Python for Data Analysts & Quants training course

Installation & Packaging

• The Anaconda distribution as Python Data Science platform
• Overview on Python virtual environment set-up
• Running Python code in Jupyter notebook
• Installation, packaging and virtualisation of Python using Conda.
• We'll set up Python using the Anaconda distribution, a free and enterprise-ready Python distribution that includes hundreds of the most popular Python packages for science, math, engineering and data analysis.
• Anaconda comes with Conda, a cross-platform tool for managing packages and virtual environments. We'll also set up Jupyter, a web-based interactive environment where users can organise, write and run their Python code in notebooks.

Environment Set-up

• The Anaconda distribution as Python Data Science platform
• Overview on Python virtual environment set-up
• Running Python code in Jupyter notebook

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

• Handling 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

• 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

• Data Visualisation with matplotlib and plotly

• 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
• Examples of interactive visualisation with plotly