Automate Excel using Python - Xlwings | Series 1

Master yourself to automate Industry-level datasets in Excel with hands-on experience on "2 Case Studies" using Python

Ratings: 2.62 / 5.00




Description

This course teaches how to automate Industry-level datasets maintained in Excel sheets using Python programming language.

MS Excel is a very helpful tool for record-keeping. But the language that comes by default for macro is VBA, which is dated. And in the field of Data Analysis, Python has a lot of interesting packages which makes a job easy. Package like xlwings links any Excel with Python macros. Packages like Pandas, takes data into tabular format and also has customized filtering of rows or columns for complex data analysis. Packages like Matplotlib, Plotly enables to create different plots - line plot, Scatter plot, Heat map for finding the correlation b/w different parameters.

In the Series, 2 Case studies has been picked from Industry process line. And correspondingly, Python macros are created to:

  1. reduce time in analysis

  2. enable customization using python packages

  3. reduce macros code lines in VBA codebase.

  4. create customized User-defined modules by python functions.

The Series-2 is also available now in the Instructor's profile.

What You Will Learn!

  • Writing macros (in python) for their Excel sheets
  • Automate Excel without any VBA code (installed by default)
  • Build your own Formulas by coding User-defined Functions (UDFs) in Python
  • Practical hands-on experience with Industry level datasets
  • Automate, streamline and completely revolutionize your Excel workflow with Python macros
  • Master unique concepts, tools and case studies, which you won't find ANY other course, guaranteed
  • Get LIFETIME access to course materials and a 1-on-1 expert support
  • Continuous course updation when python packages updated with new features

Who Should Attend!

  • Beginner developers who want to make their career on Data Science
  • People working as an Analyst, using Excel mostly