Dataset Description:

The myusabank.csv dataset contains daily financial data for a fictional bank (MyUSA Bank) over a two-year period. It includes various key financial metrics such as interest income, interest expense, average earning assets, net income, total assets, shareholder equity, operating expenses, operating income, market share, and stock price. The data is structured to simulate realistic scenarios in the banking sector, including outliers, duplicates, and missing values for educational purposes.

Potential Student Tasks:

1. Data Cleaning and Preprocessing:

   • Handle missing values, duplicates, and outliers to ensure data integrity.
   • Normalize or scale data as needed for analysis.

2. Exploratory Data Analysis (EDA):

   • Visualize trends and distributions of financial metrics over time.
   • Identify correlations between different financial indicators.

3. Calculating Key Performance Indicators (KPIs):

   • Compute metrics such as Net Interest Margin (NIM), Return on Assets (ROA), Return on Equity (ROE), and Cost-to-Income Ratio using calculated fields.
   • Analyze the financial health and performance of MyUSA Bank based on these KPIs.

4. Building Tableau Dashboards:

   • Design interactive dashboards to present insights and trends.
   • Include summary cards, bar charts, line charts, and pie charts to visualize financial performance metrics.

5. Forecasting and Predictive Modeling:

   • Use historical data to forecast future financial performance.
   • Apply regression or time series analysis to predict market share or stock price movements.

6. Business Insights and Reporting:

   • Interpret findings to derive actionable insights for bank management.
   • Prepare reports or presentations summarizing key findings and recommendations.

Educational Goals:

The dataset aims to provide hands-on experience in data preprocessing, analysis, and visualization within the context of banking and finance. It encourages students to apply data science techniques to real-world financial data, enhancing their skills in data-driven decision-making and strategic analysis.

Netflix is a popular streaming service that offers a vast catalog of movies, TV shows, and original contents. This dataset is a cleaned version of the original version which can be found here. The data consist of contents added to Netflix from 2008 to 2021. The oldest content is as old as 1925 and the newest as 2021. This dataset will be cleaned with PostgreSQL and visualized with Tableau. The purpose of this dataset is to test my data cleaning and visualization skills. The cleaned data can be found below and the Tableau dashboard can be found here .

Data Cleaning

We are going to: 1. Treat the Nulls 2. Treat the duplicates 3. Populate missing rows 4. Drop unneeded columns 5. Split columns Extra steps and more explanation on the process will be explained through the code comments

--View dataset

SELECT * 
FROM netflix;

--The show_id column is the unique id for the dataset, therefore we are going to check for duplicates
                                  
SELECT show_id, COUNT(*)                                                                                      
FROM netflix 
GROUP BY show_id                                                                                              
ORDER BY show_id DESC;

--No duplicates

--Check null values across columns

SELECT COUNT(*) FILTER (WHERE show_id IS NULL) AS showid_nulls,
    COUNT(*) FILTER (WHERE type IS NULL) AS type_nulls,
    COUNT(*) FILTER (WHERE title IS NULL) AS title_nulls,
    COUNT(*) FILTER (WHERE director IS NULL) AS director_nulls,
    COUNT(*) FILTER (WHERE movie_cast IS NULL) AS movie_cast_nulls,
    COUNT(*) FILTER (WHERE country IS NULL) AS country_nulls,
    COUNT(*) FILTER (WHERE date_added IS NULL) AS date_addes_nulls,
    COUNT(*) FILTER (WHERE release_year IS NULL) AS release_year_nulls,
    COUNT(*) FILTER (WHERE rating IS NULL) AS rating_nulls,
    COUNT(*) FILTER (WHERE duration IS NULL) AS duration_nulls,
    COUNT(*) FILTER (WHERE listed_in IS NULL) AS listed_in_nulls,
    COUNT(*) FILTER (WHERE description IS NULL) AS description_nulls
FROM netflix;

We can see that there are NULLS. 
director_nulls = 2634
movie_cast_nulls = 825
country_nulls = 831
date_added_nulls = 10
rating_nulls = 4
duration_nulls = 3 

The director column nulls is about 30% of the whole column, therefore I will not delete them. I will rather find another column to populate it. To populate the director column, we want to find out if there is relationship between movie_cast column and director column

-- Below, we find out if some directors are likely to work with particular cast

WITH cte AS
(
SELECT title, CONCAT(director, '---', movie_cast) AS director_cast 
FROM netflix
)

SELECT director_cast, COUNT(*) AS count
FROM cte
GROUP BY director_cast
HAVING COUNT(*) > 1
ORDER BY COUNT(*) DESC;

With this, we can now populate NULL rows in directors 
using their record with movie_cast 

UPDATE netflix 
SET director = 'Alastair Fothergill'
WHERE movie_cast = 'David Attenborough'
AND director IS NULL ;

--Repeat this step to populate the rest of the director nulls
--Populate the rest of the NULL in director as "Not Given"

UPDATE netflix 
SET director = 'Not Given'
WHERE director IS NULL;

--When I was doing this, I found a less complex and faster way to populate a column which I will use next

Just like the director column, I will not delete the nulls in country. Since the country column is related to director and movie, we are going to populate the country column with the director column

--Populate the country using the director column

SELECT COALESCE(nt.country,nt2.country) 
FROM netflix AS nt
JOIN netflix AS nt2 
ON nt.director = nt2.director 
AND nt.show_id <> nt2.show_id
WHERE nt.country IS NULL;
UPDATE netflix
SET country = nt2.country
FROM netflix AS nt2
WHERE netflix.director = nt2.director and netflix.show_id <> nt2.show_id 
AND netflix.country IS NULL;


--To confirm if there are still directors linked to country that refuse to update

SELECT director, country, date_added
FROM netflix
WHERE country IS NULL;

--Populate the rest of the NULL in director as "Not Given"

UPDATE netflix 
SET country = 'Not Given'
WHERE country IS NULL;

The date_added rows nulls is just 10 out of over 8000 rows, deleting them cannot affect our analysis or visualization

--Show date_added nulls

SELECT show_id, date_added
FROM netflix_clean
WHERE date_added IS NULL;

--DELETE nulls

DELETE F...

