📊 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

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...

Updated 30 January 2023

Version 14 of Dataset

License Update:

There has been some confusion around licensing for this data set. Dr. Carla Patalano and Dr. Rich Huebner are the original authors of this dataset.

We provide a license to anyone who wishes to use this dataset for learning or teaching. For the purposes of sharing, please follow this license:

CC-BY-NC-ND This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Codebook

https://rpubs.com/rhuebner/hrd_cb_v14

PLEASE NOTE -- I recently updated the codebook - please use the above link. A few minor discrepancies were identified between the codebook and the dataset. Please feel free to contact me through LinkedIn (www.linkedin.com/in/RichHuebner) to report discrepancies and make requests.

Context

HR data can be hard to come by, and HR professionals generally lag behind with respect to analytics and data visualization competency. Thus, Dr. Carla Patalano and I set out to create our own HR-related dataset, which is used in one of our graduate MSHRM courses called HR Metrics and Analytics, at New England College of Business. We created this data set ourselves. We use the data set to teach HR students how to use and analyze the data in Tableau Desktop - a data visualization tool that's easy to learn.

This version provides a variety of features that are useful for both data visualization AND creating machine learning / predictive analytics models. We are working on expanding the data set even further by generating even more records and a few additional features. We will be keeping this as one file/one data set for now. There is a possibility of creating a second file perhaps down the road where you can join the files together to practice SQL/joins, etc.

Note that this dataset isn't perfect. By design, there are some issues that are present. It is primarily designed as a teaching data set - to teach human resources professionals how to work with data and analytics.

Content

We have reduced the complexity of the dataset down to a single data file (v14). The CSV revolves around a fictitious company and the core data set contains names, DOBs, age, gender, marital status, date of hire, reasons for termination, department, whether they are active or terminated, position title, pay rate, manager name, and performance score.

Recent additions to the data include: - Absences - Most Recent Performance Review Date - Employee Engagement Score

Acknowledgements

Dr. Carla Patalano provided the baseline idea for creating this synthetic data set, which has been used now by over 200 Human Resource Management students at the college. Students in the course learn data visualization techniques with Tableau Desktop and use this data set to complete a series of assignments.

Inspiration

We've included some open-ended questions that you can explore and try to address through creating Tableau visualizations, or R or Python analyses. Good luck and enjoy the learning!

• Is there any relationship between who a person works for and their performance score?
• What is the overall diversity profile of the organization?
• What are our best recruiting sources if we want to ensure a diverse organization?
• Can we predict who is going to terminate and who isn't? What level of accuracy can we achieve on this?
• Are there areas of the company where pay is not equitable?

There are so many other interesting questions that could be addressed through this interesting data set. Dr. Patalano and I look forward to seeing what we can come up with.

If you have any questions or comments about the dataset, please do not hesitate to reach out to me on LinkedIn: http://www.linkedin.com/in/RichHuebner

You can also reach me via email at: Richard.Huebner@go.cambridgecollege.edu

This package includes a Tableau file and good/bad figures for the visual sequencing disorder group. Dataset: Medical Cost.csv is used for the creation of visualizations.

This data set is used to determine the best U.S state for 2023, by analyzing many factors such as average household income, average home value, crime rate, education, poverty rate, and job opportunities per state.

The reference for the dataset and the dashboard was Youtube Channel codebasics. I have used a fictitious company called Atlix where the Sales Director want the sales data to be in a proper format which can help in decision making.

We have a total of 5 tables namely customers, products, markets, date & transactions. The data is exported from Mysql to Tableau.

In tableau , inner joins were used.

In the transactions table, we notice that sum sales amount figures are either negative or zero while the sales qty is either 1 or more. This cannot be right. Therefore, we filter the sales amount table in Tableau by having the least sales amount as minimum 1.

When currency column from transactions table was grouped in MySql, we could see ‘USD’ and ‘INR’ showing up. We cannot have a sales data showing two currencies. This was rectified by converting the USD sales amount into INR by taking the latest exchange rate at Rs.81.

We make the above change in tableau by creating a new calculated field called ‘Normalised Sales Amount’. If [Sales Amount] == ‘USD’ then [Sales Amount] * 81 else [Sales Amount] End.

