[32% Off] Python and Tableau: The Complete Data Analytics Bootcamp! Course Coupon – Free learning guide

[32% Off] Python and Tableau: The Complete Data Analytics Bootcamp! Course Coupon


Welcome to the most comprehensive Data Analytics course available on Udemy! When you become a Data Analyst, there are two things that you should be skilled at in order to be a master data analyst, Python and Tableau!

This course is a great choice for beginners looking to expand their skills in Data Analytics. You also create a solid portfolio of your work online and can link it on your resume.

At 11+ hours, this Python and Tableau course will teach you the core principles of Data Analytics at every stage in the pipeline. Even if you have no programming experience, this course will take you from beginner to professional.

Why you should take this course:

  • Our learning is going to be project-centric: we will tackle the core concepts in Python and Tableau as it comes up in the real life projects we’ll be working on.

  • You get experience in both Python and Tableau – the two powerhouses in the data analytics community

  • You also understand how you can clean/analyze data in Python and then visualize it in Tableau – an end-to-end process that many data analysts use today.

  • You get a chance to showcase your work in a portfolio online

What are the Projects you will work on?

Sales Analysis for Value Inc: We’re going to be working on a Sales Analysis for a popular retail company called Value Inc.

Here we use Python for the following to clean our data and we use popular libraries like pandas and learn how to work with dataframes, perform calculations and much more. We are then going to extract that data to Tableau where we learn how to create calculations, parameters and develop a dashboard for the sales manager.

Loan Analysis for Blue Bank: We’re also going to be working on a Loan Analysis for a bank. Using Python and Tableau, blue bank wants to see an analysis of people who have taken loans but might have issues paying back. We learn how to read JSON files, work with lists, dictionaries, Numpy and if statements. We also learn how to do exceptions, for loops and plots in Python. Once that’s done, we extract the data to Tableau and build charts, work with Tableau Calculations and create an interactive dashboard.

Sentiment and Keyword Analysis: We’re also going to be doing a sentiment and keyword analysis for Blog Me. A blogging website that would like us to analyze news headlines and extract a sentiment from these articles. Here, we use a Natural Language Processing Library called Vader, that can extract sentiment on Python. We also learn about functions and classes on Python. Once we extract the data to Tableau, we learn how to perform Tableau Joins, work with sets and context filtering and finally create an interactive dashboard.

In this course you will learn the following:


  1. Install and Set Up Anaconda

  2. Learn about python variables and data types

  3. Learn about strings, integers

  4. Using lists, tuples and dictionaries

  5. Creating functions

  6. Using if statements and for/while loops

  7. Creating exception handling

  8. Using the exponent function

  9. Working with comments

  10. Reading and writing files

  11. Scripting classes and objects

  12. Sentiment Analysis


  1. Connecting to a data source

  2. Understanding tableau joins and unions

  3. Rename a data field

  4. Assign an alias to a data value

  5. Assign a geographic role to a data field

  6. Change data type for a data field

  7. Change default aggregation properties

  8. Bar Chart

  9. Line Chart

  10. Map

  11. Dual Axis

  12. Create a set

  13. Add a filter

  14. Manual and computed sorts

  15. Reference lines

  16. Table calculations

  17. Bins and Histograms

  18. Calculated fields

  19. Parameters

  20. Create a dashboard

  21. Add dashboard actions

  22. Download dashboards

By the time you complete this course, you’ll be a highly proficient Python and Tableau user. You will be using your skills as a data analyst to extract knowledge from data so you can analyze and visualize data with ease.

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