Target Audience

• Data analysts
• Python developers

Prerequisites

• Knowledge of Python fundamentals (variables, types, loops, conditions, functions, files)

Teaching Methods

• Theoretical input: interactive presentations with slides
• Hands-on practice: individual labs and progressive exercises using real financial datasets
• Active learning: collaborative problem-solving
• Balanced theory/practice approach: 30% theory / 70% practice
• Course materials provided to participants
• Mid-course quiz (20 questions) and final quiz (30 questions) to validate acquired skills

Target Certification: RS6701 — Manipulating, analyzing and visualizing data using Python Data Science modules — CPF eligible

Detailed Program

Day 1 — The Scientific Python Ecosystem & NumPy

• Overview of Python Data Science packages
• Installing scientific libraries: pip, venv, miniconda, mamba, miniforge, WinPython
• Development environments: IPython, Jupyter Notebook, JupyterLab, Spyder, VS Code
• Introduction to the NumPy library
• Advantages of arrays (performance, data representation)
• Creating arrays with array(), zeros(), ones(), full(), arange(), linspace(), logspace()
• Matrix multiplication with np.dot and the @ operator
• Identity matrix with identity() and eye(), diagonal matrix with diag()
• Random initialization using NumPy's random module
• Data types and attributes ndim, shape, size, dtype, itemsize, nbytes
• Indexing, slicing, advanced indexing and broadcasting
• Transposing and reshaping arrays (transpose(), reshape(), newaxis())
• Concatenating and splitting arrays (concatenate(), vstack(), hstack(), split())
• Functions: sum(), min(), max(), median(), percentile(), cumsum(), var(), argmin(), argmax()
• Boolean masks for extracting information
• Loading and saving arrays: loadtxt(), save(), load()

Day 2 — Data Manipulation with Pandas

• Introduction to the Pandas library
• Creating a Series and a DataFrame
• Extracting row and column indices (index and columns attributes)
• Importing and exporting data (CSV, Excel…)
• Data exploration: head(), tail(), info(), describe(), dtypes
• Implicit and explicit indexing with loc and iloc
• Advanced selection: boolean expressions, query() method
• Concatenating data with concat(), merging and joining with merge() and join()
• Missing values: isna(), dropna(), fillna(), interpolate()
• Sorting data: sort_index(), sort_values()
• Removing data and duplicates: drop(), drop_duplicates()
• Aggregation functions: sum(), cumsum(), min(), max(), mean(), median(), var(), std(), quantile()
• Grouping and analysis: groupby(), aggregate(), apply(), filter(), transform()
• Pivot tables: pivot_table()
• Moving averages: rolling(), expanding(), ewm()
• Multi-indexing: MultiIndex.from_product(), from_tuple(), from_arrays()
• String processing and regular expressions with Pandas
• Time series data: to_datetime(), date_range(), asfreq(), resample()

Day 3 — Visualization with Matplotlib & Seaborn

• Introduction to Matplotlib: MATLAB-style vs object-oriented approach
• Figure and Axes objects
• Plotting curves with plot(): color, style, width, title, legend
• Scatter plots with scatter()
• Error bars with errorbar()
• Area filling with fill_between()
• Histograms with hist()
• Multiple charts with subplots() and 3D plots with mplot3d
• Pandas plotting: plot(), bar(), barh(), hist(), box(), scatter(), pie()
• Introduction to Seaborn: Figure-level API and Axes-level API
• Relational plots: relplot(), lineplot(), scatterplot()
• Distributions: displot(), histplot(), jointplot(), pairplot()
• Categorical data: catplot(), barplot(), countplot(), boxplot(), violinplot()
• Heatmaps: heatmap()
• Linear regression models: lmplot()
• Customization: set_theme(), set_style(), set_context(), despine()

Day 4 — Interactive Visualization with Plotly

• Introduction to the Plotly library and Kaleido: exploring Plotly Express
• Plotting curves with line(): customizing figures with title, width, height, marker, labels
• Adding information: hover_data, hover_name, text
• Multiple charts: facet_row, facet_col
• Style customization: template option and default themes
• Area charts with area(): adding patterns with pattern_shape
• Scatter plots with scatter(): using size, size_max, opacity, symbol
• Color bars: color_continuous_scale, update_layout(), update_coloraxes()
• Formatting bar charts with bar() and histograms with histogram()
• 3D charts with scatter_3d() and line_3d()
• Mapping data with line_map(), scatter_map(), line_geo(), scatter_geo(), and choropleth()