Pen-and-paper homework and project-based learning are both commonly used instructional methods in introductory statistics courses. However, there have been few studies comparing these two methods exclusively. In this case study, each was used in two different sections of the same introductory statistics course at a regional state university. Students’ statistical literacy was measured by exam scores across the course, including the final. The comparison of the two instructional methods includes using descriptive statistics and two-sample t-tests, as well authors’ reflections on the instructional methods. Results indicated that there is no statistically discernible difference between the two instructional methods in the introductory statistics course.

Dataset

This dataset was created by Dhinesh Gupthaa K

Contents

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.

In 2024, the total number of open source projects taken up was about *** million. Of these, the majority was through JavaScript with about *** million projects, far more than those in any other language.

Financial overview and grant giving statistics of Project America

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.

Dataset

This dataset was created by Glitch_in_Vector

Contents

Chunk_0 for me, Choose others as you want.

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

Crowdfunding has become one of the main sources of initial capital for small businesses and start-up companies that are looking to launch their first products. Websites like Kickstarter and Indiegogo provide a platform for millions of creators to present their innovative ideas to the public. This is a win-win situation where creators could accumulate initial fund while the public get access to cutting-edge prototypical products that are not available in the market yet.

At any given point, Indiegogo has around 10,000 live campaigns while Kickstarter has 6,000. It has become increasingly difficult for projects to stand out of the crowd. Of course, advertisements via various channels are by far the most important factor to a successful campaign. However, for creators with a smaller budget, this leaves them wonder,

"How do we increase the probability of success of our campaign starting from the very moment we create our project on these websites?"

Data Sources

All of my raw data are scraped from Kickstarter.com.

1. First 4000 live projects that are currently campaigning on Kickstarter (live.csv)

   • Last updated: 2016-10-29 5pm PDT
   • amt.pledged: amount pledged (float)
   • blurb: project blurb (string)
   • by: project creator (string)
   • country: abbreviated country code (string of length 2)
   • currency: currency type of amt.pledged (string of length 3)
   • end.time: campaign end time (string "YYYY-MM-DDThh:mm:ss-TZD")
   • location: mostly city (string)
   • pecentage.funded: unit % (int)
   • state: mostly US states (string of length 2) and others (string)
   • title: project title (string)
   • type: type of location (string: County/Island/LocalAdmin/Suburb/Town/Zip)
   • url: project url after domain (string)

2. Top 4000 most backed projects ever on Kickstarter (most_backed.csv)

   • Last updated: 2016-10-30 10pm PDT
   • amt.pledged
   • blurb
   • by
   • category: project category (string)
   • currency
   • goal: original pledge goal (float)
   • location
   • num.backers: total number of backers (int)
   • num.backers.tier: number of backers corresponds to the pledge amount in pledge.tier (int[len(pledge.tier)])
   • pledge.tier: pledge tiers in USD (float[])
   • title
   • url

See more at http://datapolymath.paperplane.io/

Annual Statistics of Approved Projects under General Support Programme

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 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

Files included are original data inputs on stream fishes (fish_data_OEPA_2012.csv), water chemistry (OEPA_WATER_2012.csv), geographic data (NHD_Plus_StreamCat); modeling files for generating predictions from the original data, including the R code (MVP_R_Final.txt) and Stan code (MV_Probit_Stan_Final.txt); and the model output file containing predictions for all NHDPlus catchments in the East Fork Little Miami River watershed (MVP_EFLMR_cooc_Final). This dataset is associated with the following publication: Martin, R., E. Waits, and C. Nietch. Empirically-based modeling and mapping to consider the co-occurrence of ecological receptors and stressors. SCIENCE OF THE TOTAL ENVIRONMENT. Elsevier BV, AMSTERDAM, NETHERLANDS, 613(614): 1228-1239, (2018).

Hydrographic and Impairment Statistics (HIS) is a National Park Service (NPS) Water Resources Division (WRD) project established to track certain goals created in response to the Government Performance and Results Act of 1993 (GPRA). One water resources management goal established by the Department of the Interior under GRPA requires NPS to track the percent of its managed surface waters that are meeting Clean Water Act (CWA) water quality standards. This goal requires an accurate inventory that spatially quantifies the surface water hydrography that each bureau manages and a procedure to determine and track which waterbodies are or are not meeting water quality standards as outlined by Section 303(d) of the CWA. This project helps meet this DOI GRPA goal by inventorying and monitoring in a geographic information system for the NPS: (1) CWA 303(d) quality impaired waters and causes; and (2) hydrographic statistics based on the United States Geological Survey (USGS) National Hydrography Dataset (NHD). Hydrographic and 303(d) impairment statistics were evaluated based on a combination of 1:24,000 (NHD) and finer scale data (frequently provided by state GIS layers).

The statistic shows the global market size of the IT project and portfolio management (IT PPM) market from 2014 to 2019 and a forecast for 2024. In 2019, The total market size of the global IT project and portfolio management (IT PPM) was at **** billion U.S. dollars.

Financial overview and grant giving statistics of Washington Research Project Inc

Comprehensive YouTube channel statistics for MAHESA Project, featuring 367,000 subscribers and 133,972,054 total views. This dataset includes detailed performance metrics such as subscriber growth, video views, engagement rates, and estimated revenue. The channel operates in the Music category and is based in ID. Track 100 videos with daily and monthly performance data, including view counts, subscriber changes, and earnings estimates. Analyze growth trends, engagement patterns, and compare performance against similar channels in the same category.

This data set is no longer current – The most current data and all historical data sets can be found at https://data.nrel.gov/submissions/244 This database represents a list of community solar projects identified through various sources as of June 2019. The list has been reviewed but errors may exist and the list may not be comprehensive. Errors in the souces e.g. press releases may be duplicated in the list. Blank spaces represent missing information. NREL invites input to improve the database including to - correct erroneous information - add missing projects - fill in missing information - remove inactive projects. Updated information can be submitted to the contact(s) located on the current data set page linked at the top.

Overview This is the WFIP2 event log covering all sites and instruments for the entire project duration. Final Event Log and Common Case Study Set Additional details may be added here.

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