Conclusion: The dashboard prepared is an interactive dashboard with filters. For eg. By Clicking on Mumbai under “Sales by Markets” we will see the results change in the other charts as well as they Will now show the results pertaining only to Mumbai. This can be done by year , month, customers , products etc. Parameter with filter has also been created for top customers and top products. This produces a slider which can be used to view the top 10 customers and products and slide it accordingly.

Following information can be passed on to the sales team or director.

Total Sales: from Jun’17 to Feb’20 has been INR 12.83 million. There is a drop of 57% in the sales revenue from 2018 to 2019. The year 2020 has not been considered as it only account for 2 months data. Markets: Mumbai which is the top most performing market and accounts for 51% of the total sales market has seen a drop in sales of almost 64% from 2018 to 2019. Top Customers: Path was on 2nd position in terms of sales in the year 2018. It accounted for 19% of the total sales after Electricalslytical which accounted for 21% of the total sales. But in year 2019, both Electricalslytical and Path were the 2nd and 4th highest customers by sales. By targeting the specific markets and customers through new ideas such as promotions, discounts etc we can look to reverse the trend of decreasing sales.

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.

AbstractThe H1B is an employment-based visa category for temporary foreign workers in the United States. Every year, the US immigration department receives over 200,000 petitions and selects 85,000 applications through a random process and the U.S. employer must submit a petition for an H1B visa to the US immigration department. This is the most common visa status applied to international students once they complete college or higher education and begin working in a full-time position. The project provides essential information on job titles, preferred regions of settlement, foreign applicants and employers' trends for H1B visa application. According to locations, employers, job titles and salary range make up most of the H1B petitions, so different visualization utilizing tools will be used in order to analyze and interpreted in relation to the trends of the H1B visa to provide a recommendation to the applicant. This report is the base of the project for Visualization of Complex Data class at the George Washington University, some examples in this project has an analysis for the different relevant variables (Case Status, Employer Name, SOC name, Job Title, Prevailing Wage, Worksite, and Latitude and Longitude information) from Kaggle and Office of Foreign Labor Certification(OFLC) in order to see the H1B visa changes in the past several decades. Keywords: H1B visa, Data Analysis, Visualization of Complex Data, HTML, JavaScript, CSS, Tableau, D3.jsDatasetThe dataset contains 10 columns and covers a total of 3 million records spanning from 2011-2016. The relevant columns in the dataset include case status, employer name, SOC name, jobe title, full time position, prevailing wage, year, worksite, and latitude and longitude information.Link to dataset: https://www.kaggle.com/nsharan/h-1b-visaLink to dataset(FY2017): https://www.foreignlaborcert.doleta.gov/performancedata.cfmRunning the codeOpen Index.htmlData ProcessingDoing some data preprocessing to transform the raw data into an understandable format.Find and combine any other external datasets to enrich the analysis such as dataset of FY2017.To make appropriated Visualizations, variables should be Developed and compiled into visualization programs.Draw a geo map and scatter plot to compare the fastest growth in fixed value and in percentages.Extract some aspects and analyze the changes in employers’ preference as well as forecasts for the future trends.VisualizationsCombo chart: this chart shows the overall volume of receipts and approvals rate.Scatter plot: scatter plot shows the beneficiary country of birth.Geo map: this map shows All States of H1B petitions filed.Line chart: this chart shows top10 states of H1B petitions filed. Pie chart: this chart shows comparison of Education level and occupations for petitions FY2011 vs FY2017.Tree map: tree map shows overall top employers who submit the greatest number of applications.Side-by-side bar chart: this chart shows overall comparison of Data Scientist and Data Analyst.Highlight table: this table shows mean wage of a Data Scientist and Data Analyst with case status certified.Bubble chart: this chart shows top10 companies for Data Scientist and Data Analyst.Related ResearchThe H-1B Visa Debate, Explained - Harvard Business Reviewhttps://hbr.org/2017/05/the-h-1b-visa-debate-explainedForeign Labor Certification Data Centerhttps://www.foreignlaborcert.doleta.govKey facts about the U.S. H-1B visa programhttp://www.pewresearch.org/fact-tank/2017/04/27/key-facts-about-the-u-s-h-1b-visa-program/H1B visa News and Updates from The Economic Timeshttps://economictimes.indiatimes.com/topic/H1B-visa/newsH-1B visa - Wikipediahttps://en.wikipedia.org/wiki/H-1B_visaKey FindingsFrom the analysis, the government is cutting down the number of approvals for H1B on 2017.In the past decade, due to the nature of demand for high-skilled workers, visa holders have clustered in STEM fields and come mostly from countries in Asia such as China and India.Technical Jobs fill up the majority of Top 10 Jobs among foreign workers such as Computer Systems Analyst and Software Developers.The employers located in the metro areas thrive to find foreign workforce who can fill the technical position that they have in their organization.States like California, New York, Washington, New Jersey, Massachusetts, Illinois, and Texas are the prime location for foreign workers and provide many job opportunities. Top Companies such Infosys, Tata, IBM India that submit most H1B Visa Applications are companies based in India associated with software and IT services.Data Scientist position has experienced an exponential growth in terms of H1B visa applications and jobs are clustered in West region with the highest number.Visualization utilizing programsHTML, JavaScript, CSS, D3.js, Google API, Python, R, and Tableau

