the Department of Energy’s Enterprise Project Management Organization (EPMO), providing leadership and assistance in developing and implementing DOE-wide policies, procedures, programs, and management systems pertaining to project management, and independently monitors, assesses, and reports on project execution performance. The office validates project performance baselines–scope, cost and schedule–of the Department’s largest construction and environmental clean-up projects prior to budget request to Congress—an active project portfolio totaling over $30 billion. The office also serves as Executive Secretariat for the Department’s Energy Systems Acquisition Advisory Board (ESAAB) and the Project Management Risk Committee (PMRC). In these capacities, the Director is accountable to the Deputy Secretary.

Dataset

This dataset was created by Glitch_in_Vector

Contents

Chunk_0 for me, Choose others as you want.

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...

This data set contains DOT construction project information. The data is refreshed nightly from multiple data sources, therefore the data becomes stale rather quickly.

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 Variables

Agile Effectiveness (measured on a Likert scale from 2 to 5): This variable captures how respondents perceive the effectiveness of Agile methodology in enhancing project management processes.

Risk Mitigation (Likert scale 2 to 5): This variable reflects respondents' views on how well Agile methodology supports the mitigation of risks throughout the project lifecycle.

Management Satisfaction (Likert scale 2 to 5): This variable measures how satisfied the management is with the outcomes of projects where Agile methodologies were implemented.

Supply Chain Improvement (Likert scale 2 to 5): This variable captures the perceived improvements in supply chain processes that result from using Agile methods.

Time Efficiency (Likert scale 2 to 5): This measures the impact of Agile methodology on improving the efficiency of time management within projects.

Cost Savings (percentage from 10% to 48%): This variable quantifies the percentage of cost savings achieved as a result of implementing Agile methods.

Project Success (binary: 0 = Failure, 1 = Success): This is the dependent variable and represents whether or not the project was considered successful.

City of Pittsburgh Capital Projects Budgets NOTE: The data in this dataset has not updated since 2021 because of a broken data feed. We're working to fix it.

A complete copy of the Materials Project database as of 10/18/2018. Mp_all files contain structure data for each material while mp_nostruct does not.Available as Monty Encoder encoded JSON and as CSV. Recommended access method for these particular files is with the matminer Python package using the datasets module. Access to the current Materials Project is recommended through their API (good), pymatgen (better), or matminer (best).Note on citations: If you found this dataset useful and would like to cite it in your work, please be sure to cite its original sources below rather than or in addition to this page.Dataset discussed in:A. Jain*, S.P. Ong*, G. Hautier, W. Chen, W.D. Richards, S. Dacek, S. Cholia, D. Gunter, D. Skinner, G. Ceder, K.A. Persson (*=equal contributions) The Materials Project: A materials genome approach to accelerating materials innovation APL Materials, 2013, 1(1), 011002.Dataset sourced from:https://materialsproject.org/Citations for specific material properties available here:https://materialsproject.org/citing

Lists all the firms that bid on projects from 2004 to present

Cyclistic Trip Data

This dataset contains rows of cyclistic trip data, collected from Index of bucket "divvy-tripdata". The dataset includes 10 columns, each representing a different attribute or feature of the data.

The data has been preprocessed to remove any missing values, duplicates, or other inconsistencies. It is ready for use in a wide range of data analysis and machine learning tasks.

The dataset includes the following columns:

• rideable_type : Type of bicycle used for rides
• member_casual : Type of members performing the drive
• ride_length : Bicycle ride length in minutes
• date : Realized date of cycling ride
• day_of_week : Days of the week when the bike ride takes place
• month : Months of riding
• day : Days of the month in which the ride was carried out
• year : Year in which the ride took place
• hour : Hours when riding is carried out
• season : Seasons in which the ride takes place

This dataset contains data from the capstone project section of the google data analytics course on the coursera platform. This dataset has been cleaned and processed, ready for the user to analyze. We hope that it will help everyone who takes this course and tries to make the preparation process related to the data in the last stage.

The Coastal Science Projects Dashboard showcases the external and internal projects of the Coastal Science program. The dashboard contains a full projects list, projects by major estuary, and links to project reports. The dashboard will be regularly updated with new information. Contact Email: coastal-data@twdb.texas.gov

This dataset contains capital commitment plan data by managing agency, project identification number and project schedules. The dataset was updated three times a year during the Preliminary, Executive and Adopted Capital Commitment Plans. Starting in January 2024, OMB will no longer update this dataset. It is being replaced by the Capital Projects Dashboard administered by the Mayor's Office of Operations.

Data Annotation Training Project

## Overview

Data Annotation Training Project is a dataset for object detection tasks - it contains Objects annotations for 1,230 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Get data on announced projects funded through the Rural Economic Development (RED) program.

Ontario's RED program funds projects that stimulate economic growth in rural and Indigenous communities.

The data includes:

• name of the organization that requested funding
• county or district where the organization is located
• project name
• date that funding was approved
• amount of funding provided

From 2013 to 2016, the RED program funded projects led by businesses or communities.

Starting in 2017, the RED program only focuses on projects led by:

• not-for-profit organizations
• municipalities
• local service boards
• Indigenous communities and organizations

Learn more about the Rural Economic Development program.

A variety of cumulative (over reporting periods) data points for Rescue Plan projects: individual recipient count, business recipient count, non profit recipient count, and expenditure.-- Additional Information: Category: ARPA Update Frequency: As Necessary-- Metadata Link: https://www.portlandmaps.com/metadata/index.cfm?&action=DisplayLayer&LayerID=61045

Analysis of the projects proposed by the seven finalists to USDOT's Smart City Challenge, including challenge addressed, proposed project category, and project description. The time reported for the speed profiles are between 2:00PM to 8:00PM in increments of 10 minutes.

Smart local energy systems require the communication, automation and engagement of individual systems, each producing substantial quantities of data. Data analytics and digital communication technologies need to be used to explore and investigate hypothetical and real-time operating scenarios for future smart local energy systems. To conduct experimental data analysis of the way energy is generated, stored, shared and consumed, the Newcastle team created the SLES database to store data from our partners and results of our case studies. A novel, generic and scalable smart energy platform for optimised design and the real-time efficient control of power generation and delivery for local energy systems through IoT services for data analytics was proposed. The structure of each table of the SLES database is shown in the attached sql file (sles_db.sql). The design and functionality of the smart energy platform is presented in the paper submitted to IREC2021 - 12th International Renewable Engineering Conference (https://irec2021.meu.edu.jo/). The title of the paper is "An integration platform for optimised design and real-time control of smart local energy systems".

This dataset is constructed from project activity experience.

Columns: not done - Projects that didn't worked out until accomplishment (0 = done // 1 = not done) time required - Time in hours estimated for the accomplishment cost - Cost per hour

This list includes all pipeline projects that have submitted an Intake. Some may be held at Intake due to early concept status or because the developer has reached their maximum project limit in ORCA.

[AI+X] Project Ball Data

## Overview

[AI+X] Project  Ball Data is a dataset for object detection tasks - it contains Passes Dribbles annotations for 500 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).