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Python Data Analysis with JupyterLab E-learning course

In this e-learning course you work with Python in JupyterLab. You learn data analysis with NumPy and Pandas, and create visualisations with Matplotlib. Basic knowledge of Python is required.

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  • You work directly in JupyterLab with real data
  • NumPy, pandas and Matplotlib in a single course
  • One year of access, at your own pace
  • Videos, quizzes and exercises in a single learning path
  • Ready for data analysis in your own work

In this e-learning course you dive into data analysis with Python and JupyterLab. You work with NumPy, pandas and Matplotlib to edit, group and visualise data intelligently. A year of access, at your own pace.

Description

In this e-learning course you get to work with Python for data analysis via JupyterLab. Python is a versatile programming language with clearly readable code, allowing you to achieve a great deal with few lines. Thanks to its intuitive set-up and the use of indentation, Python is accessible and is used worldwide for scripts, applications, data analysis and websites.

You work in JupyterLab with Jupyter notebooks and learn how to write, test and share Python programs quickly. You go into NumPy in more depth for working with one-dimensional and two-dimensional arrays, and into pandas for segmenting and grouping data using Series objects and DataFrames. You also gain insight into simple visualisation techniques with Matplotlib.

The course is in English and consists of videos, lectures, quizzes and exercises. You will spend approximately 16 hours on the material. A basic knowledge of Python is required: you can work with lists, tuples and dictionaries, with loops and conditionals, and you can write your own functions. Once activated, you have access to the e-learning course for a year, so you can work at your own pace.

Topics

In this e-learning you work in JupyterLab on data analysis with Python. You use Jupyter notebooks and Markdown to record your code and explanations clearly. After that you dive into the most important libraries for data analysis and visualisation.

  • working with JupyterLab and Jupyter notebooks
  • documenting with Markdown
  • the purpose and use of NumPy
  • one-dimensional NumPy arrays
  • two-dimensional NumPy arrays
  • using Boolean arrays to create new arrays
  • the purpose and application of pandas
  • Series objects for one-dimensional data
  • DataFrame objects for two-dimensional data
  • creating plots with Matplotlib

Result

After this e-learning course you will work independently with Jupyter Notebook to write, test and share Python programs. You use NumPy for calculations with arrays and Pandas for segmenting and grouping data. You also create clear visualisations with Matplotlib and use all of this for clear data analyses.

Target audience

This e-learning course is intended for data analysts, researchers, developers and students who want to take their data analysis to a higher level with Python and JupyterLab. You already have a basic knowledge of Python: you can handle lists, tuples and dictionaries, you write loops and conditionals and you can create your own functions. The course is in English, so you need to be comfortable reading and listening in English.

Teaching method

You follow this course as an e-learning course, entirely at your own pace. You get access to extensive videos, lectures, quizzes and practical exercises. This takes you step by step towards carrying out data analyses independently with Python and JupyterLab.

Allow for around 16 hours of study time. The course remains available for a year after activation, so you can always go back to sections you would like to review. The course is delivered in English.

Prior knowledge

You need basic knowledge of Python. You can work with lists, tuples and dictionaries, you understand loops and conditionals and you can write your own functions. No further prior knowledge is required.

Comments

This e-learning course is in English and remains accessible for a year after activation. You work through videos, quizzes and exercises at your own pace. Basic knowledge of Python (lists, tuples, dictionaries, loops, conditionals and functions) is required to keep up.

Practical information

Education level
Suitable for all levels
Preparation time
None
Included
Course materials
Certificate
At the end of the course you will receive a certificate of attendance

In-company

Would you like to organise this training for a whole group or several groups? This can be cost-effective. For example, we can tailor the content entirely to your organisation, and training a whole group is also cheaper.

Are you looking at large numbers of participants or a complete academy? That involves a great deal. Consider planning and communication, for example, or identifying and agreeing the objectives and content of the training. Or differences in level, feedback mechanisms and safeguarding. Fortunately, Learnit has extensive experience in this field (more than 25 years) and we are happy to help you through this process.

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This course for your team?

Plan your own group on a date that suits you, at your office, at our premises or online. We tailor the content to your day-to-day work, and training a whole group is cheaper than individual bookings.

