Python Data Analyst Training Overview
Accelebrate's Python for Data Analysis training course teaches data analysts how to search, manipulate, and analyze data using the powerful Python programming language.
Location and Pricing
Accelebrate offers instructor-led enterprise training for groups of 3 or more online or at your site. Most Accelebrate classes can be flexibly scheduled for your group, including delivery in half-day segments across a week or set of weeks. To receive a customized proposal and price quote for private corporate training on-site or online, please contact us.
In addition, some courses are available as live, instructor-led training from one of our partners.
Objectives
- Extract data from binary files or other binary data streams
- Create data structures using classes and named tuples
- Search and replace text with regular expressions
- Read and write CSV and other data formats
- Serialize data to pickle files, JSON, and XML
- Consume and process data from the Web
- Deal with missing data
- Share data with Excel spreadsheets
- Analyze data with SciPy/NumPy
Prerequisites
All attendees should have basic Python programming skills.
Outline
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Introduction
File I/O
- Opening a file
- Iterating over lines
- Reading characters or bytes
- Reading all lines
- Formatted output
- Using fileinput
Classes
- Defining classes
- Constructors
- Instance methods and data
- Class/static methods and data
Generators and Other Iterables
- Iterables
- Saving memory with generators
- Generator expressions
- Generator functions
- Generator classes
- Stacking generators
Data Structures
- How to store data
- The basics: lists and tuples
- Named access with dictionaries
- Named tuples: best of both worlds
- Using classes as data structures
Consuming Data from the Web
- Web data sources
- Data via URL
- RESTful data
- Screen-scraping
Excel Spreadsheets
- The xlrd, xlwr, and xlutil modules
- Reading an existing spreadsheet
- Creating a spreadsheet from scratch
- Modifying an existing spreadsheet
Dates and Times
- Python date and time objects
- The time module
- Using calendars
- Converting between formats
- Parsing and printing
- Time zones
Regular Expressions
- RE syntax overview
- Basic patterns
- RE objects
- Searching and matching
- Compilation flags
- Grouping
- Replacing text
- Splitting a string
Working with Binary Data
- Isn't all data binary?
- Binary file handling
- Parsing raw data
- Writing a binary stream
Analyzing Datasets
- Sorting data
- Filtering values
- Basic statistics
- Leveraging SciPy/NumPy
- Using pandas
Bigger Data - Working with PyTables
- About HDF5 data
- Using PyTables
- Reading a dataset
- Pulling data
- Updating the dataset
- Writing to HDF5
Conclusion
Training Materials
All Python training attendees receive comprehensive courseware.
Software Requirements
- Any Windows, Linux, or macOS operating system
- Anaconda Python 3.5 or later
- A text editor or IDE (PyCharm Community Edition recommended)
