification, Regression, Clustering,…)
• Work with large volumes of data (big data).
• Obtain information from web pages automatically
• Analyze and draw conclusions from textual data
• Design and implement programs with a graphical interface
• Manage files and messaging efficiently and automatically
• Automatically generate PDF reports from data
• Integrate Python with SQL databases
• Integrate Python with NoSQL databases
• Apply Object-Oriented Programming with Python

Student profile

This course is designed for all types of professionals who wish to learn the basics of programming from scratch, specifically adapted to a language as versatile as Python.

Prerequisites

No previous knowledge of computers is required, although it is assumed that students have sufficient command of a computer.

Course Materials

Students will receive a copy of the documentation and exercises developed by Netmind.

Faculty

We have a team of highly qualified instructors who combine training activities with the development of their professional activity as experts in the field of ICT. Professionals capable of transferring the most abstract technical concepts in a pleasant, practical and understandable way.

Methodology

The course is face-to-face, participatory and practical. The teacher will introduce the contents through realistic problems. The participants will assimilate the knowledge through the resolution of adaptive level activities.

The course is structured in two main modules, which offer an approach adapted to specific training needs:

• Introductory course (16 hours). Exposure and practice of Python fundamentals. It covers the essential elements of learning to provide students with a consolidated starting point in programming.
• Extension Modules (4 hours/module). Independent learning units that explore advanced functionality or work on solving more complex projects.

Certification

No official certificate.

Accreditation

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.

Python Fundamentals

Basic Module (16 h)

1. Introduction to programming

• What is programming and what is not?
• What is programming for?
• Why Python?

2.  The programming environment

• Installation
• Basic concepts of the environment
• Jupyter Notebook and Spyder

3. Python basics

• Types of variables
• Lists
• Dictionaries

4. Programming fundamentals and flow

• Conditionals
• Loops
• Functions and methods
• Best practices and code integration

5. Basic data processing

• Basic file management
• Multidimensional objects with Numpy
• Calculation and statistics

6. Data exploitation

• Dataframes with Pandas
• Data filtering
• Clustering and statistical information extraction
• Data visualization with Matplotlib and Plotly

Extension Modules (4+ h)

Module A. Project Workshop (4-8 hours)

• Realization of a complete practical project, based on real data.
• Learning to program in self-organized teams

Module B. Introduction to Data Science I (4 hours)

• Fundamentals of Machine Learning
• Fundamentals of the ScikitLearn package
• Data preparation for analysis
• Basic Classification Algorithms
• Introduction to Validation

Module C. Introduction to Data Science II (4 hours)

• Basic Regression Algorithms
• Clustering Algorithms (Clustering)
• Dimensionality Reduction (PCA)
• Introduction to parameter selection

Module D. Introduction to Big Data (4 hours)

• Installation and basics of the PySpark package
• Actions and transformations
• Datasets and SQL
• Distributed machine learning

Module E. Fundamentals of Web Scraping (4 hours)

• Introduction to BeautifulSoup
• Exploiting an HTML file
• Scanning the web automatically
• Working with the tweepy API

Module F. Text Mining Fundamentals (4 hours)

• From text to knowledge
• Preparing textual data
• Textual data description and visualization
• Machine Learning with textual data

Module G. Introduction to graphical interfaces – GUI (4 hours)

• Fundamentals of the Tkinter package
• User interaction
• Event management
• GUI design

Module H. File management (4 hours)

• Access and management of files and directories
• Formats and transformation
• Compressed files
• Automation (processes and messaging)

Module I. Automatic generation of documents (4 hours)

• Fundamentals of PDF transformation
• pyFPDF and ReportLab
• Tables and formats
• Automation

Module J. Integrating Python with SQL (4 hours)

• Fundamentals of SQL and relational databases
• Loading and handling MySQL data
• SQL operations
• The exploitation of tables from MySQL

Module K. Integrating Python with NoSQL (4 hours)

• NoSQL and JSON fundamentals
• Loading and handling MongoDB data
• PyMongo operations

Module L. Object-Oriented Programming (4 hours)

• Fundamentals of Object-Oriented Programming
• Classes and constructors
• Instances and inheritance
• Creation of object-based programs

JDB 208 | JDB208 | JDB-208