Analyze Tableau's unique SaaS business model: From perpetual licenses to enterprise deals. Key benchmarks and data from their S-1 for startup founders.

About Datasets:

Domain : Finance Project: Bank loan of customers Datasets: Finance_1.xlsx & Finance_2.xlsx Dataset Type: Excel Data Dataset Size: Each Excel file has 39k+ records

KPI's: 1. Year wise loan amount Stats 2. Grade and sub grade wise revol_bal 3. Total Payment for Verified Status Vs Total Payment for Non Verified Status 4. State wise loan status 5. Month wise loan status 6. Get more insights based on your understanding of the data

Process: 1. Understanding the problem 2. Data Collection 3. Data Cleaning 4. Exploring and analyzing the data 5. Interpreting the results

This data contains bar chart, text, stacked bar chart, dashboard, horizontal bars, donut chart, area chart, treemap, slicers, table, image.

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The Tableau consulting services market is experiencing robust growth, driven by the increasing adoption of data visualization and business intelligence (BI) solutions across diverse industries. The market, estimated at $2.5 billion in 2025, is projected to exhibit a Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033, reaching approximately $7.5 billion by 2033. This growth is fueled by several key factors, including the rising demand for data-driven decision-making, the need for efficient data analysis and reporting, and the increasing complexity of data sources. Large enterprises are the primary drivers of market expansion, leveraging Tableau's capabilities for comprehensive data analysis across various departments. However, the SME segment is showing significant growth potential as they increasingly recognize the value proposition of data visualization for improved operational efficiency and strategic planning. The market is segmented by service type, with online services gaining traction due to their flexibility and accessibility, while offline services continue to hold significant market share, driven by the requirement for personalized consulting and on-site training. Geographic expansion, particularly in rapidly developing economies in Asia-Pacific, contributes to the overall market growth. However, factors such as the high initial investment cost for implementing Tableau solutions and the need for skilled professionals to effectively utilize the platform act as restraints. The competitive landscape is characterized by a mix of large multinational consulting firms like Deloitte, Accenture, and Wipro, alongside specialized Tableau consulting firms like AlphaConverge and Latticework Insights. These firms offer a range of services, including implementation, customization, training, and ongoing support. The market’s future growth will hinge on several factors: the development of advanced analytics capabilities within Tableau, the expansion of its integration with other business applications, and the continued increase in the availability of skilled Tableau consultants globally. A key trend is the increasing demand for cloud-based Tableau solutions, reflecting the broader shift towards cloud computing in the enterprise sector. Furthermore, the development of industry-specific solutions built on Tableau is likely to drive further market growth and specialization.

Unlock the potential of data visualization with our in-depth analysis of the booming Tableau Services market. Discover key trends, growth drivers, and regional market share projections for 2025-2033, including insights into consulting, maintenance, and development services across enterprise segments. Learn more about leading companies and future opportunities.

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Accoleds 2015 presentation on the various data and visualization tools.

Traffic analytics, rankings, and competitive metrics for tableau.com as of September 2025

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The Tableau Server License market has witnessed significant growth as organizations increasingly turn to data visualization tools to make informed business decisions. Tableau Server provides a robust platform that allows users to publish, share, and collaborate on interactive data visualizations, helping companies s

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📊 Sales & Customer Analytics – Tableau Dashboard (PDF & Interactive) 🔍 Overview This dataset includes a Tableau project analysing sales trends & customer insights with an interactive dashboard switch.

The dashboards provide actionable insights into: ✅ Sales performance & revenue trends 📈 ✅ Top-performing products & regions 🌍 ✅ Customer segmentation & behavior analysis 🛍️ ✅ Retention strategies & marketing impact 🎯

📂 Files Included 📄 Sales & Customer Analytics Dashboard (PDF Report) – A full summary of insights. 🎨 Tableau Workbook (.twbx) – The interactive dashboards (requires Tableau). 🖼️ Screenshots – Previews of the dashboards.

🔗 Explore the Interactive Dashboards on Tableau Public :

Sales Dashboard:[https://public.tableau.com/app/profile/egbe.grace/viz/SalesCustomerDashboardsDynamic_17385906491570/CustomerDashboard] Customer Dashboard: [https://public.tableau.com/app/profile/egbe.grace/viz/SalesCustomerDashboardsDynamic_17385906491570/CustomerDashboard]

📌 Key Insights from the Dashboards ✅ Revenue trends show peak sales periods & seasonal demand shifts. ✅ Top-selling products & regions help businesses optimize their strategies. ✅ Customer segmentation identifies high-value buyers for targeted marketing. ✅ Retention analysis provides insights into repeat customer behaviour.

💡 How This Can Help: This dataset and Tableau project can help businesses & analysts uncover key patterns in sales and customer behavior, allowing them to make data-driven decisions to improve growth and customer retention.

💬 Would love to hear your feedback! Let’s discuss the impact of sales analytics in business strategy.

📢 #DataAnalytics #Tableau #SalesAnalysis #CustomerInsights #BusinessIntelligence #DataVisualization

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