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  3. Python and Tableau The Complete Data Analytics Bootcamp

Python and Tableau The Complete Data Analytics Bootcamp

In this course, you will be using your skills as a data analyst to extract knowledge from data so you can analyze and visualize data with ease.

Devasha Naidoo
Devasha Naidoo
Data Science | core | 11 hours |   Published: Jun 2022
In partnership with:  Coursera

    Discussions

Overview

1.9KSTUDENTS*
95.7%RECOMMEND*

This course includes:

  • 11 hours of on-demand video 
  • Certificate of completion 
  • Direct access/chat with the instructor
  • 100% self-paced online
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 to 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

Skills You Will Gain

Data Analysis
Python
Tableau

Learning Outcomes (At the end of this program you will be able to)

  • In this course, you’ll understand the inner workings of the data analytics pipeline by using Python for Data Cleaning & Analysis and Tableau for Data Visuals
  • Be able to program in Python and execute code
  • Create Interactive Charts and Dashboards on Tableau
  • Create a portfolio of 3 real-life Python/Tableau projects to apply for data analytics jobs
  • Learn how to perform Data Analytics on Python with Pandas, Numpy, Matplotlib, and Others

Prerequisites

  • Basic Knowledge of Computers
  • Basic Knowledge of Microsoft Excel

Who Should Attend

  • Anyone who wants to kickstart their career in Data Analytics
  • Anyone who wants to learn more about Tableau
  • Anyone who wants to learn more about Python

Curriculum

Instructors

Frequently Asked Questions

How much do the courses at Starweaver cost?

We offer flexible payment options to make learning accessible for everyone. With our Pay-As-You-Go plan, you can pay for each course individually. Alternatively, our Subscription-Based plan provides you with unlimited access to all courses for a monthly or yearly fee.

Do you offer any certifications upon completion of a course at Starweaver?

Yes, we do offer a certification upon completion of our course to showcase your newly acquired skills and expertise.

Does Starweaver offer any free courses or trials?

No, we don't offer any free courses, but we do offer 5-day trial only on our subscriptions-based plans.

Are Starweaver's courses designed for beginners or advanced students?

Our course is designed with three levels to cater to your learning needs - Core, Intermediate, and Advanced. You can choose the level that best suits your knowledge and skillset to enhance your learning experience.

What payment options are available for Starweaver courses?

We accept various payment methods such as major credit cards, PayPal, wire transfer, and company purchase orders. For more information related to payments contact customer support.

Do you offer refunds?

Yes, we do offer a 100% refund guarantee for our courses within a specified time frame. If you are not satisfied with the course, contact our customer support team to request a refund with your order details. Some restrictions may apply.

*Where courses have been offered multiple times, the “# Students” includes all students who have enrolled. The “%Recommended” shown is also based on this data.
Devasha Naidoo

Devasha Naidoo

Devasha is a seasoned data professional with a diverse background spanning Data Visualization, Analytics, Data Warehousing, Data Science, and Python Programming. Complementing these core skills are proficiencies in R and Web Development technologies such as HTML, CSS, and JavaScript. With an academic foundation in Chemical Engineering, Devasha has seamlessly transitioned analytical thinking into the realm of data, establishing a reputation for making complex concepts accessible and actionable.

As a Udemy Instructor, Devasha has impacted over 80,000 students worldwide, delivering courses that blend practical knowledge with hands-on experience. Currently serving as Lead Facilitator at The London School of Economics and Political Science (LSE), she plays a key role in driving curriculum implementation that connects academic rigor with real-world relevance. Her focus is on ensuring students not only understand theoretical frameworks but can also apply them effectively in industry settings.

In addition, Devasha continues to innovate at Databudd, where she designs and delivers content that simplifies data analytics, technology, and AI for a global audience. Her career includes influential roles such as Technology Architect at Vantage Data and Analytics Manager at iKhokha, where she led large-scale data projects and developed tools that turned raw data into business intelligence. Through these roles, she has demonstrated a keen ability to create interactive visualizations, apply advanced analytics, and generate insights that drive strategic decisions.

Devasha is passionate about empowering others through data literacy and remains committed to bridging the gap between data theory and practical application across both academic and professional landscapes.

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1Module 1 - Introduction
2Module 2 - Project 1: Value Inc. Sales Analysis
3Module 3 - Project 2: Blue Bank Loan Analysis
4Module 4 - Project 3: BlogMe Sentiment Analysis
5Module 5 - Tableau - Project 1: Value Inc
6Module 6 - Tableau - Project 2: Blue Bank Loan Analysis
7Module 7 - Tableau - Project 3: BlogMe Sentiment Analysis

Segment - 15 - Working with JSON Data

Segment - 16 - Working with Arrays

Segment - 17 - If Statements and For Loops

Segment - 18 - Running our Final Script

Segment - 19 - Try and Except and Plots

Segment - 36 - Reviewing What We Will Be Building

Segment - 37 - Connecting to the Data

Segment - 38 - Building Charts - Part 1

Segment - 39 - Building Charts - Part 2

Segment - 40 - Building and Publishing the Dashboard

Segment - 01 - Introduction to the Course

Segment - 02 - Introduction to Python

Segment - 03 - How to Install Python on Windows

Segment - 04 - How to Install Python on Mac

Segment - 05 - How to Install Tableau Public

Segment - 06 - How the Course is Structured

Segment - 07 - Introduction to Spyder

Segment - 08 - Introduction to Python and the Spyder Layout

Segment - 09 - What Happens When We Close Spyder

Segment - 10 - Working with Pandas and Reading CSV Files

Segment - 11 - Working with Calculations on Python

Segment - 12 - Concatenating Variables and Working with iloc

Segment - 13 - Running our Final Script

Segment - 14 - Using Various Functions in Python

Segment - 20 - Group By and Aggregates

Segment - 21 - Functions in Python

Segment - 22 - Classes in Python

Segment - 23 - Sentiment Analysis

Segment - 31 - Reviewing What We Will Be Building

Segment - 32 - Connecting to the Data

Segment - 33 - Building Charts - Part 1

Segment - 34 - Building Charts - Part 2

Segment - 35 - Building and Publishing Dashboard

Segment - 24 - Introduction to Tableau Public

Segment - 25 - Reviewing What We Will Be Building

Segment - 26 - Connecting to the Data Source

Segment - 27 - Creating a Line Chart

Segment - 28 - Creating Bar Charts

Segment - 29 - Creating Maps and Headline Cards

Segment - 30 - Creating Dashboards and Publishing