Project Introduction and Goals

This project is focused on analyzing a sales dataset using Google Sheets for data cleaning and Tableau for visualizations. The main objective is to uncover actionable insights such as top performing countries, best selling products, and monthly sales trends. I aim to present these findings through an interactive dashboard that can be used by business stakeholders for decision making.

Process Overview

1. Data Cleaning (Google Sheets) • Removed blank rows and filtered out missing values. • Standardized product and region names for consistency. • Split combined columns (e.g., date & time) for easier analysis. • Replaced missing or incorrect values with relevant estimates (e.g., average or “unknown”).

2. Exploratory Analysis • Calculated total sales by country. • Identified the best-selling products and frequent buyers. • Tracked monthly sales trends.

3. Visualization (Tableau)

   • Created a dynamic sales dashboard including: • Line chart showing sales over time • Pie chart of product categories • Bar chart of top 10 customers by revenue • Country-wise sales comparison

4. Conclusion

The analysis reveals key patterns in sales distribution, highlights top contributors to revenue, and suggests areas needing attention (e.g., low-performing countries). The dashboard enables real-time filtering and deeper insight for users.

https://public.tableau.com/views/Salesperformanceanalysis_17478415969510/Salesperformanceanalysis?:language=en-GB&:sid=&:display_count=n&:origin=viz_share_link

Cancer, the second-leading cause of mortality, kills 16% of people worldwide. Unhealthy lifestyles, smoking, alcohol abuse, obesity, and a lack of exercise have been linked to cancer incidence and mortality. However, it is hard. Cancer and lifestyle correlation analysis and cancer incidence and mortality prediction in the next several years are used to guide people’s healthy lives and target medical financial resources. Two key research areas of this paper are Data preprocessing and sample expansion design Using experimental analysis and comparison, this study chooses the best cubic spline interpolation technology on the original data from 32 entry points to 420 entry points and converts annual data into monthly data to solve the problem of insufficient correlation analysis and prediction. Factor analysis is possible because data sources indicate changing factors. TSA-LSTM Two-stage attention design a popular tool with advanced visualization functions, Tableau, simplifies this paper’s study. Tableau’s testing findings indicate it cannot analyze and predict this paper’s time series data. LSTM is utilized by the TSA-LSTM optimization model. By commencing with input feature attention, this model attention technique guarantees that the model encoder converges to a subset of input sequence features during the prediction of output sequence features. As a result, the model’s natural learning trend and prediction quality are enhanced. The second step, time performance attention, maintains We can choose network features and improve forecasts based on real-time performance. Validating the data source with factor correlation analysis and trend prediction using the TSA-LSTM model Most cancers have overlapping risk factors, and excessive drinking, lack of exercise, and obesity can cause breast, colorectal, and colon cancer. A poor lifestyle directly promotes lung, laryngeal, and oral cancers, according to visual tests. Cancer incidence is expected to climb 18–21% between 2020 and 2025, according to 2021. Long-term projection accuracy is 98.96 percent, and smoking and obesity may be the main cancer causes.

The Council’s Department of Employment Services (DOES) Dashboard is an interactive data visualization tool, built in Tableau, that allows users to analyze the agency’s operating budget and expenditure data. Negative expenditures under the Expenditure Summary tab most likely indicates a reversal under the District’s accrual-based accounting methodology. You can read more about government accounting best practices here. Visit the DC Council Office of the Budget Director website for further documentation.

