Python for data analytics
During this training, we learn how we can analyse data using Python. In the last few years, the fields of data analytics and data science became of great importance throughout the professional world. At the same time, we notice a spectacular growth in the popularity of Python in this field: it is on its way to become the leading language in scientific and business data analysis. Python's general strengths as a multi-purpose programming language, combined with a number of specialised libraries, make it a very valuable tool for exploring, analysing, and visualising all sorts of data.
In this course we look at the possibilities of Python in the field of data analysis and visualisation, including popular libraries such as NumPy, Pandas and Matplotlib.
Schedule
| date | dur. | lang. | location | price | |
| 16 Apr | 2 | Leuven | 1000 EUR (excl. VAT) | ||
| 10 Jun | 2 | Woerden | 1000 EUR (exempt from VAT) | ||
| SESSION INFO AND ENROLMENT | |||||
Intended for
This training is aimed at whoever wants to start practising data analysis: developers, data scientists, data architects, marketeers, and anyone who needs to manipulate, visualize, or summarize their corporate data.
Background
Basic Python knowledge is a prerequisite for this training, as well as some notions of statistics (see courses Python fundamentals and Statistics fundamentals).
Main topics
- Reading and writing data
- Text files, CSV
- Databases
- Microsoft Excel
- XML, HTML, JSON
- Data Wrangling and exploratory analysis
- Concatenating, merging and joining data
- Transforming data
- Pivoting
- Handling missing data
- Data aggregation and Grouping
- Regular Expressions
- Visualisation
- NumPy
- Pandas
- Matplotlib
- Jupyter Notebooks
- Other useful packages
Training method
Classroom training with demos and practical exercises.
Duration
2 days.
Course leader
Arnout Veugelen.
Reviews
4.3/5 (based on 40 evaluations; the most recent ones are shown below)
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Really appreciated the course level, the instructor has been really clear in his explanations
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| (William, ) |
The course was well organised, the explanations clear and the supporting documuments were very helpful. I would recommend it
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| (Erica, ) |
Excellent. The material was very complete, user friendly and well organised. The teacher was very knowledgeable about the topic and make the training not only interactive and interesting but also entertaining
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| (Ana Maria Rivera Serrano, ) |
Very satisfied (8.5/10)
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| (N.N., European Securities and Markets Authority, ) |
Very good, good pace and structure of the course.
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| (N.N., European Securities and Markets Authority, ) |
Very good for our needs. Perhaps slightly too "Theoric" in the initial part for an "Economists" audience.
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| (Luigi, ) |
I would have appreciated something more "genomics oriented" with actual analysis examples - pipeline drafts (for example RNA-seq, WGS or WEX data analysis)
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| (N.N., Keygene, ) |
A lot of useful insights provided, included material for self learning & practice. I am satisfied with this course and I can recommend it again in the future. Thank you!
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| (N.N., ) |
Click here to see more comments...
Good, maybe a little more exercises could be added.
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| (N.N., European Securities and Markets Authority, ) |
goede cursus, zeer tevreden
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| (Arnold Moeken, ING - Amsterdamse Poort, ) |
Goed, maar 2 dagen was eigenlijk te kort om het vakgebied Data Analytics goed tot haar recht te laten komen.
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| (N.N., ) |
Zeer goede intro in dit onderwerp!
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| (Paul, ) |
Nice!
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| (Tommy Vansteenkiste, BASF, ) |
Very good, just need a bit more exercises with the corrections (during class)
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| (N.N., ) |
Also interesting
Enrollees for this training also took the following courses:
| SESSION INFO AND ENROLMENT |