Context

Apple's transformation from "Apple Computer, Inc." to the world's first trillion-dollar company was driven by a masterful expansion of its product portfolio. While the iPhone was the star, its success was bolstered and complemented by the steady performance of the Mac, the creation of the tablet market with the iPad, and the explosive growth of the Wearables category with the Apple Watch and AirPods.

This synthetic dataset was created to provide a single, unified view of this incredible journey. It allows analysts, students, and enthusiasts to explore the entire Apple hardware ecosystem side-by-side on an annual basis. Since Apple stopped reporting official unit sales in 2018, this dataset uses a combination of historical reported data and realistic, revenue-based estimations to provide a continuous timeline from 2007 to a projection for 2025.

Content

The dataset consists of a single CSV file, apple_full_product_portfolio_2007_2025.csv. The columns are structured to provide a complete overview of Apple's performance:

Identifier Columns:

Year: The calendar year.

Average_Stock_Price_USD_Annual: The approximate average AAPL stock price for the year, adjusted for splits.

Model Release Columns:

iPhone_Model_Released: The flagship iPhone model(s) launched that year.

MacBook_Model_Released: The year's most significant MacBook releases (e.g., MacBook Air, Pro, key chip updates like M1).

iPad_Model_Released: The year's most significant iPad releases (e.g., iPad, Pro, Air, Mini).

Watch_Model_Released: The year's most significant Apple Watch releases (e.g., Series number, SE, Ultra).

Product Performance Metrics (pattern repeats for each product):

[Product]_Units_Sold_Millions: Estimated units sold for the product line.

[Product]_ASP_USD: Estimated Average Selling Price for the product line.

[Product]_Revenue_Billions: Estimated revenue in billions for the product line. (Products include: iPhone, MacBook, iPad, Watch, AirPods)

Consolidated Financials:

Services_Revenue_Billions: Revenue from services like the App Store, iCloud, Apple Music, etc.

Other_Products_Revenue_Billions: Revenue from all other minor products.

Total_Revenue_Billions: The comprehensive total annual revenue for Apple Inc.

Methodology

This dataset is a carefully constructed synthetic chronicle.

Data before 2018 is based on Apple's official (but now discontinued) unit sale reports and financial statements.

Data from 2018 onwards is estimated based on Apple's public quarterly financial reports, using reported category revenues to inform unit sales and ASP calculations.

Projections for 2024-2025 are conservative forecasts based on recent market trends.

Inspiration (Potential Project Ideas) This rich, multi-product dataset opens the door for deep strategic analysis:

The Rise of an Ecosystem: Create a stacked area chart of all revenue columns to visualize how Apple's revenue mix has evolved from being iPhone-centric to a balanced portfolio with massive growth in Wearables and Services.

Impact of Generational Leaps: Did the introduction of the M1 chip for MacBooks in 2020 have a more significant impact on sales and ASP than the Touch Bar in 2016? Pinpoint key model releases and measure their financial impact.

Cannibalization or Halo Effect?: Explore the relationship between product lines. Did the explosive growth of the iPad in its early years affect MacBook sales? Does a strong iPhone year correlate with a strong Apple Watch year?

Predictive Modeling: With over 18 years of comprehensive data, can you build a model that uses the performance of individual product lines to predict Apple's total revenue or future stock price?

Apple is one of the most influential and recognisable brands in the world, responsible for the rise of the smartphone with the iPhone. Valued at over $2 trillion in 2021, it is also the most valuable...

The Smartwatch Price Dataset contains information about the features and prices of popular smartwatch models from various brands. The dataset includes columns such as Brand, Model, Operating System, Connectivity, Price (USD), Display Type, Display Size (inches), Resolution, Water Resistance (meters), Battery Life (days), Heart Rate Monitor, GPS, and NFC.

Columns

Brand: the manufacturer of the smartwatch

Model: the specific model of the smartwatch

Operating System: the operating system used by the smartwatch (e.g. watchOS, Wear OS, Garmin OS, Fitbit OS, etc.)

Connectivity: the types of connectivity supported by the smartwatch (e.g. Bluetooth, Wi-Fi, Cellular)

Display Type: the type of display technology used by the smartwatch (e.g. AMOLED, Retina, E-Ink, LCD)

Display Size (inches): the size of the smartwatch's display in inches

Resolution: the resolution of the smartwatch's display in pixels

Water Resistance (meters): the depth to which the smartwatch can be submerged in water without damage

Battery Life (days): the estimated battery life of the smartwatch in days

Heart Rate Monitor: whether or not the smartwatch has a built-in heart rate monitor

GPS: whether or not the smartwatch has built-in GPS for location tracking

NFC: whether or not the smartwatch has NFC (Near Field Communication) for contactless payments or other wireless data transfer.

