The Data Preparation Tools market is experiencing robust growth, projected to reach a significant market size by 2033. Driven by the exponential increase in data volume and variety across industries, coupled with the rising need for accurate, consistent data for effective business intelligence and machine learning initiatives, this sector is poised for continued expansion. The 18.5% Compound Annual Growth Rate (CAGR) signifies strong market momentum, fueled by increasing adoption across diverse sectors like IT and Telecom, Retail & E-commerce, BFSI (Banking, Financial Services, and Insurance), and Manufacturing. The preference for self-service data preparation tools empowers business users to directly access and prepare data, minimizing reliance on IT departments and accelerating analysis. Furthermore, the integration of data preparation tools with advanced analytics platforms and cloud-based solutions is streamlining workflows and improving overall efficiency. This trend is further augmented by the growing demand for robust data governance and compliance measures, necessitating sophisticated data preparation capabilities. While the market shows significant potential, challenges remain. The complexity of integrating data from multiple sources and maintaining data consistency across disparate systems present hurdles for many organizations. The need for skilled data professionals to effectively utilize these tools also contributes to market constraints. However, ongoing advancements in automation and user-friendly interfaces are mitigating these challenges. The competitive landscape is marked by established players like Microsoft, Tableau, and IBM, alongside innovative startups offering specialized solutions. This competitive dynamic fosters innovation and drives down costs, benefiting end-users. The market segmentation by application and tool type highlights the varied needs and preferences across industries, and understanding these distinctions is crucial for effective market penetration and strategic planning. Geographical expansion, particularly within rapidly developing economies in Asia-Pacific, will play a significant role in shaping the future trajectory of this thriving market.

This project focuses on data mapping, integration, and analysis to support the development and enhancement of six UNCDF operational applications: OrgTraveler, Comms Central, Internal Support Hub, Partnership 360, SmartHR, and TimeTrack. These apps streamline workflows for travel claims, internal support, partnership management, and time tracking within UNCDF.Key Features and Tools:Data Mapping for Salesforce CRM Migration: Structured and mapped data flows to ensure compatibility and seamless migration to Salesforce CRM.Python for Data Cleaning and Transformation: Utilized pandas, numpy, and APIs to clean, preprocess, and transform raw datasets into standardized formats.Power BI Dashboards: Designed interactive dashboards to visualize workflows and monitor performance metrics for decision-making.Collaboration Across Platforms: Integrated Google Collab for code collaboration and Microsoft Excel for data validation and analysis.

This project focuses on analyzing the S&P 500 companies using data analysis tools like Python (Pandas), SQL, and Power BI. The goal is to extract insights related to sectors, industries, locations, and more, and visualize them using dashboards.

Included Files:

sp500_cleaned.csv – Cleaned dataset used for analysis

sp500_analysis.ipynb – Jupyter Notebook (Python + SQL code)

dashboard_screenshot.png – Screenshot of Power BI dashboard

README.md – Summary of the project and key takeaways

This project demonstrates practical data cleaning, querying, and visualization skills.

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 stacked column chart, Donut chart, Stacked area chart, pie chart, matrix, slicer, treemap, clustered column chart, Map, Dashboard, Page Navigator, card, text box.

[Dataset Name] - About This Dataset

Overview

This dataset is used in a data cleaning project based on the raw data from Alex the Analyst's Power BI tutorial series. The original dataset can be found here.

Context

The dataset is employed in a mini project that involves cleaning and preparing data for analysis. It is part of a series of exercises aimed at enhancing skills in data cleaning using Pandas.

Content

The dataset contains information related to [provide a brief description of the data, e.g., sales, customer information, etc.]. The columns cover various aspects such as [list key columns and their meanings].

Acknowledgements

The original dataset is sourced from Alex the Analyst's Power BI tutorial series. Special thanks to [provide credit or acknowledgment] for making the dataset available.

Citation

If you use this dataset in your work, please cite it as follows:

How to Use

1. Download the dataset from this link.
2. Explore the Jupyter Notebook in the associated repository for insights into the data cleaning process.

Feel free to reach out for any additional information or clarification. Happy analyzing!

