First introduced in 2015, the unit sales of Apple Watches have grown until 2022 and then decreased in 2023. At the end of the given period, Apple sold over ** million smartwatches, down from almost ** million Apple Watch unit sales recorded in 2022.

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?

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

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 Apple Product Review Dataset is a valuable resource for analyzing user opinions, satisfaction, and sentiments regarding various Apple products. This dataset typically contains user-generated reviews and ratings from popular platforms like Apple’s App Store or e-commerce websites. Each review provides insight into the users’ experiences, preferences, and challenges with devices such as iPhones, MacBooks, iPads, and Apple Watches. By analyzing these reviews, researchers and businesses can gain an understanding of product strengths and weaknesses, as well as emerging trends in customer expectations. One of the key aspects of this dataset is the sentiment analysis it allows. By categorizing reviews into positive, negative, or neutral sentiments, data analysts can measure overall customer satisfaction and identify common pain points. For example, users might praise the sleek design and advanced technology of an iPhone while expressing frustration with its battery life or high price. These insights can be incredibly useful for Apple and other tech companies as they work to improve their products based on real-world feedback. Moreover, the dataset helps in identifying patterns of user behavior. Through analysis of the reviews, it is possible to see how different demographics react to Apple products, such as identifying whether younger users are more inclined towards certain features or if professional users demand more productivity-oriented improvements. It can also highlight how user sentiment evolves over time, particularly with software updates or the release of new models. In conclusion, the Apple Product Review Dataset serves as a rich source of information for companies looking to enhance their product development, marketing strategies, and customer support systems. With proper data analysis, it is possible to drive better customer engagement, enhance product features, and maintain brand loyalty by addressing the core issues customers face.

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.

These datasets provide a comprehensive overview of Apple's financial performance by product and geographical segments from 2010 to 2024. The data is compiled directly from the company's annual 10-K filings with the U.S. Securities and Exchange Commission (SEC), ensuring a high degree of reliability and accuracy.

Dataset 1: Apple Sales by Product Segment This dataset details Apple's net sales across its major product and service categories. It shows the evolution of Apple's business model from a hardware-centric company to one with a significant and growing services component. The columns represent different business lines that have changed over time:

iPhone, Mac, iPad: Core hardware product categories.

iPod: An early product category whose sales data was eventually reclassified into a broader segment.

Other Products / Wearables, Home & Accessories: This category has evolved. Prior to 2015, this was a more general "Other Products" category. In later years, it was expanded and renamed to include products like the Apple Watch, AirPods, and HomePod, reflecting their growing strategic importance.

Services: This segment includes revenue from the App Store, Apple Music, iCloud, Apple Pay, and other digital services. Its consistent growth highlights Apple's successful diversification strategy.

Dataset 2: Apple Sales by Geographic Segment This dataset breaks down Apple's net sales by major global regions, providing insight into the company's international market penetration and performance. It includes data for:

Americas: Includes North and South America.

Europe: Includes European countries, as well as Africa, India, and the Middle East.

Greater China: Includes mainland China, Hong Kong, and Taiwan.

Japan: A distinct and long-standing key market for Apple.

Rest of Asia Pacific: Includes Australia and various other countries in Asia.

Example Use Cases These datasets can be used for a wide range of analytical purposes, including:

Trend Analysis and Forecasting: Analysts can use this data to identify long-term trends in Apple's sales. For example, by plotting the data, one could easily visualize the rapid growth of the Services segment, or the decline of the iPod as a standalone product. This can help in forecasting future revenue and strategic planning.

Market Share Analysis: By comparing Apple's sales data to the total market size for smartphones, personal computers, or tablets, analysts can estimate Apple's market share over time. This can be done on a global or regional basis to identify areas of strength and weakness.

Strategic Decision Making: Business leaders can use the geographic sales data to evaluate the success of market-specific strategies. For instance, a company could compare the growth rates in Greater China and Europe to determine which region offers more potential for a new product launch.

Comparative Analysis: The data can be used to compare Apple's performance against its competitors like Samsung or Google. By normalizing the data, one could analyze which company has a more diversified revenue stream or a stronger presence in specific international markets. I

Current-Deferred-Revenue Time Series for Apple Inc. Apple Inc. designs, manufactures, and markets smartphones, personal computers, tablets, wearables, and accessories worldwide. The company offers iPhone, a line of smartphones; Mac, a line of personal computers; iPad, a line of multi-purpose tablets; and wearables, home, and accessories comprising AirPods, Apple Vision Pro, Apple TV, Apple Watch, Beats products, and HomePod, as well as Apple branded and third-party accessories. It also provides AppleCare support and cloud services; and operates various platforms, including the App Store that allow customers to discover and download applications and digital content, such as books, music, video, games, and podcasts, as well as advertising services include third-party licensing arrangements and its own advertising platforms. In addition, the company offers various subscription-based services, such as Apple Arcade, a game subscription service; Apple Fitness+, a personalized fitness service; Apple Music, which offers users a curated listening experience with on-demand radio stations; Apple News+, a subscription news and magazine service; Apple TV, which offers exclusive original content and live sports; Apple Card, a co-branded credit card; and Apple Pay, a cashless payment service, as well as licenses its intellectual property. The company serves consumers, and small and mid-sized businesses; and the education, enterprise, and government markets. It distributes third-party applications for its products through the App Store. The company also sells its products through its retail and online stores, and direct sales force; and third-party cellular network carriers and resellers. The company was formerly known as Apple Computer, Inc. and changed its name to Apple Inc. in January 2007. Apple Inc. was founded in 1976 and is headquartered in Cupertino, California.

All data points were acquired using my Apple Watch SE. Important to acknowledge that data points like heart rate and stride length are not exact, but we can use data points to establish a trend. The dataset will be updated every month.