Price (USD): the price of the smartwatch in US dollars

The dataset provides a comprehensive overview of the different smartwatches available in the market and can be used for various purposes such as price comparison, feature analysis, and market research. The data is gathered from various sources such as official brand websites, online retailers, and tech blogs. This dataset can be useful for individuals or businesses interested in the smartwatch industry, as well as researchers and data analysts.

• The purpose of creating this dataset is solely for educational use, and any commercial use is strictly prohibited and this dataset was large language models generated and not collected from actual data sources.

Cover image: https://pin.it/13TyoYn

The number of Apple iPhone unit sales dramatically increased between 2007 and 2024. Indeed, in 2007, when the iPhone was first introduced, Apple shipped around **** million smartphones. By 2024, this number reached over ***** million units. The newest models and iPhone’s lasting popularity Apple has ventured into its 17th smartphone generation with its Phone ** lineup, which, released in September 2025, includes the **, ** Plus, ** Pro and Pro Max. Powered by the A19 bionic chip and running on iOS **, these models present improved displays, cameras, and functionalities. On the one hand, such features come, however, with hefty price tags, namely, an average of ***** U.S. dollars. On the other hand, they contribute to making Apple among the leading smartphone vendors worldwide, along with Samsung and Xiaomi. In the first quarter of 2024, Samsung shipped over ** million smartphones, while Apple recorded shipments of roughly ** million units. Success of Apple’s other products Apart from the iPhone, which is Apple’s most profitable product, Apple is also the inventor of other heavy-weight players in the consumer electronics market. The Mac computer and the iPad, like the iPhone, are both pioneers in their respective markets and have helped popularize the use of PCs and tablets. The iPad is especially successful, having remained as the largest vendor in the tablet market ever since its debut. The hottest new Apple gadget is undoubtedly the Apple Watch, which is a line of smartwatches that has fitness tracking capabilities and can be integrated via iOS with other Apple products and services. The Apple Watch has also been staying ahead of other smart watch vendors since its initial release and secures around ** percent of the market share as of the latest quarter.

In summary, this dataset provides a comprehensive record of Apple product appearances in various movies and TV shows, along with the frequency of their occurrences. Analyzing this dataset can offer valuable insights into Apple's strategic product placement endeavors and their marketing strategies within the entertainment industry. this dataset was scraped from the productplacementblog which claims is the best database in the product placement field over internet.

Note: Following the data scraping process, I incorporated additional information from the IMDb dataset available on Kaggle to enhance this dataset. This augmentation allowed me to determine the 'startYear,' 'averageRating,' and 'numVotes' columns. It is important to note that 'startYear' indicates the year of the title's initial release, not the precise release date. Furthermore, the 'imgCount' column represents the count of timestamps or scenes featuring Apple products. In cases where multiple Apple products appear within the same Movie/Show, we do not have specific scene-level granularity to discern which scenes correspond to each product. Therefore, 'imgCount' reflects the cumulative count of scenes where any Apple product was showcased

Content

• tconst: An alphanumeric unique identifier for each title.
• Title: The title of the movie or TV show.
• Movie / Show: A binary indicator (0 or 1) distinguishing between movies and TV shows.
• Season: Numeric representation of the season when the product appeared (in the case of TV shows).
• Episode: Numeric value representing the episode in which the product appeared (for TV shows).
• imgCount: The count of timestamps/scenes featuring Apple products.
• iPhone/iPad/iMac/MacBook/macOS/AirPods/Apple Watch: Eight boolean columns indicating which Apple products were featured.
• averageRating: IMDb's average rating of the title.
• numVotes: The number of IMDb votes the title received. -**startYear**: The year of the title's initial release.

Exploring Daily Weather Report Usage

Understanding Regional and Socioeconomic Variations

By FiveThirtyEight [source]

About this dataset

This dataset contains survey responses from people about their daily weather report usage and weather check. It consists of columns such as Do You Typically Check a Daily Weather Report?, How do you Typically Check the Weather?, If You Had a Smartwatch (like the Soon to be Released Apple Watch), How Likely or Unlikely Would You Be to Check the Weather on That Device? Age, What is Your Gender?, and US Region. With this data, we can explore usage patterns in checking for daily weather reports across different regions, genders, ages and preferences for smartwatch devices in doing so. This dataset offers an interesting insight into our current attitudes towards checking for the weather with technology - and by understanding these patterns better, we can create more engaging experiences tailored to individuals’ needs

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How to use the dataset

To get started, it is helpful to first examine the columns in the dataset. The columns are Do you typically check a daily weather report?, How do you typically check the weather?, If you had a smartwatch (like the soon to be released Apple Watch), how likely or unlikely would you be to check the weather on that device?, Age, What is your gender?, US Region. Each row contains data for one survey participant, with their answers for each column included in each row.

