Automate Excel using Python - Xlwings | Series 2

Master yourself to automate Industry-level datasets in Excel with hands-on experience on "3 Case Studies" using Python

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Automate Excel using Python - Xlwings | Series 2

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

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 i.e. dataframe 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. You would learn the most modern interactive chart with required customization.

In the Series, 3 Case studies has been picked from Industry process line. And correspondingly, Python macros are created to:

reduce time in analysis
enable customization using python packages
reduce macros code lines in VBA codebase.
create customized User-defined modules by python functions.

The "Data Science" field is mainly composed of 3 stages:
a. Data Acquisition: The method of fetching data from the storage like Excel here.

b. Data Wrangling: The method of filtering, extracting the required data from the raw dataset.

c. Data Visualization: The method of visualizing the data in the best possible form of chart. Here, we target at showing the most modern interactive chart.