In this four-day course, students will learn strategies using Python for common tasks in business analytics and finance, including data exploration, reporting and modeling.
We will use Anaconda, the leading open data science platform powered by Python, to ensure efficiency.
The course combines both hands-on exploration and guided lessons in an interactive environment ideally suited for exploratory analytics and development.
What You Will Learn
During this course, you will use Anaconda and the PyData stack to learn how to:
- access and prepare data from various sources
- clean or transform data to facilitate deeper analysis
- explore multidimensional datasets through through summary statistics and visualization
- analyze time-series and cross-sectional data
- construct models for prediction and statistical inference
- report findings with a mixture or text, code and graphics
- create reproducible environments for sharing analysis with collaborators
- write performant and idiomatic Python that is documented and backed by a test
- financial analysis using the core Python language and standard library
- Jupyter notebooks for interactive programming, analytics and reporting
- conda for managing packages and run-time environments
- manipulating, summarizing and visualizing data in Python with Pandas
- reading & writing data in Python with Pandas:
- databases (e.g., MySQL, Postgres, MS SQL Server, Oracle)
- spreadsheets (e.g., Excel)
- fast array-oriented data formats (e.g., HDF5, NetCDF)
- Python test suites using pytest
- custom, interactive Python data visualization with Matplotlib and Bokeh
The content is presented in directly relevant contexts for finance professionals and business analysts. Typical tasks showcased include trading strategies, pricing securities, risk analysis, summarizing
data and report generation.
Who Should Attend
Our primary audience consists of business analysts and finance professionals. Data scientists and data engineers can also benefit from this course, especially if they are new to Python and Anaconda but have experience with other programming languages, such as Java or R.
This course has a limit of 20 participants.
We assume participants are familiar, but not necessarily proficient with, a programming language. We also assume familiarity with some data analysis tools (Excel, tableau, R, SAS, SPSS, Matlab) and tasks. Previous experience with Python is helpful but not required.