Prelude

This dataset is a cleaned version of the Chicago Crime Dataset, which can be found here. All rights for the dataset go to the original owners. The purpose of this dataset is to display my skills in visualizations and creating dashboards. To be specific, I will attempt to create a dashboard that will allow users to see metrics for a specific crime within a given year using filters and metrics. Due to this, there will not be much of a focus on the analysis of the data, but there will be portions discussing the validity of the dataset, the steps I took to clean the data, and how I organized it. The cleaned datasets can be found below, the Query (which utilized BigQuery) can be found here and the Tableau dashboard can be found here.

About the Dataset

Important Facts

The dataset comes directly from the City of Chicago's website under the page "City Data Catalog." The data is gathered directly from the Chicago Police's CLEAR (Citizen Law Enforcement Analysis and Reporting) and is updated daily to present the information accurately. This means that a crime on a specific date may be changed to better display the case. The dataset represents crimes starting all the way from 2001 to seven days prior to today's date.

Reliability

Using the ROCCC method, we can see that: * The data has high reliability: The data covers the entirety of Chicago from a little over 2 decades. It covers all the wards within Chicago and even gives the street names. While we may not have an idea for how big the sample size is, I do believe that the dataset has high reliability since it geographically covers the entirety of Chicago. * The data has high originality: The dataset was gained directly from the Chicago Police Dept. using their database, so we can say this dataset is original. * The data is somewhat comprehensive: While we do have important information such as the types of crimes committed and their geographic location, I do not think this gives us proper insights as to why these crimes take place. We can pinpoint the location of the crime, but we are limited by the information we have. How hot was the day of the crime? Did the crime take place in a neighborhood with low-income? I believe that these key factors prevent us from getting proper insights as to why these crimes take place, so I would say that this dataset is subpar with how comprehensive it is. * The data is current: The dataset is updated frequently to display crimes that took place seven days prior to today's date and may even update past crimes as more information comes to light. Due to the frequent updates, I do believe the data is current. * The data is cited: As mentioned prior, the data is collected directly from the polices CLEAR system, so we can say that the data is cited.

Processing the Data

Cleaning the Dataset

The purpose of this step is to clean the dataset such that there are no outliers in the dashboard. To do this, we are going to do the following: * Check for any null values and determine whether we should remove them. * Update any values where there may be typos. * Check for outliers and determine if we should remove them.

The following steps will be explained in the code segments below. (I used BigQuery for this so the coding will follow BigQuery's syntax) ```

Examining the dataset

There are over 7.5 million rows of data

Putting a limit so it does not take a long time to run

SELECT * FROM portfolioproject-350601.ChicagoCrime.Crime LIMIT 1000;

Seeing which points are null

There are 85,000 null points so we can exclude them as it's not a significant amount since it is only ~1.3% of the dataset

Most of the null points are in the lat and long, which we will need later

Because we don't have the full address, we can't estimate the lat and long in SQL so we will have to delete the rows with Null Data

SELECT * FROM portfolioproject-350601.ChicagoCrime.Crime WHERE unique_key IS NULL OR case_number IS NULL OR date IS NULL OR primary_type IS NULL OR location_description IS NULL OR arrest IS NULL OR longitude IS NULL OR latitude IS NULL;

Deleting all null rows

DELETE FROM portfolioproject-350601.ChicagoCrime.Crime WHERE
unique_key IS NULL OR case_number IS NULL OR date IS NULL OR primary_type IS NULL OR location_description IS NULL OR arrest IS NULL OR longitude IS NULL OR latitude IS NULL;

Checking for any duplicates in the unique keys

None to be found

SELECT unique_key, COUNT(unique_key) FROM `portfolioproject-350601.ChicagoCrime....

****Dataset Overview – LinkedIn Survey of Data Professionals****

The dataset is derived from a LinkedIn-based survey targeting professionals in the data field, including Data Analysts, Data Scientists, Data Engineers, and others. It provides valuable insights into career trends, salary expectations, educational backgrounds, and tool preferences among respondents.

This dataset originates from Alex Freberg's Power BI tutorial project (credits and links provided in the video description). It serves as an excellent resource for beginners looking to build standalone visualization projects using Power BI or Tableau. The dataset allows users to showcase data storytelling, interactive dashboard design, and visualization skills effectively;

Skills which can be displayed;

•Data transformation using Power Query •Data cleaning using Power BI(unstandardized information,missing data,unnecessary and empty columns) •Usage of DAX formulas for Data Exploration

Key Columns in the Dataset:

Dataset contains a wide range of valuable information, some columns (such as "Email," "City," and "Referrer") are intentionally left blank or contain incomplete data, as they are either not essential for analysis or were anonymized to protect respondent privacy. These fields can typically be excluded during data cleaning and preprocessing stages without impacting the integrity of the insights drawn from the core survey questions.

