This supply chain analysis provides a comprehensive view of the company's order and distribution processes, allowing for in-depth analysis and optimization of various aspects of the supply chain, from procurement and inventory management to sales and customer satisfaction. It empowers the company to make data-driven decisions to improve efficiency, reduce costs, and enhance customer experiences. The provided supply chain analysis dataset contains various columns that capture important information related to the company's order and distribution processes:

• OrderNumber • Sales Channel • WarehouseCode • ProcuredDate • CurrencyCode • OrderDate • ShipDate • DeliveryDate • SalesTeamID • CustomerID • StoreID • ProductID • Order Quantity • Discount Applied • Unit Cost • Unit Price

Looking for a free Walmart product dataset? The Walmart Products Free Dataset delivers a ready-to-use ecommerce product data CSV containing ~2,100 verified product records from Walmart.com. It includes vital details like product titles, prices, categories, brand info, availability, and descriptions — perfect for data analysis, price comparison, market research, or building machine-learning models.

Key Features

Complete Product Metadata: Each entry includes URL, title, brand, SKU, price, currency, description, availability, delivery method, average rating, total ratings, image links, unique ID, and timestamp.

CSV Format, Ready to Use: Download instantly - no need for scraping, cleaning or formatting.

Good for E-commerce Research & ML: Ideal for product cataloging, price tracking, demand forecasting, recommendation systems, or data-driven projects.

Free & Easy Access: Priced at USD $0.0, making it a great starting point for developers, data analysts or students.

Who Benefits?

• Data analysts & researchers exploring e-commerce trends or product catalog data.
• Developers & data scientists building price-comparison tools, recommendation engines or ML models.
• E-commerce strategists/marketers need product metadata for competitive analysis or market research.
• Students/hobbyists needing a free dataset for learning or demo projects.

Why Use This Dataset Instead of Manual Scraping?

• Time-saving: No need to write scrapers or deal with rate limits.
• Clean, structured data: All records are verified and already formatted in CSV, saving hours of cleaning.
• Risk-free: Avoid Terms-of-Service issues or IP blocks that come with manual scraping.
  Instant access: Free and immediately downloadable.

Dataset

This dataset was created by Nafe Muhtasim

Released under CC0: Public Domain

Contents

The complete dataset used in the analysis comprises 36 samples, each described by 11 numeric features and 1 target. The attributes considered were caspase 3/7 activity, Mitotracker red CMXRos area and intensity (3 h and 24 h incubations with both compounds), Mitosox oxidation (3 h incubation with the referred compounds) and oxidation rate, DCFDA fluorescence (3 h and 24 h incubations with either compound) and oxidation rate, and DQ BSA hydrolysis. The target of each instance corresponds to one of the 9 possible classes (4 samples per class): Control, 6.25, 12.5, 25 and 50 µM for 6-OHDA and 0.03, 0.06, 0.125 and 0.25 µM for rotenone. The dataset is balanced, it does not contain any missing values and data was standardized across features. The small number of samples prevented a full and strong statistical analysis of the results. Nevertheless, it allowed the identification of relevant hidden patterns and trends.

Exploratory data analysis, information gain, hierarchical clustering, and supervised predictive modeling were performed using Orange Data Mining version 3.25.1 [41]. Hierarchical clustering was performed using the Euclidean distance metric and weighted linkage. Cluster maps were plotted to relate the features with higher mutual information (in rows) with instances (in columns), with the color of each cell representing the normalized level of a particular feature in a specific instance. The information is grouped both in rows and in columns by a two-way hierarchical clustering method using the Euclidean distances and average linkage. Stratified cross-validation was used to train the supervised decision tree. A set of preliminary empirical experiments were performed to choose the best parameters for each algorithm, and we verified that, within moderate variations, there were no significant changes in the outcome. The following settings were adopted for the decision tree algorithm: minimum number of samples in leaves: 2; minimum number of samples required to split an internal node: 5; stop splitting when majority reaches: 95%; criterion: gain ratio. The performance of the supervised model was assessed using accuracy, precision, recall, F-measure and area under the ROC curve (AUC) metrics.

The data analytic market size is projected to grow from USD 69.40 billion in the current year to USD 877.12 billion by 2035, representing a CAGR of 25.93%, during the forecast period till 2035.

Copies of Anaconda 3 Jupyter Notebooks and Python script for holistic and clustered analysis of "The Impact of COVID-19 on Technical Services Units" survey results. Data was analyzed holistically using cleaned and standardized survey results and by library type clusters. To streamline data analysis in certain locations, an off-shoot CSV file was created so data could be standardized without compromising the integrity of the parent clean file. Three Jupyter Notebooks/Python scripts are available in relation to this project: COVID_Impact_TechnicalServices_HolisticAnalysis (a holistic analysis of all survey data) and COVID_Impact_TechnicalServices_LibraryTypeAnalysis (a clustered analysis of impact by library type, clustered files available as part of the Dataverse for this project).