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  • Tailored content
  • Multiple groups or an academy are also possible
  • More than 25 years' experience
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What others say about this course

“A good C / embedded C beginners' course that moves through the material quickly, so a lot of ground can be covered. There is plenty of variation between theory and practice. This course is also suitable for people with some (hobby) programming experience.”

Kiman Velt
Nedap N.V.
Embedded development with Python or C++8.3

“The course gave me a good start in better understanding the embedded system I work with at the company.”

R. van Putten
Rhosonics Analytical BV
Embedded development with Python or C++8.0

“A tailor-made course delivered by a knowledgeable, experienced and motivated trainer. A good balance between theory and practice.”

Rowan Klein Gunnewiek
Nedap N.V.
Embedded development with Python or C++8.0

“A good C / embedded C beginners' course that moves through the material quickly, so a lot of ground can be covered. There is plenty of variation between theory and practice. This course is also suitable for people with some (hobby) programming experience.”

Kiman Velt
Nedap N.V.
Embedded development with Python or C++8.3

“I followed this course entirely to my satisfaction. A very good trainer with a great deal of practical experience managed to convey the material clearly to me and my colleagues with appealing examples and exercises. I can recommend this course to anyone who wants to become familiar with Python in a short time.”

Robin van Schaik
Albelli B.V.
Programming in Python8.3

“In 2 days the course gave me a good picture of Python, the basics, the possibilities and also hands-on work and practice with it. The threshold for me to start using it myself is now lower.”