The SHMI is the ratio between the actual number of patients who die following hospitalisation at the trust and the number that would be expected to die on the basis of average England figures, given the characteristics of the patients treated there. It includes deaths which occurred in hospital and deaths which occurred outside of hospital within 30 days (inclusive) of discharge. Deaths related to COVID-19 are excluded from the SHMI. The SHMI gives an indication for each non-specialist acute NHS trust in England whether the observed number of deaths within 30 days of discharge from hospital was 'higher than expected' (SHMI banding=1), 'as expected' (SHMI banding=2) or 'lower than expected' (SHMI banding=3) when compared to the national baseline. Trusts may be located at multiple sites and may be responsible for 1 or more hospitals. A breakdown of the data by site of treatment is also provided. The SHMI is composed of 142 different diagnosis groups and these are aggregated to calculate the overall SHMI value for each trust. The number of finished provider spells, observed deaths and expected deaths at diagnosis group level for each trust is available in the SHMI diagnosis group breakdown files. For a subset of diagnosis groups, an indication of whether the observed number of deaths within 30 days of discharge from hospital was 'higher than expected', 'as expected' or 'lower than expected' when compared to the national baseline is also provided. Details of the 142 diagnosis groups can be found in Appendix A of the SHMI specification. Notes: 1. As of the July 2020 publication, COVID-19 activity has been excluded from the SHMI. The SHMI is not designed for this type of pandemic activity and the statistical modelling used to calculate the SHMI may not be as robust if such activity were included. Activity that is being coded as COVID-19, and therefore excluded, is monitored in a new contextual indicator 'Percentage of provider spells with COVID-19 coding' which is part of this publication. 2. Please note that there has been a fall in the number of spells for most trusts between this publication and the previous SHMI publication, ranging from 0 per cent to 4 per cent. This is due to COVID-19 impacting on activity from March 2020 onwards and appears to be an accurate reflection of hospital activity rather than a case of missing data. 3. Day cases and regular day attenders are excluded from the SHMI. However, some day cases for University College London Hospitals NHS Foundation Trust (trust code RRV) have been incorrectly classified as ordinary admissions meaning that they have been included in the SHMI. Maidstone and Tunbridge Wells NHS Trust (trust code RWF) has submitted a number of records with a patient classification of ‘day case’ or ‘regular day attender’ and an intended management value of ‘patient to stay in hospital for at least one night’. This mismatch has resulted in the patient classification being updated to ‘ordinary admission’ by the HES data cleaning rules. This may have resulted in the number of ordinary admissions being overstated. The trust has been contacted to clarify what the correct patient classification is for these records. Values for these trusts should therefore be interpreted with caution. 4. On 1 October 2020 Poole Hospital NHS Foundation Trust (trust code RD3) merged with The Royal Bournemouth and Christchurch Hospitals NHS Foundation Trust (trust code RDZ). The new trust is called University Hospitals Dorset NHS Foundation Trust (trust code R0D). This new organisation structure is reflected from this publication onwards. 5. Airedale NHS Foundation Trust (trust code RCF) has submitted an increased number of delivery episode records. HES data cleaning rules have amended some of the records to birth episodes however, most records have not been changed. It is therefore considered likely that the increased number of delivery episodes (and corresponding reduction in ordinary episodes) is incorrect. Values for this trust should therefore be interpreted with caution. 6. Further information on data quality can be found in the SHMI background quality report, which can be downloaded from the 'Resources' section of the publication page. 7. This tool is in Microsoft Power BI which does not fully support all accessibility needs. If you need further assistance, please contact us for help.

Data was imported from the BAK file found here into SQL Server, and then individual tables were exported as CSV. Jupyter Notebook containing the code used to clean the data can be found here

Version 6 has a some more cleaning and structuring that was noticed after importing in Power BI. Changes were made by adding code in python notebook to export new cleaned dataset, such as adding MonthNumber for sorting by month number, similar for WeekDayNumber.

Cleaning was done in python while also using SQL Server to quickly find things. Headers were added separately, ensuring no data loss.Data was cleaned for NaN, garbage values and other columns.

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 Power Query, Power Pivot, Merge data, Clustered Bar Chart, Clustered Column Chart, Line Chart, 3D Pie chart, Dashboard, slicers, timeline, formatting techniques.

Titanic Analysis May they all rest in peace... I extracted some statistics based on the dataset available about the passengers of the sunken Titanic ship. The workflow included the following stages:

Data collection. Data understanding. Data cleaning. Analysis and posing questions. Drawing answers to the questions and extracting results. Creating a visualization of those results on a dashboard. Power Query. Power Pivot.

Dataset contains 1 excel workbook (.xlsx) with 2 sheets.

• Sheet 1 - App Store Games contains the mobile games launched on App Store from 2008 - 2019.
• Sheet 2 - Data Dictionary is just the explanation of columns in data.

This data can be used to practice EDA and some data cleaning tasks. Can be used for Data visualization using python Matplotlib and Seaborn libraries.

I used this dataset for a Power BI project also and created a Dashboard on it. Used python inside power query to clean and convert some encoded and Unicode characters from App URL, Name, and Description columns.

Total Columns: 16

• App URL
• App ID
• Name
• Subtitle
• Icon URL
• Average User Rating
• User Rating Count
• Price per App (USD)
• Description
• Developer
• Age Rating
• Languages
• Size in Bytes
• Primary Genre
• Genres
• Release Date