Highlights
- Set up a Python data science environment using Anaconda, JupyterLab, or VS Code and run analysis in Jupyter notebooks
- Master Python core concepts — data types, control flow, custom functions, and date/time handling
- Import and reshape data from CSV, JSON, and SQL databases into Pandas DataFrames
- Query and filter DataFrames using modern Pandas patterns including method chaining with .pipe(), .assign(), and .query()
- Handle missing data using filtering, interpolation, and custom filling strategies
- Transform and aggregate data using Pandas apply functions across individual values, rows, and columns
- Join, merge, concatenate, and reindex multiple tables to combine data from different sources
- Work with time series data including date indexing, resampling, and moving window operations
- Use NumPy for multi-dimensional arrays, vectorised operations, and linear algebra — with an introduction to Polars for high-performance data processing
- Create static visualisations with Matplotlib and interactive charts with Plotly Express — bar plots, line plots, scatter plots, histograms, and more
- Connect to SQL databases using SQLAlchemy 2.0, execute queries, and load results directly into Pandas
- Use AI tools to generate, debug, and optimise Pandas code — accelerating real-world data analysis workflows
Course Details
Environment Set-up
• The Anaconda distribution as Python Data Science platform
• Overview on Python virtual environment set-up
• "JupyterLab, VS Code, and Jupyter notebooks — choosing the right environment"
Python core concepts
• Built-in data types in Python
• Working with strings, numbers, lists, tuples and dictionaries
• Control flow statements
• Conditional execution with if statements
• Conditional loops with where statements
• Looping over a sequence with for statements
• Defining and using custom functions
• Working with dates and times
• Accessing data on file (CSV, JSON, ...)
Python Data Science libraries
• Working with data in pandas
- Working with table-like data in pandas
- Creating Series and DataFrame objects
- Loading data from file into DataFrame objects
- Adding, removing and updating databases
Retrieving data in pandas
- Querying data to extract specific rows and columns
- Filtering data on specific conditions
- Understanding pandas indexing
Data manipulation in pandas
- Data transformation
- Applying functions to transform individual values
- Applying functions to aggregate values by rows and columns
- Data Transformation for Python for Data Analysts & Quants.
- Modern method chaining with .pipe(), .assign(), and .query()
Missing data in pandas
- Identifying missing data points
- Filtering out missing data
- Filling missing data with given values, interpolation and other filling strategies
Data Analysis in pandas
- Extracting summary statistics over DataFrame objects
- Performing data aggregation queries (groupby() method)
- Aggregating multiple columns in the same query
- Exploratory analysis of new datasets
Data Visualisation in pandas
- Plotting data from a Series or DataFrame object
- Bar plots, line plots, scatter plots, histograms and other common charts
- Basic customisation of charts
Working with multiple tables
- Concatenation of multiple tables based on structure/schema
- Join/merge operations with DataFrame objects based on values
- Reindexing operations, dealing with duplicate labels in the index
- Dealing with duplicate records
- Renaming columns
Time Series with pandas
- Working with date and time data types in pandas
- Creating ranges of date/time data points
- Indexing by time
- Resampling: data aggregation over time
- Moving window operations, e.g. moving average
- Defining custom calendars, custom business days, custom holidays
Working with text data in DataFrames
- Using the str attribute in pandas objects
- String manipulation functions in pandas
- Filtering data with string pattern matching
SQL databases
- Connecting to SQL databases with SQLAlchemy
- Loading data from SQL to pandas
- Sending SQL queries to a database and retrieving the results in Python and pandas
- SQLAlchemy 2.0 — updated connection and query patterns
NumPy
- Working with multi-dimensional arrays with NumPy
- Arithmetic operations with NumPy arrays
- Vectorised operations with NumPy arrays
- Stats and linear algebra with NumPy
- Slicing and indexing NumPy arrays
- Introduction to Polars for high-performance data processing
Data Visualisation
- Overview on the basic types of charts available with the Python libraries
- Bar plots, line plots, histograms, scatter plots, pie charts
- Customising the layout and format of a chart
- Examples of static visualisation with matplotlib
- Interactive visualisation with Plotly Express
AI-assisted data analysis
using AI tools to generate, debug, and optimise Pandas code
Who should attend
- Finance and Investment Analysts
- Data Scientists and Analysts
- Financial Planners and Advisors
- Graduates and Students in Finance
- Business Professionals in Finance
- Experienced Python developers
Feedback
4.8 out of 5 average
"I hadn't integrated Pandas with Python before joining my company. So it's very useful to consolidate my understanding of such skills via this course." JL, Data Analyst, Python for Data Analytics, March 2021