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Introduction to Programming in Python Course

Continuous availability
5 Weeks (64h)
No timetable | 2 hours/day recommended

Review of the key programming concepts needed to process and use data through code. You will learn how to use the Python programming language, and practice using selfassessed practical material. | 10% discount for payment in full

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Academic Information

Why this course?

  • The most widely-used language: According to the TIOBE index (The Importance of Being Earnest), published by TIOBE Software BV, Python is the most commonly-used programming language today.
  • Versatility and clarity: It is one of the most versatile programming languages that exists and can be used on various operating systems. IEEE Spectrum defines it as a multiparadigm, dynamic and multi-purpose programming language, designed to be quick to learn, use, and understand. What’s more, it offers a clean and uniform syntax.
  • Interpreted language: The most notable feature of Python is that it is an interpreted language, meaning that it isn’t compiled like Java or C/C++, rather it is interpreted at runtime.
  • Learning curve: Python is an easy language to read and write as it is highly similar to human language. Moreover, it is an open-source, cross-platform language and consequently free to use, meaning it can be used to develop software without limits.
  • For professionals who want to work in data analysis and are interested in adding Python programming to their job skills.
Course aims
  • Understand the Python working environment
  • Identify the key variables in Python
  • Introduction to object-oriented programming (OOP)
  • Learn to use functions, loops, and control structures in Python
  • Understand the main Python libraries (Pandas, datetime and NumPy)
  • Learn to make visualizations using tools such as Plotnine, Matplotlib and Plotly
Professional competencies of the course

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.


  • Practical activities right from the start
  • In each unit there is a multiple-choice test and at the end of the course students have to resolve a real-world case.
Study Plan

Study Plan

1. Python Fundamental Concepts

This unit is designed for students to learn and understand fundamental programming concepts using Python. This is a versatile and easy-to-learn language, which has become a popular choice for both beginners and experienced developers given its clear, readable syntax.

  • Introduction to Python
  • Python variables
  • Object types
  • Control structures
  • Functions
2. Intermediate Python

This unit looks at 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 unit covers a broad spectrum of skills essential for any data professional.

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

This unit explores two fundamental tools for data processing in Python: the datetime library and NumPy. The datetime library enables dates and times to be handled precisely, allowing arithmetic operations and comparisons. NumPy, on the other hand, is a pillar of scientific computing, providing support for multidimensional arrays and high-performance mathematical functions.

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

This unit looks at several issues related to creating and customizing graphs using tools such as Python, Matplotlib and Plotly. Starting with the installation of key libraries and loading data for manipulation, it goes on to explore graphs, from lines and bar charts to boxes, histograms, and scatter plots. It also looks at methods for adding titles, customizing axes, and editing the appearance of charts.

  • 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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