This dataset contains detailed information about the COVID-19 pandemic. The inspiration behind this dataset is to analyze trends, identify patterns, and understand the global impact of COVID-19 through SQL queries. It is designed for anyone interested in data exploration and real-world analytics.

R code for running GLMM and BRT analysis

Explore the booming AI Data Analysis Tool market, driven by big data and advanced AI. Discover market size, CAGR, key drivers, trends, restraints, and leading companies for 2025-2033.

The COVID-19 pandemic necessitated the move to online teaching and assessment. This has created challenges in teaching laboratory skills and producing assessments that are robust and fair. Our solution was to use bespoke laboratory videos to provide laboratory training and to generate unique data sets for each student in coursework and exams. For assessments, R was used to produce student data packs comprising data and images, and associated staff answer files with plotted data and worked answers. In the new open-book online environment, this approach enabled us to create assessments that were the students’ own work with no evidence of student collusion. We observed no difference in student performance for the coursework or exam: The mean and median marks for the course remained the same as in previous years.

This is a case study for the company to improve sales

Business Goal
Date: 2023/09/15
Dataset: Sales quantity of a certain brand from January to December 2022 and from January to September 2023.

Please describe what you observe (no specific presentation format required). Among your observations, identify at least three valuable insights and explain why you consider them valuable.
If more resources were available to you (including time, information, etc.), what would you need, and what more could you achieve?

Metadata of the file Data Period: January 2022 - September 2023 Data Fields: - item - store_id - sales of each month

Metadata of the file Data Period: January 2022 - September 2023 Data Fields: - item - store_id - sales of each month

Sample question & answer 1. Product insights: identify the product sales analysis, such as BCG matrix 2. Store insights: identify the sales performance of the sales 3. Supply chain insights: identify the demand 4. Time series forecasting: identify tread, seasonality

Feel free to leave comments on the discussion. I'd appreciate your upvote if you find my dataset useful! 😀

The Data Analysis Application Solution market is booming, projected to reach $45 billion by 2033 with a 15% CAGR. Explore key drivers, trends, and challenges shaping this rapidly evolving sector, featuring leading companies like SAP, Microsoft, and BigID. Discover market segmentation, regional insights, and future growth forecasts.

Problem Statement-

Bike-sharing systems are meant to rent bicycles and return to different places for bike-sharing purposes in Washington DC.

You are provided with rental data spanning 2 years. It would help if you predicted the total count of bikes rented during each hour covered by the test set, using only information available prior to the rental period.

This is the bike rental dataset, to practice pandas profiling. This dataset contains numerical values.

Tasks to perform : 1. Perform Exploratory Data Analysis 2. Use Pandas Profiling

Compare the pandas profiling report with Exploratory Data Analysis

This graph shows how the impact factor of ^ is computed. The left axis depicts the number of papers published in years X-1 and X-2, and the right axis displays their citations in year X.

This dataset is designed for skill gap analysis, focusing on evaluating the skill gap between students’ current skills and industry requirements. It provides insights into technical skills, soft skills, career interests, and challenges, helping in skill gap analysis to identify areas for improvement.

By leveraging this dataset, educators, recruiters, and researchers can conduct skill gap analysis to assess students’ job readiness and tailor training programs accordingly. It serves as a valuable resource for identifying skill deficiencies and skill gaps improving career guidance, and enhancing curriculum design through targeted skill gap analysis.

Following is the column descriptors: Name - Student's full name. email_id - Student's email address. Year - The academic year the student is currently in (e.g., 1st Year, 2nd Year, etc.). Current Course - The course the student is currently pursuing (e.g., B.Tech CSE, MBA, etc.). Technical Skills - List of technical skills possessed by the student (e.g., Python, Data Analysis, Cloud Computing). Programming Languages - Programming languages known by the student (e.g., Python, Java, C++). Rating - Self-assessed rating of technical skills on a scale of 1 to 5. Soft Skills - List of soft skills (e.g., Communication, Leadership, Teamwork). Rating - Self-assessed rating of soft skills on a scale of 1 to 5. Projects - Indicates whether the student has worked on any projects (Yes/No). Career Interest - The student's preferred career path (e.g., Data Scientist, Software Engineer). Challenges - Challenges faced while applying for jobs/internships (e.g., Lack of experience, Resume building issues).

The size of the Industrial Data Analysis Tools market was valued at USD XXX million in 2024 and is projected to reach USD XXX million by 2033, with an expected CAGR of XX % during the forecast period.

