Online Course top-rated
Growth Analysis with SQL, Python, and Tableau

Name: Growth Analysis with SQL, Python, and Tableau Course
Price: 36 USD

with Nikola Pulev

Gain first-hand data analyst experience: Use SQL, Python, and Tableau to tackle real-world business challenges

4.8

863 reviews on

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1,576 students already enrolled

Skill level:

Advanced

Duration:

4 hours

Lessons (4 hours)

CPE credits:

5.5

CPE stands for Continuing Professional Education and represents the mandatory credits a wide range of professionals must earn to maintain their licenses and stay current with regulations and best practices. One CPE credit typically equals 50 minutes of learning. For more details, visit NASBA's official website: www.nasbaregistry.org

Accredited

certificate

What you learn

Improve your SQL, Python, and Tableau proficiency using real business data.
Interpret subscription and transaction data to find growth insights.
Create professional, multi-page Tableau dashboards with clean formatting.
Use data to spot challenges, analyze trends, and propose improvements.
Create a portfolio‑ready project with sophisticated Tableau visualizations.

Topics & tools

Tableau DashboardMySQL queryKey Performance IndicatorsFunnelCombo ChartLine chartData AnalysisStacked Bar ChartBar chartWaterfall ChartGrowth MetricsHover TooltipData VisualizationDashboard ReportingData PreprocessingTableauSqlPython

Your instructor

Nikola Pulev

Data Scientist | Course Creator at 365 Data Science | University of Cambridge Physics graduate

Track record

Bringing real-world expertise from leading global companies

Academic background

Bachelor's degree, Natural Sciences, Physics

Media and recognition

Silver Medal, International Physics Olympiad
National and international awards in mathematics and physics competitions

Ask Nikola a question

Course overview

Accredited certificates

Course OVERVIEW

Description

CPE Credits: 5.5 Field of Study: Information Technology
Delivery Method: QAS Self Study

Every company aims to maximize its market presence. Consequently, growth metrics become a primary focus for analysis and monitoring month over month. In this course, we’ll develop a dashboard for tracking growth data. Utilizing SQL and Python, we'll extract relevant data from 365's database and import it into Tableau to create a professional, multi-page dashboard. Finally, we'll explore insights and findings from the visualizations.

Prerequisites

Any SQL environment (such as SQLite, MySQL Workbench, or an online SQL editor).
Tableau Desktop or Tableau Public
Python (version 3.8 or later), pandas library, and a code editor or IDE (e.g., Jupyter Notebook, Spyder, or VS Code)

Advanced preparation

Curriculum

55 lessons 1 exam

1. Introduction to the Course

9 min

This section introduces the course and covers what you’ll learn. We also touch on the prerequisites: SQL, Python, and Tableau fundamentals.

9 min

This section introduces the course and covers what you’ll learn. We also touch on the prerequisites: SQL, Python, and Tableau fundamentals.

What Does the Course Cover? Free

Motivation - Our Story
2. Case Study Overview

32 min

This part introduces the terminology and highlights the essential questions for a growth dashboard. These questions will guide us in designing the dashboard's layout and graphs.

32 min

This part introduces the terminology and highlights the essential questions for a growth dashboard. These questions will guide us in designing the dashboard's layout and graphs.

Introduction to the Project Free

Term Definitions - Visitors, UTMs, Channel of Acquisition Free

Term Definitions - User Types and Funnels

Key Questions and Metrics

Sketching the Dashboard (Part 1)

Sketching the Dashboard (Part 2)
3. Retrieving Relevant Data from the Database

84 min

Here, we explore the 365 database utilized in this Growth Analysis with SQL, Python, and Tableau course—focusing on using MySQL to extract necessary data sources for graph creation. Additionally, Python will be employed to retrieve the data source for the waterfall chart.

84 min

Here, we explore the 365 database utilized in this Growth Analysis with SQL, Python, and Tableau course—focusing on using MySQL to extract necessary data sources for graph creation. Additionally, Python will be employed to retrieve the data source for the waterfall chart.

Types of Data Access Free

The Database - Part 1

The Database - Part 2

Funnels - Theory

Funnel - SQL Query (Part 1)

Funnel - SQL Query (Part 2)

Funnel - SQL Query (Part 3)

Waterfall Chart - Theory

Waterfall Chart - SQL Query

Waterfall Chart - Processing the Data in Python (Part 1)

Waterfall Chart - Processing the Data in Python (Part 2)

Waterfall Chart - Processing the Data in Python (Part 3)

Assignment 1 - Create SQL queries

Waterfall Chart - Data Export (Part 4)
4. Crafting the Graphs

75 min

Once all data sources are ready, we'll start crafting various graphs in Tableau for the dashboard, including (among others) combined bar and line charts, stacked bars, and waterfall charts. Along the way, we'll tackle unique challenges and innovative solutions—like adjusting the axis minimum, toggling labels, and conditional number formatting.