Timestamp – When the response was recorded. Unique ID Email Date Taken (America/New_York) Time Taken (America/New_York) Browser OS City Country Referrer Time Spent Q1 - Which Title Best Fits your Current Role? Q2 - Did you switch careers into Data? Q2 - Did you switch careers into Data? Q3 - Current Yearly Salary (in USD) Q4 - What Industry do you work in? Q5 - Favorite Programming Language Q6 - How Happy are you in your Current Position with the following? (Salary) Q6 - How Happy are you in your Current Position with the following? (Coworkers) Q6 - How Happy are you in your Current Position with the following? (Management) Q6 - How Happy are you in your Current Position with the following? (Upward Mobility) Q6 - How Happy are you in your Current Position with the following? (Learning New Things) Q7 - How difficult was it for you to break into Data? Q8 - If you were to look for a new job today, what would be the most important thing to you? Q9 - Male/Female? Q10 - Current Age Q11 - Which Country do you live in? Q12 - Highest Level of Education Q13 - Ethnicity

Purpose of the Dataset:

To explore career dynamics and compensation trends in the data industry. To understand how skills, tools, education, and location correlate with salaries and satisfaction.

Credits: Power BI Portfolio Project by Alex The Analyst: https://www.youtube.com/watch?v=I0vQ_VLZTWg&t=6506s Alex's Github for Power BI tutorial: https://github.com/AlexTheAnalyst/PowerBI/blob/main/Power%20BI%20-%20Final%20Project.xlsx

This dataset contains retail sales records from a superstore, including detailed information on orders, products, categories, sales, discounts, profits, customers, and regions.

It is widely used for business intelligence, data visualization, and machine learning projects. With features such as order date, ship mode, customer segment, and geographic region, the dataset is excellent for:

Sales forecasting

Profitability analysis

Market basket analysis

Customer segmentation

Data visualization practice (Tableau, Power BI, Excel, Python, R)

Inspiration:

Great dataset for learning how to build dashboards.

Commonly used in case studies for predictive analytics and decision-making.

Source: Originally inspired by a sample dataset frequently used in Tableau training and BI case studies.

This dataset represents a Snowflake Schema model built from the popular Tableau Superstore dataset which exists primarily in a denormalized (flat) format.

This version is fully structured into fact and dimension tables, making it ready for data warehouse design, SQL analytics, and BI visualization projects.

The dataset was modeled to demonstrate dimensional modeling best practices, showing how the original flat Superstore data can be normalized into related dimensions and a central fact table.

Use this dataset to: - Practice SQL joins and schema design - Build ETL pipelines or dbt models - Design Power BI dashboards - Learn data warehouse normalization (3NF → Snowflake) concepts - Simulate enterprise data warehouse reporting environments

I’m open to suggestions or improvements from the community — feel free to share ideas on additional dimensions, measures, or transformations that could improve and make this dataset even more useful for learning and analysis.

Transformation was done using dbt, check out the models and the entire project.

Context

This is my personal project which I analyzed the main factor that leads me to select the TV show. This time, I used Python for web scrapping (or known as crawling) the data from IMDb.com and used Spreadsheet to clean the dataset. Finally, I used Tableau to visualize the data.

This time, I've utilized web-crawling to build up the database. For this project, I gathered the data from the top 100 TV shows listed by the user named 'carlosotsubo' from IMDB.com.

Content

1. tv_show: titles
2. season_years: it ranges from the beginning year to the ending year.
   • Note: some TV shows are still ongoing.
3. first_season_yr: the beginning year of the season
4. last_season_yr: the final or ending year of the season
5. running_time_min: the running time of TV show per episode
6. genre: in this dataset, it would be the main genre
7. subgenre1: subgenre #1
8. subgenre2: subgenre #2
9. imdb_rating: ratings by IMDb members
10. watched_yn: the determining factor on whether I've watched or not.

Acknowledgements

I sincerely thank the IMDb user named, 'carlosotsubo,' for providing the list of top 100 TV shows.

Inspiration

The following questions need to be answered:

1. How do I choose which TV show to watch?
2. Does running time also affect my decision to watch the show?
3. If not, would the genre be the main factor that affects my decision?

