In this training science of data we start with the grip of working tools: Jupyter Notebook, Spyder, among others. Then we start with Numpy (numerical Python). We insist on calculation functions: maths, stats, fi nancial, etc. The heart of the training is Pandas. This module allows you to sort, filter, select, cut, truncate, categorize, group, aggregate, rotate, stack, cross, and so on. Pandas does what a spreadsheet and SQL table do and more. Pandas allows you to apply Numpy or other functions. In addition, Pandas can automate procedures and work in volume (parallel computing on multiple processors, on graphics card or on a cluster). Pandas is often the machine learning model foundation, because you have to prepare the data before using it. Finally, Pandas makes it easy to visualize data with modules like Matplotlib and Seaborn. There is also a GeoPandas extension for geomatics to enhance map visualization.
Some specializations and pointed topics not covered are available extra. We will revise them quickly.
Install the Anaconda distribution
Introduction to Numpy
Create ndarray objects
Data selection
Add, edit, delete items
Use numpy functions
Enter exit
Series objects
DataFrames objects
Data selection
Aggregation functions
Merge, Join, Remodeling
Use lambda functions
Make a dynamic crossover (Pivot Table)
Manipulate excel data (csv) and json
2D curve display
Point cloud display
Histogram display
Web API
Request to an API
Get the answer
Treat the answer
Application: API Twitter, analyze and visualize in time
->
→ R Language Course: RStudio, Reporting
→ Python Training for Data Science | scientific python
→ Python Training – Data Science (Numpy Pandas Matplotlib)
Formation en ligne
Vidéos de formations sur les logiciels en bureautique
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