75 min

Once all data sources are ready, we'll start crafting various graphs in Tableau for the dashboard, including (among others) combined bar and line charts, stacked bars, and waterfall charts. Along the way, we'll tackle unique challenges and innovative solutions—like adjusting the axis minimum, toggling labels, and conditional number formatting.

The Structure of This Section Free

The Free and Conversions Graph (Part 1) Free

The Free and Conversions Graph (Part 2)

The Free and Conversions Graph (Part 3)

The Free and Conversions Graph (Part 4)

The Free and Conversions Graph (Part 5)

Assignment 2 - Labels toggle

The Free and Conversions Graph (Part 6)

Paid per Date Graph

Paid per Date – Styling and Formatting

Waterfall Chart

Assignment 3 - Other Waterfall Chart Periods

Visitors Bar Chart

Assignment 4 - Create Visitors Bar Chart for Monthly

Homepage KPIs

Assignment 5 - Homepage KPIs for Quarterly and Annual

The Rest of the Graphs

Assignment 6 - Create Maps and Funnel
5. Assembling the Dashboard

37 min

We'll organize all completed graphs across various dashboard pages, utilizing containers and navigation buttons disguised as filters to enhance user experience.

37 min

We'll organize all completed graphs across various dashboard pages, utilizing containers and navigation buttons disguised as filters to enhance user experience.

Dashboard Beginings – The Title Banner

The Title Banner (Part 2)

Incorporating the Graphs into the First Page

The First Page – Continuation

Constructing the Second Page – Sheet Swapping

Assignment 7 - Populate Pages 3,4 and 5

Assignment 8 - Create the Homepage

Constructing the Homepage
6. Discussions and Insights

25 min

The Growth Analysis with SQL, Python, and Tableau course’s final section is dedicated to analyzing the dashboard, gaining valuable insights, and suggesting changes that would ideally improve the platform’s performance and increase the company’s revenue.

25 min

The Growth Analysis with SQL, Python, and Tableau course’s final section is dedicated to analyzing the dashboard, gaining valuable insights, and suggesting changes that would ideally improve the platform’s performance and increase the company’s revenue.

The Significance of Data Interpretation

Free Users Data

Paid Users Data

Waterfall Chart – Subscriptions

Visitors Data

Maps

Closing Thoughts
7. Course exam

40 min

40 min

Course exam

Free lessons

1.1 What Does the Course Cover?

4 min

1.2 Motivation - Our Story

5 min

2.1 Introduction to the Project

3 min

2.2 Term Definitions - Visitors, UTMs, Channel of Acquisition

9 min

2.3 Term Definitions - User Types and Funnels

5 min

2.4 Key Questions and Metrics

5 min

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With a background in economics and over five years of experience in project management across private and international organizations—including a European Union project in Sudan—I sought new opportunities after relocating to the UAE. Transitioning into data analytics, I began my journey with the 365 Data Science platform, which provided an outstanding and well-structured learning experience. The platform's practical approach and high-quality courses helped me progress from a beginner to a proficient data analyst.

Rabab A.

Before 365: Project Coordinator | SOFRECO

After 365: Freelance Data Analyst

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96%

of our students recommend

365 Data Science.

As a Data Science student, I feared graduating without the practical skills to succeed. 365 Data Science gave me exactly what I needed—projects, career tracks, and in-depth courses in Python, R, SQL, Excel, Power BI, Tableau, and more. I applied these skills to university projects, built advanced solutions like chatbots, and understood the big picture. The structured learning, real-world applications, and interview preparation boosted my confidence, helping me become the top student in my class and ready for the industry.

Luis B. D. M.

After 365: Data Science Alumni from NOVA IMS

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4.8

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365 Data Science was absolutely essential in getting me where I am today. The curriculum gave me a solid foundation in data analysis, data science, machine learning, and deep learning—both theory and practice. The structured approach and hands-on projects made complex topics easy to understand and apply. Earning certificates boosted my confidence and LinkedIn profile, helping me stand out in interviews. That momentum led me to internships and, ultimately, to my current role as an AI Engineer.