Data Visualization

After my own analysis, I've created the data visualization:

https://public.tableau.com/app/profile/jae.hwan.kim/viz/HowdoIchoosewhichTVshowtowatch/Dashboard1

If you guys give me feedback, I will be glad to hear! Thanks!

📌**Description**

This project is part of my final task in the IBM Data Visualization with Tableau course. The dataset is based on the official World Happiness Report, which provides annual insights into global well-being and the key factors that influence happiness across countries.

For this project, the dataset covering 2015–2023 has been slightly modified for educational purposes. These modifications were made to ensure consistency across years and to simplify the analysis for visualization tasks.

The project explores whether demographic, regional, and economic characteristics influence happiness across countries. The dataset includes key metrics such as: - Country & Region - Happiness Score - GDP per Capita - Social Support - Healthy Life Expectancy - Freedom to Make Life Choices - Generosity - Perceptions of Corruption

🔍**Project Tasks**

Happiness Status Overview (2015–2023) - Regional average happiness scores - Top 10 happiest countries - Bottom 10 least happy countries
Effect of GDP per Capita - GDP vs. Happiness Score trends (Top 10 happiest countries)
Effect of Other Factors (Top 10 Happiest Countries) - Social support, freedom, generosity, and life expectancy
Effect of Other Factors (Bottom 10 Least Happy Countries) - Same indicators analyzed for the least happy countries
Final Story & Insights - Dashboards combined into a story with narrative inferences

🔗**Interactive Dashboard**

👉 Explore my full Tableau story here: World Happiness Report 2015–2023 Dashboard

The Ecommerce transaction analysis is one of great way to learn data visualization with Power BI or Tableau. Your visualization must reveals customer sales, product sales, regional sales, monthly sales, time of the day sales to gain valuable insights and business planning. You may use Combo Charts, Cards, Bar Charts, Tables, or Line Charts; for the customer segmentation page, you could employ Column Charts, Bubble Charts, Point Maps, Tables, etc.

About Dataset:

Domain : Marketing Project: User Profiling and Segmentation Datasets: user_profile_for_ads Dataset Type: Excel Data Dataset Size: 16k+ record

KPI's: 1. Distribution of Key Demographic Variables like: a. Count of Age b. Count of Gender c. Count of Education Level d. Count of Income Level e. Count of Device Usage

1. Understanding Online Behavior like: a. Count of Time Spent Online (hrs/Weekday) b. Count of Time Spent Online (hrs/Weekend)

2. Ad Interaction Metrics: a. Count of likes and Reactions b. Count of click through rates (CTR) c. Count of Conversion Rate d. Count of Ad Interaction Time (secs) e. Count of Ad Interaction Time by Top Interests

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

This data contains bar chart, horizontal bars, circle, treemap, area chart, square, line chart, dashboard, slicers, navigation button.

Context

With growing demands and cut-throat competitions in the market, a Superstore Giant is seeking your knowledge in understanding what works best for them. They would like to understand which products, regions, categories and customer segments they should target or avoid.

Retail dataset of a global superstore for 4 years.

You can even take this a step further and try and build a Regression model to predict Sales or Profit.

Go crazy with the dataset, but also make sure to provide some business insights to improve.

Metadata

Order ID => Unique Order ID for each Customer.

Order Date => Order Date of the product.

Ship Date => Shipping Date of the Product.

Ship Mode=> Shipping Mode specified by the Customer.

Customer Name => Name of the Customer.

Segment => The segment where the Customer belongs.

State => State of residence of the Customer.

Country => Country of residence of the Customer.

Market => The market place of the product.

Region => Region where the Customer belong.

Product ID => Unique ID of the Product.

Category => Category of the product ordered.

Sub-Category => Sub-Category of the product ordered.

Product Name => Name of the Product

Unit Price => The price for one unit.

Quantity => Quantity of the Product.

Discount => Discount provided.

Shipping Cost => The cost for shipping

Order Priority => Items shipped via priority are shipped by air which results in faster delivery times.

Sales => Sales of the Product.

Expenses => The expense is the cost of operations that a company incurs to generate revenue.

Revenue => The Revenue refers to the total earnings.

Year => Year of the Sales.

Acknowledgements

I do not own this data. I merely found it from the Tableau website and add some row. All credits to the original authors/creators. For educational purposes only.