Frederica Janga
Nederlandse Gasunie N.V.
Programming in Python7.7

Frequently asked questions

How long does the Python data analysis with JupyterLab e-learning course take?
The Python Data Analysis with JupyterLab e-learning course takes approximately 16 hours, which you work through at your own pace. After activation you have a year's access to the course, so you can revisit topics whenever you like. You work with videos, lectures, quizzes and exercises covering JupyterLab, NumPy, Pandas and Matplotlib. Basic knowledge of Python is required.
What prior knowledge do you need for the Python Data Analysis with JupyterLab e-learning course?
You need a basic knowledge of Python to start this e-learning. In concrete terms, this means you can work with lists, tuples and dictionaries, understand loops and conditionals, and can write your own functions in Python. Do you not yet have that basis? Then take an introductory Python course first. With this prior knowledge, you will pick up data analysis via JupyterLab, NumPy, Pandas and Matplotlib with ease.
Is the Python data analysis with JupyterLab e-learning course in Dutch or in English?
The Python data analysis with JupyterLab e-learning course is in English. The videos, lectures, quizzes and exercises are all provided in English. The course remains accessible for a year after activation, so you can work at your own pace. A reasonable command of English is therefore useful in order to follow all the explanations about JupyterLab, NumPy, pandas and matplotlib properly.
Which topics are covered in the Python Data Analysis with JupyterLab e-learning?
In the Python Data Analysis with JupyterLab e-learning course you learn to work with JupyterLab, Jupyter notebooks and Markdown. You get to work with NumPy for one- and two-dimensional arrays, including Boolean arrays. You then work with pandas for series objects and DataFrames to segment and group data. Finally, you create plots and visualisations with matplotlib for a complete data analysis workflow.
How long do you have access to the Python data analysis with JupyterLab e-learning course?
You have a year's access to the Python data analysis with JupyterLab e-learning course, counting from the moment of activation. Within that period you can view and repeat all videos, lectures, quizzes and exercises without limit. This allows you to set your own study pace and review sections whenever you want to refresh your knowledge or practise in more depth.
What will you learn about NumPy, Pandas and Matplotlib in this Python e-learning course?
You learn to carry out data analysis with three core libraries. With NumPy you work with one- and two-dimensional arrays and Boolean selections. With Pandas you segment and group data using Series and DataFrame objects. With Matplotlib you create plots to visualise results. Together these tools give you the basis to process, analyse and present datasets within JupyterLab.
When should you choose this Python data analysis with JupyterLab e-learning course instead of a classroom course?
This e-learning suits you if you can already program in Python and want to progress independently to data analysis. You work at your own pace with videos, lectures, quizzes and exercises, and have access for a year after activation. The study load is around 16 hours. A classroom course makes more sense if you prefer to ask a trainer questions live, want to spar with fellow participants or need a fixed date to get started. The e-learning is in English, so you need to be comfortable with English technical terminology. Expected prior knowledge: lists, tuples, dictionaries, loops, conditionals and being able to write your own functions in Python. If you do not have that basis, a Python basics course is a more logical starting point than this data analysis module.
Which type of data analysis is Python with JupyterLab suitable for, and when is R or Excel the better choice?
Python with JupyterLab is suitable for data analysis where you want to combine code with explanation, charts and interim results in a single notebook. Think of exploring datasets, cleaning data with pandas, numerical operations with NumPy and creating visualisations with Matplotlib. It is strong if you want to repeat, share or later re-run analyses with other data. R is often a more logical choice for heavy statistical work and academic research. Excel is sufficient for small datasets and one-off analyses without programming logic. Choose Python with JupyterLab if you work with larger files, want to automate your analyses or want to record your results reproducibly. In this e-learning course you learn exactly that approach, provided you already have a command of basic Python.
What does this Python data analysis e-learning course give me that free YouTube tutorials do not?
The Python data analysis with JupyterLab e-learning course offers a structured route of roughly 16 hours, built around videos, lectures, quizzes and exercises. Free YouTube material is often fragmented and lacks a logical progression from NumPy to Pandas to Matplotlib. In this course you work in a fixed order: first Jupyter notebooks and Markdown, then one-dimensional and two-dimensional NumPy arrays, followed by Series and DataFrame objects in Pandas, and finally data visualisation with Matplotlib. You have a year's access after activation, so you can revisit sections whenever you need them in practice. The course is in English and requires prior knowledge of lists, tuples, dictionaries, loops, conditionals and functions in Python. That makes it more focused than separate tutorials for anyone who really wants to master data analysis.
How does JupyterLab compare to ordinary Python scripts in an IDE such as VS Code or PyCharm for data analysis?
JupyterLab works with notebooks: you run code in separate cells and see the result immediately, including tables and charts. That is ideal for data analysis, where you explore, transform and visualise data step by step. In an IDE such as VS Code or PyCharm you usually run a complete script in one go, which is more convenient for production code, applications or larger projects. In this e-learning you learn how to write, test and share Python code quickly using Jupyter notebooks, combined with Markdown for explanatory text. You work with NumPy for arrays, with pandas for series and DataFrames, and with Matplotlib for visualisations. For exploratory analysis, reporting and sharing findings with colleagues, JupyterLab is a logical choice. For reusable scripts or web applications you can still turn to an IDE later on.
What does a typical workflow in JupyterLab look like for a data analysis project, and what will I learn about it in this e-learning course?
A typical workflow in JupyterLab runs in cells: you load data into a notebook, explore it with Pandas, transform columns with NumPy, visualise intermediate results with Matplotlib and document your steps in Markdown cells. The notebook combines code, output and explanation in a single file, which makes sharing analyses with colleagues easier than separate scripts. In this e-learning course you practise this workflow step by step. You learn to set up Jupyter notebooks, use Markdown for annotation, work with one-dimensional and two-dimensional NumPy arrays, apply Boolean arrays to filter data, and use Pandas Series and DataFrame objects to segment and group data. Creating plots with Matplotlib completes the cycle. The course takes approximately 16 hours and remains accessible for one year after activation.
After this Python data analysis e-learning, will I be able to clean and analyse a dataset independently, or will I still need additional training?
After this e-learning course you can independently load a structured dataset (CSV, Excel, JSON) into a Jupyter notebook, clean it up with Pandas, carry out operations with NumPy arrays and create initial visualisations with Matplotlib. That is sufficient for exploratory data analysis and reporting within one table or a few linked tables. The course does not cover statistical modelling, machine learning (scikit-learn) or working with databases via SQL connections. For those topics you will need follow-up training. If you work with very large datasets or streaming data, you will also be outside the scope of JupyterLab. For most analysts and researchers who are running up against the limits of Excel, this e-learning course is enough to make the move to Python and get started in practice straight away.

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