The data can be used for exploring correlations between factors such as age, gender, region/location, daily weather checking habits/preferences etc.. Some of these variables are numerical (such as age) and others are categorical (such as gender). You can use this data when creating visualizations showing relationships between these factors. You may also want to create summary tables showing average values for different categories of each factor, allowing for easy comparison across groups or over time periods (depending on how much historical data is available).

Research Ideas

• Analyzing trends in the usage of daily weather reports by age, gender and region.
• Exploring consumer preferences for checking the weather via smartwatches and mobile devices in comparison to other methods (e.g., TV or radio).
• Examining correlations between people's likelihood to check their daily weather report and their demographic characteristics (location, age, gender)

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: weather-check.csv | Column name | Description | |:-------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------| | Do you typically check a daily weather report? | This column indicates whether or not the respondent typically checks a daily weather report. (Categorical) | | How do you typically check the weather? | This column indicates how the respondent typically checks the weather. (Categorical) | | If you had a smartwatch (like the soon to be released Apple Watch), how likely or unlikely would you be to check the weather on that device? | This column indicates how likely or unlikely the respondent would be to check the weather on a smartwatch. (Categorical) | | Age | This column indicates the age of the respondent. (Numerical) ...

IntroductionAlthough allogeneic hematopoietic stem cell transplantation (HCT) can be a curative therapy for hematologic disorders, it is associated with treatment-related complications and losses in cardiorespiratory fitness and physical function. High-intensity interval training (HIIT) may be a practical way to rapidly improve cardiorespiratory fitness and physical function in the weeks prior to HCT. The primary aim of this study was to assess the feasibility of implementing a pre-HCT home-based HIIT intervention. The secondary aim was to evaluate pre to post changes in cardiorespiratory fitness and physical function following the intervention.MethodsThis was a single-arm pilot study with patients who were scheduled to undergo allogeneic HCT within six months. Patients were instructed to complete three 30-minute home-based HIIT sessions/week between the time of study enrollment and sign-off for HCT. Sessions consisted of a 5-minute warm-up, 10 high and low intervals performed for one minute each, and a 5-minute cool-down. Prescribed target heart rates (HR) for the high- and low-intensity intervals were 80–90% and 50–60% of HR reserve, respectively. Heart rates during HIIT were captured via an Apple Watch and were remotely monitored. Feasibility was assessed via retention, session adherence, and adherence to prescribed interval number and intensities. Paired t-tests were used to compare changes in fitness (VO2peak) and physical function [Short Physical Performance Battery (SPPB), 30-second sit to stand, and six-minute walk test (6MWT)] between baseline and sign-off. Pearson correlations were used to determine the relationship between intervention length and changes in cardiorespiratory fitness or functional measures.ResultsThirteen patients (58.8±11.6 years) participated in the study, and nine (69.2%) recorded their training sessions throughout the study. Median session adherence for those nine participants was 100% (IQR: 87–107). Adherence to intervals was 92% and participants met or exceeded prescribed high-intensity HR on 68.8±34.8% of intervals. VO2peak improved from baseline to sign-off (14.6±3.1 mL/kg/min to 17.9±3.3 mL/kg/min; p

The dataset is meant for performing visualization activities. You are expected to explore data and plot various aspects of it to present relevant and meaningful information

| Header | Definition| |Do you typically check a daily weather report? | Yes or No | |How do you typically check the weather? | "The Weather Channel", "Local TV News", "Radio weather", "Internet search", "The default weather app on your phone", "Newsletter", "Newspaper", "A specific website or app (please provide the answer) A specific website or app (please provide the answer) If they responded this value for the second question, they were asked to write-in the app or website they used.| |If you had a smartwatch (like the soon to be released Apple Watch), how likely or unlikely would you be to check the weather on that device?| "Very Likely", "Somewhat Likely", "Somewhat unlikely", "Very unlikely"| |Age | 18-29, 30-44, 45-59, 60+| |What is your gender?| Female, Male| |How much total combined money did all members of your HOUSEHOLD earn last year?| $0 to $9,999, $10,000 to $24,999, $25,000 to $49,999, $50,000 to $74,999, $75,000 to $99,999, $100,000 to $124,000, $125,000 to $149,999, $150,000 to $174,999, $175,000 to $199,999, $200,000+, Prefer not to answer.| |US Region| New England, Middle Atlantic, East North Central, West North Central, South Atlantic, East South Central, West South Central, Mountain, Pacific. |

This dataset contains the data behind the story Where People Go To Check The Weather.. The data is copyright of Disney as mentioned on this site.