This Python course at a glance
This Python course (IABAC™ accredited) focuses on the possibilities Python offers in data analysis & data science. In this Python course for data science, you will learn to explore, analyze, and transform data. Additionally, you’ll gain skills in visualizing your insights.
Python offers various useful data analysis and data science 'packages' that extend its standard functionality. Almost every data analysis relies on these packages. Therefore, we cover the most popular packages extensively. The course materials serve as a handy reference for future data analyses.
No programming experience is required to join this course. Since Python is an accessible programming language, you can start coding right away during this training.
This Python course alternates between classroom instruction of theory and hands-on, challenging assignments. On the very first morning, you’ll write your own Python scripts.
Throughout the training, you’ll use a personal online Python environment that remains accessible even after the course, allowing you to review everything at your own pace later on.
Upon completion, you will receive an official certificate from this IABAC™ accredited course.
Reviews from participants & clients
Learning objectives of this Python course
Python basics for data analysis
We guide you through the fundamental skills needed to work with Python. You’ll learn about the different data types, how to use functions, and practice writing scripts independently. The combination of theory and exercises ensures that you retain the material from the training effectively.
Working with datasets
By combining data from different sources, you’ll learn how to generate new insights and explore datasets effectively. You’ll enhance datasets with newly calculated columns, group data, and apply specific transformations.
Visualize like never before
Valuable information is meant to be shared. You’ll learn how to present it clearly by creating insightful tabular reports and making them impactful with a variety of versatile charts.
Is this Python Course right for me?
This course is perfect for you if:
- You work in an environment where you deal with data and information.
- Analyses or reports need to be automated, but you don’t have an IT background.
- You want to create advanced visualizations that go far beyond simple graphs in Excel.
- You’ve heard about data science and want to understand it better.
With this training, we provide you with a kick-start to performing data analyses with Python. The many possibilities will broaden your perspective on data analysis.
Required knowledge
No specific prior knowledge is required, but basic math skills and experience with tools like Excel files, text files, or databases can be helpful. We recommend at least a bachelor’s (HBO) level of thinking and working.
Combine this course with our machine learning training?
The Python course for data science can be combined with the machine learning training at a reduced price. Both are included in our 4-day Data Science Course training program.
Content of the Python Course for data analysis & data science
The content of this training is accredited by the International Association of Business Analytics Certification (IABAC™).
Day 1: Basic Python Skills
Part 1: Introduction to Data Science and Python
This Python course begins with an introduction to Python, covering its history and key features. You’ll learn why Python is so well-suited for data science. We explore different ways to work with Python code, and you’ll write your first script using Jupyter Notebook, one of the most popular tools for writing and running Python scripts. Learning to use Jupyter Notebook during the course is a double win.
Part 2: Variables and Data Types
To use and manipulate data, you need variables. A variable assigns a name to a data element. Different types of data (such as numbers and text) are stored as different data types. You’ll learn about various data types and how they behave differently.
Part 3: Lists
In data science, you often work with large volumes of data. Lists are the first way you’ll learn to manage datasets in Python. You’ll practice editing lists and learn how best to use them.
Part 4: Dictionaries
Dictionaries are widely used in datasets. In a dictionary, you can link values to specific keys, similar to a traditional dictionary. You’ll learn why dictionaries are useful, where they are applied, and practice using them in a data analysis.
Part 5: Logic, Methods, and Functions
You’ll learn about different types of logic and how to apply them, for example, to filter a dataset based on a condition. Additionally, you’ll learn to write your own functions to handle recurring tasks more easily. You’ll practice using methods, functions, and creating your own.
Day 2: Python Tools for Data Scientists
Part 6: NumPy
The Python package NumPy allows you to efficiently work with large datasets by storing data in a NumPy array. In this course, you’ll learn how to create a NumPy array and perform calculations on it. Mastering NumPy is essential for any data scientist.
Part 7: Pandas
One of the most widely used Python packages for data science is Pandas. With Pandas, you can import datasets from various sources, such as databases or Excel files. You’ll learn to explore and transform datasets efficiently, combining them, adding calculated columns, grouping data, and applying filters. In short, you’ll use Pandas to extract valuable insights from data.
Part 8: Matplotlib
A picture is worth a thousand words. Visualizing data insights allows you to communicate underlying messages effectively. The Python package Matplotlib offers a variety of visualization options. You’ll learn how to use Matplotlib to create diverse, versatile visualizations of your datasets.
Part 9: Final Assignment
During this Python course for data science, you’ll apply your new skills to a real-world dataset in a final assignment. You’ll combine data from multiple sources, apply logic, and transform it into actionable insights presented through tables and visualizations.
Self-study add-ons for specific learning goals
You can enhance this classroom training with optional add-ons. We can discuss wether certain topics are relevant for your organisation.
Add-On: Working with APIs & SQL from Python
In this add-on, you’ll learn how APIs are structured and how to work with them. You’ll also explore how to use SQL within Python. Participants who choose this add-on are interested in retrieving or storing data in various ways.
Add-On: Professional Programming
In this add-on, you’ll learn how to write professional-grade code. You’ll discover what it takes to work effectively within a professional team and focus on advanced techniques for writing quality code. The module concludes with methods for managing code in a reliable and efficient way.
Add-On: GIS (Spatial Data) with Python
In this add-on, you’ll learn to work with spatial data using Python. You’ll explore the key packages needed to perform spatial analyses, various transformations, and (interactive) visualizations. You’ll work with popular packages such as Shapely, GeoPandas, and Folium.
Not Just Any Training
At Data Science Partners, we aim to train you to become a truly skilled Data Analyst. While much of this is achieved during the Python course, we also ensure your development continues after the training. We do this in two ways.
After completing the training, each participant receives our custom-developed and highly comprehensive reference guide, which includes all the course materials, exercises, and answers used during the training.
As an alumni participant, you can attend our Data Science inspiration meetups free of charge whenever we organize them.
Location, dates, and times
This Python Course for data analysis & data science is offered exclusively as an in-company program for groups. The location, date, and times are flexible. Contact us for a customized quote tailored to your specific needs.
• Dates are flexible
• Location is flexible