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Introductory programming course: Python

5 weeks (64h.)

No timetable | 2 hours per day recommended | Review of the key programming concepts necessary to deal with the processing and use of data through code. You will learn the Python programming language, which you will be able to practice using self-correcting practical material.

Academic information

Why study this course?
  • Most widespread language: According to the TIOBE (The Importance of Being Earnest) index, compiled and published by TIOBE Software BV, Python is the most widely used programming language in use today.
  • Versatility and clarity: One of the most versatile programming languages in existence, it is cross-platform, so it can be used on different operating systems. IEEE Spectrum defines it as a multi-paradigm, dynamic, multi-purpose programming language, designed to be fast - to learn, use and understand - and to impose a clean, uniform syntax.
  • Interpreted language: The most notable features of Python are that it is an interpreted language, which means that it is not compiled unlike other languages such as Java or C/C++, but is interpreted at runtime.
  • Learning curve: Python is an easy language to read and write due to its high similarity to the human language. In addition, it is an open source cross-platform language and therefore free of charge, which allows unlimited software development.
  • For professionals who want to work in data analytics and are interested in adding Python programming to their job skills.
Program aims
  • Understanding the Python framework
  • Identify the main variables in Python
  • An introduction to object-oriented programming
  • Using functions, loops and control structures in Python
  • Knowledge of the main Python libraries (Pandas, datetime and NumPy).
  • Learn how to make visualisations with tools such as Plotnine, Matplotlib and Plotly.
Professional skills

As a specialized data scientist, you will be prepared to take up the following opportunities : Data scientist, data analyst, data engineer, expert in data visualization, expert in data storage and processing architectures, expert in machine learning, expert in business intelligence, Chief Data Officer (CDO), business analytics, business intelligence. Through the Data Science Master’s Course Online, you will gain the technical knowledge required to obtain the following qualifications and certifications: AWS Certified Data Analytics, Google Data Analytics Certificate, IBM Data Analytics Professional, Associates Certified Analytics Professional (aCAP), Professional Certification BigML Certified Engineer.

Methodology
  • Practical activities from minute 1
  • For each unit there will be a multiple-choice test and at the end of the course a real case will have to be solved.

Study plan

1. Python basics

This unit is designed to facilitate the learning and understanding of fundamental programming concepts using the Python language. Python is a versatile and easy-to-learn language that has become a popular choice for both beginners and experienced developers due to its clear and readable syntax.

  • Introduction to Python
  • Variables in Python
  • Types of objects
  • Control structures
  • Functions
2. Intermediate Python

This unit covers a variety of key concepts and techniques in data analysis using the Pandas library in Python. From manipulating DataFrames to performing advanced operations such as merging and aggregating datasets, the content covers a broad spectrum of essential skills for any data professional.

  • Introduction to Pandas
  • Basic Pandas
  • Pandas intermediate
  • Advanced Pandas
3. Advanced Python

This unit explores two fundamental tools for data processing in Python: the datetime library and NumPy. The datetime library provides functionality for handling dates and times accurately, allowing arithmetic operations and comparisons between them. NumPy, on the other hand, stands as a pillar of scientific computing, providing support for multidimensional arrays and high-performance mathematical functions.

  • Datetime in Python
  • The NumPy library
4. Visualisation in Python

This unit covers various aspects related to the creation and customisation of graphics using tools such as Python, Matplotlib and Plotly. It starts with the installation of key libraries and the loading of data for further manipulation. Different types of graphs are explored, from line and bar charts to box, histogram and scatter plots. In addition, methods for adding titles, customising axes and modifying the appearance of graphs are highlighted.

  • Plotnine
  • Matplotlib
  • Plotly
*The academic program may be subject to changes in line with the changing demand for specific skills in the market. Your employability is our goal.
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