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Python for Excel Users First Steps

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Topic: Business
George Mount

Spreadsheets are the tool of choice for data analysis and reporting among a majority of business professionals, who are well-acquainted with common data processes like sorting, filtering, and aggregation. But spreadsheets have their limitations, which templates and complex formulas fail to solve.

For quick, dynamic, and repeatable data processes, Python is a helpful addition to your skillset. Join expert George Mount to bridge the gap between spreadsheets and Python. You’ll discover how to pivot your existing knowledge about data into a new application, as you learn Python equivalents of common spreadsheet tasks, how Python can augment and automate common data preparation and manipulation tasks often done in spreadsheets, and more. The thought of learning a programming language may be intimidating, but as a spreadsheet user, you know more about data and coding than you think.

What you'll learn-and how you can apply it

By the end of this live online course, you’ll understand:

  • Python’s powerful universe of open source packages and tools
  • The Python equivalents of common spreadsheet tasks such as PivotTables and lookups
  • Where Python can augment and automate common data preparation and manipulation tasks often done in spreadsheets
  • The role of variables, objects, and functions in Python

And you’ll be able to:

  • Load, view, and write spreadsheet files from Python
  • Perform common data wrangling tasks such as sorting, filtering, and aggregation
  • Navigate and execute code in Jupyter notebooks
  • Identify, install, and implement useful packages for your needs

This training course is for you because...

  • You're an analyst responsible for collecting, analyzing, and interpreting data for business insights.
  • You want to learn how to use Python and its most common packages inside Jupyter notebooks.
  • You’d like to speed up, automate, and validate your reporting and analysis using open source software.
  • You’re a spreadsheet user interested in learning more about data science or software development.

Prerequisites

  • A computer with the Anaconda distribution of Python installed (instructions)
  • A working knowledge of basic tasks and functions in Excel, including sorting and filtering, IF statements, conditional aggregates like SUMIF() and COUNTIF(), and PivotTables and VLOOKUP()
  • No prior programming knowledge required (As an Excel user, you already know more programming than you realize.)

Recommended preparation:

Recommended follow-up:

About your instructor

  • George Mount develops data analytics curriculum and training programs through his company Stringfest Analytics. He’s been featured on industry outlets such as Excel TV and the MyExcelOnline podcast through his website Georgejmount.com. His blog features content on Excel and, more generally, business analytics, training, and career development. He holds a master’s degree in information systems with a certificate of achievement in quantitative methods from Case Western Reserve University.

Schedule

The timeframes are only estimates and may vary according to how the class is progressing

Up and running from spreadsheets to Python (55 minutes)

  • Presentation: Welcome to Planet Python—What is Python, and when would you use it instead of a spreadsheet?; hello from Jupyter—navigating and executing Python code from Jupyter notebooks; from spreadsheet cells and ranges to Python lists and dictionaries—creating, inspecting, and manipulating lists and dictionaries
  • Jupyter Notebook exercise: Assign, index, and subset variables in Python
  • Q&A

Break (5 minutes)

Working with tabular data (55 minutes)

  • Presentation: From spreadsheet tables to Python DataFrames—creating, inspecting, and manipulating DataFrames, importing spreadsheet data into Python; from lookups and PivotTables to pandas manipulation—manipulating tabular data in pandas (sorting, summarizing, merging, reshaping, exporting to spreadsheets)
  • Jupyter Notebook exercise: Manipulate and analyze tabular data with pandas
  • Q&A

Break (5 minutes)

Python for data analysis (60 minutes)

  • Presentation: Data visualization with seaborn—visualizing univariate and bivariate distributions (bar charts, histograms, scatter plots, line charts), customizing plots and themes; from “That’s hard in spreadsheets” to “That’s easy in Python!”—conducting an end-to-end data analysis project (appending, transposing, summarizing, and visualizing a set of CSV files)
  • Jupyter Notebook exercise: Build an end-to-end Python data analysis project from spreadsheet data
  • Q&A