Case Study: How Does a Bike-Share Navigate Speedy Success?¶

Introduction

Welcome to the Cyclistic bike-share analysis case study! In this case study, you will perform many real-world tasks of a junior data analyst. You will work for a fictional company, Cyclistic, and meet different characters and team members. In order to answer the key business questions, you will follow the steps of the data analysis process: ask, prepare, process, analyze, share, and act. Along the way, the Case Study Roadmap tables — including guiding questions and key tasks — will help you stay on the right path. By the end of this lesson, you will have a portfolio-ready case study. Download the packet and reference the details of this case study anytime. Then, when you begin your job hunt, your case study will be a tangible way to demonstrate your knowledge and skills to potential employers.

Scenario

You are a junior data analyst working in the marketing analyst team at Cyclistic, a bike-share company in Chicago. The director of marketing believes the company’s future success depends on maximizing the number of annual memberships. Therefore, your team wants to understand how casual riders and annual members use Cyclistic bikes differently. From these insights, your team will design a new marketing strategy to convert casual riders into annual members. But first, Cyclistic executives must approve your recommendations, so they must be backed up with compelling data insights and professional data visualizations. Characters and teams ● Cyclistic: A bike-share program that features more than 5,800 bicycles and 600 docking stations. Cyclistic sets itself apart by also offering reclining bikes, hand tricycles, and cargo bikes, making bike-share more inclusive to people with disabilities and riders who can’t use a standard two-wheeled bike. The majority of riders opt for traditional bikes; about 8% of riders use the assistive options. Cyclistic users are more likely to ride for leisure, but about 30% use them to commute to work each day. ● Lily Moreno: The director of marketing and your manager. Moreno is responsible for the development of campaigns and initiatives to promote the bike-share program. These may include email, social media, and other channels. ● Cyclistic marketing analytics team: A team of data analysts who are responsible for collecting, analyzing, and reporting data that helps guide Cyclistic marketing strategy. You joined this team six months ago and have been busy learning about Cyclistic’s mission and business goals — as well as how you, as a junior data analyst, can help Cyclistic achieve them. ● Cyclistic executive team: The notoriously detail-oriented executive team will decide whether to approve the recommended marketing program.

About the company

In 2016, Cyclistic launched a successful bike-share offering. Since then, the program has grown to a fleet of 5,824 bicycles that are geotracked and locked into a network of 692 stations across Chicago. The bikes can be unlocked from one station and returned to any other station in the system anytime. Until now, Cyclistic’s marketing strategy relied on building general awareness and appealing to broad consumer segments. One approach that helped make these things possible was the flexibility of its pricing plans: single-ride passes, full-day passes, and annual memberships. Customers who purchase single-ride or full-day passes are referred to as casual riders. Customers who purchase annual memberships are Cyclistic members. Cyclistic’s finance analysts have concluded that annual members are much more profitable than casual riders. Although the pricing flexibility helps Cyclistic attract more customers, Moreno believes that maximizing the number of annual members will be key to future growth. Rather than creating a marketing campaign that targets all-new customers, Moreno believes there is a very good chance to convert casual riders into members. She notes that casual riders are already aware of the Cyclistic program and have chosen Cyclistic for their mobility needs. Moreno has set a clear goal: Design marketing strategies aimed at converting casual riders into annual members. In order to do that, however, the marketing analyst team needs to better understand how annual members and casual riders differ, why casual riders would buy a membership, and how digital media could affect their marketing tactics. Moreno and her team are interested in analyzing the Cyclistic historical bike trip data to identify trends

Three questions will guide the future marketing program:

How do annual members and casual riders use Cyclistic bikes differently? Why would casual riders buy Cyclistic annual memberships? How can Cyclistic use digital media to influence casual riders to become members? Moreno has assigned you the first question to answer: How do annual members and casual rid...

Data analysis can be accurate and reliable only if the underlying assumptions of the used statistical method are validated. Any violations of these assumptions can change the outcomes and conclusions of the analysis. In this study, we developed Smart Data Analysis V2 (SDA-V2), an interactive and user-friendly web application, to assist users with limited statistical knowledge in data analysis, and it can be freely accessed at https://jularatchumnaul.shinyapps.io/SDA-V2/. SDA-V2 automatically explores and visualizes data, examines the underlying assumptions associated with the parametric test, and selects an appropriate statistical method for the given data. Furthermore, SDA-V2 can assess the quality of research instruments and determine the minimum sample size required for a meaningful study. However, while SDA-V2 is a valuable tool for simplifying statistical analysis, it does not replace the need for a fundamental understanding of statistical principles. Researchers are encouraged to combine their expertise with the software’s capabilities to achieve the most accurate and credible results.

Brazil data analytics market is expected to grow at a substantial rate during the forecast period 2026.