Development, testing & web - Data Science with Python, API Society Certification
A practical course focused on Data Science with Python, API Society Certification. Participants build skills across three priorities: The Scientific Python Ecosystem, NumPy Library, Pandas Library.
Detailed programme
The Scientific Python Ecosystem
- Presentation of Python data science packages.
- Installation of libraries in virtual environment: pip and module venv, miniconda, mamba, miniforge, WinPython.
- Development environment.
- Use IPython, Jupyter Notebook, JupyterLab, IDE:
- Spyder's example.
- Discover the text editor: VS Code.
NumPy Library
- Introduction and creation of tables.
- Presentation of the NumPy bookshop.
- Benefits of tables (performance, data manipulation).
- Creating arrays with array(), zeros(), ones(), full(), arange(), linspace(), logspace().
- Matrix multiplication with np.dot and operator @.
- Initialization with random data (random module).
- Handle tables and operations.
- Indexing, slicing, and advanced indexing.
- Transpose and change table dimensions (transpose(), reshape()).
- Concate and cut tables (concatenate(), split()).
- Manipulate classical and mathematical functions (sum(), min(), max(), median().
- Compare and hide data with Boolean masks.
- Data management and visualization.
- Load and save arrays (loadtxt(), save(), load()).
- Use the Axis option in the functions.
- Extract information from data.
- Using visualization practices: choice of modules and types of graphics.
- Generate interactive graphics.
Pandas Library
- Introduction and data structures.
- Presentation of the Pandas Library.
- Creation of series with the series class.
- Creating 2D or DataFrame tables with the DataFrame class.
- Extraction of row and column indices (index and column attributes).
- Read and export data in different formats (csv, xls).
- Implement basic methods: head() and tail().
- Indexing and slicing: implicit, explicit, and use of loc and iloc indexers.
- Select data and use Boolean expressions.
- Data handling and processing.
- Insert and modify data.
- Rename columns with rename().
- Concate data with concat() and fusion/joint with merge() and join().
- Copy data: superficial or deep copy (copy()).
- Process missing data (isna(), isnull(), notna(), notnull(), dropna(), fillna(), interpolate()).
- Manipulate clues: set index(), sort index().
- Sort values with spell values().
- Transpose data with transposition().
Data Analysis and Aggregation
- Data aggregation: sum(), cumsum(), min(), max(), count(), mean(), median(), var(), std(), quantile(), describe() Grouping and groupby() analysis.
- Use aggregate(), apply(), filter(), transform().
- Create dynamic cross tables with pivot table().
- Segment data with qcut() and cut().
- Calculate slippery averages with rolling(), expanding(), ewm().
- Process time data through to datetime(), to timedelta(), date range(), period range()...
Matplotlib Library
- Introduction and creation of graphs.
- Library presentation.
- Show graphics from a Python script (plt.show()) or from a notebook.
- Use the MATLAB style or object-oriented style to display graphics.
- Change the style of the graphics.
- Objects figure and axes.
- Draw curves with plot().
- Types of graphics and interactions.
- Show dot clouds with scatter().
- Show error bars with error bar().
- Fill the surface between two lines with fill between().
- Trace histograms with hist().
- Trace 3D graphics with mplot3d.
- Interact with graphics in Jupyter notebook with the interact widget.
- Use pandas plot to create tracks quickly: plot(), bar(), barh(), hist(), box(), scatter(), pie().
Seaborn Library
- Introduction to Seaborn and basic features.
- Operation of the Seaborn API: Distinction between figure and axis level pads.
- Relational Plots: use functions to plot relationships between variables.
- Trace distributions: use functions to view data distributions.
- Qualitative data: tracing of categorical data.
- Thermal charts: use the heatmap() function to draw thermal maps.
- Linear regression models: draw regression models with lmplot().
- Customization of graphics: change the rendering of the figure with the functions.
Plotly Library
- Presentation of the Plotly bookshop and Kaleido: introduction and exploration of Plotly Express.
- Draw curves with line(): modification of the figure with the options title, width, height, marker, labels, etc..
- Create area graphics with area(): adding patterns with pattern shape.
- Create dot clouds with scatter(): use of options size, size max, opacity, symbol, color continuous 3D graphics: use of scatter 3d() and line 3d().
- Shape bar diagrams with bar() and histograms with histogram().
- Draw maps with line map(), scatter map(), line geo(), scatter geo(), and choropleth().
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- Exceev Consulting
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75012, Paris, France - Exceev Technology
332 Bd Brahim Roudani
20330, Casablanca, Morocco