Also, I couldn't figure out how to export the data from my phone, so I am going old-fashioned, typing in each point after each run.

The Activity recognition from in-the-WIld SmartwatchEs (ArWISE) dataset is based on sensor data and activity labels collected from smart watches as part of several studies for a total of 854 participants across 20 cohorts. The sensor data consisted of 10Hz accelerometer, gyroscope, and location information that has been processed into anonymized features computed from one minute windows of data: local time, date, and day of week; mean and standard deviation of yaw, pitch, roll, x/y/z/total rotation rate, x/y/z/total acceleration, speed, course, distance from home, and bearing from home. The activity label is one of eat, errands, exercise, hobby, housework, hygiene, relax, sleep, socialize, travel, work, other. There are 470M data points total, of which 37M are labeled. Methods We introduce ArWISE (Activity recognition from in-the-Wild SmartwatchEs), a dataset containing labeled and unlabeled data collected by Apple Watches. ArWISE represents readings collected from 20 studies in 2 countries over 8 years. Data Collection Data collection followed a consistent protocol for each study. Participants were given an Apple Watch to wear each day on their non-dominant arm. While they wore the watch, a custom app collected 3d accelerometer and gyroscope readings at 10Hz. Additionally, the app collected the person’s location every minute or when the magnitude of the acceleration vector exceeded a threshold. At random times throughout each day, the smartwatch prompted the participant to select an activity from a scroll-down list that best described their current activity. The distribution of user-provided labels across 12 activity categories are Eat (6.5%), Errands (3.7%), Exercise (4.7%), Hobby (1.1%), Housework (19.7%), Hygiene (1.9%), Other (3.1%), Relax (37.7%), Sleep (3.0%), Socialize (3.7%), Travel (5.6%), Work (9.1%). The label was applied to five minutes of sensor readings ending at the time of the participant’s response. Additionally, an external annotator provided labels for a much greater density of data collected for cohorts 7 and 18. This person used a tool that visualized 3D movement data, a map of visited locations, and time stamps, at arbitrary time frames. While the data collection mechanism was the same for all study cohorts, other parameters varied. These include the number of participants, participant demographics, length of data collection, and other clinical variables that were collected. A summary of study cohort parameters is given in Table 1, where HOA=healthy older adult, SCD=subjective cognitive decline, and MCI=mild cognitive impairment.

Table 1. ArWISE Cohorts.

Cohort Sample Study/participant characteristics

1

4

Younger adults, self-reported activities

2

185

HOA/SCD/MCIa, English and Spanish self-reported activities

3

56

Younger adults, no activity labels

4

46

HOA/SCD/MCI, self-reported activities

5

10

Older adult pairs, no activity labels

6

35

HOA/SCD/MCI, no activity labels

7

37

HOA/SCD/MCI, self-reported activities and expert-annotated activities

8

9

Younger adults, self-reported activities

9

15

Younger adults, self-reported activities

10

13

Younger adults, self-reported activities

11

3

Younger adults, self-reported activities

12

18

Younger adults, self-reported activities

13

10

Younger adults, self-reported activities

14

22

Younger adults, self-reported activities

15

21

HOA/SCD/MCI, no activity labels

16

6

Younger adults, self-reported activities

17

103

HOA/SCD/MCI, self-reported activities

18

16

HOA/SCD/MCI, self-reported activities and expert-annotated activities

19

16

HOA/SCD/MCI, self-reported activities

20

229

HOA/SCD/MCI, no activity labels

Dataset Characteristics The ArWISE dataset is unique among the resources that are typically available for human activity recognition. Some of the most-analyzed datasets reflect movement categories based on data that are collected in controlled settings [1], [2]. However, more recent wearable sensor datasets represent activities observed in uncontrolled settings. Although 150 participants are monitored for only 24 hours with movement-only sensors, Capture-24 [3] includes labels for functional activities of household chores, sports, and sleep in real-world settings. ExtraSensory [4] monitors a smaller set of 60 participants with up to 20 seconds of movement and location readings but provides diverse activity and location. The UK Biobank [5] offers 7 days of accelerometry data for 100,000+ participants and Intuition [6] longitudinally observes 23,004 participants, though no ground-truth labels are provided for these data. The ArWISE dataset contains 37,578,059 labeled points from 503 participants across 15 cohorts and 469,881,358 total points for 854 participants across 20 cohorts. Each point represents one minute of data. ArWISE offers unique benefits for HAR analysis, including a large set of participants, functional activity labels, longitudinal observations, and consistency in the data collection mechanism. Data Preprocessing Our functional activity recognition models consider both raw time series data and engineered features. Table 2 summarizes the features that are available for both cases.

Table 2. ArWISE raw and engineered data features.

Type Category Feature

Raw (10Hz)

time

date and time

Raw (10Hz)

motion

yaw, pitch, roll, rotation rate (x,y,z), acceleration (x,y,z)

Raw (10Hz)

location

latitude, longitude, altitude, course, speed

Engineered (1 min)

time

time of day (radians, sin, cos), day of week

Engineered (1 min)

motion

mean & stdev (each raw movement variable), mean & stdev (rotation vector magnitude, acceleration vector magnitude)

Engineered (1 min)

location

mean & stdev (course, speed) mean & stdev (distance from home, latitude distance from home, longitude distance from home) mode & stdev (bearing from home)

Class label activity eat, errands, exercise, hobby, housework, hygiene, relax, sleep, socialize, travel, work, other

We imputed missing values (with mode for location and median for other features) and dropped data points where there was not a complete minute of sensor readings leading up to the label. We also normalized each feature separately. For the engineered features, we aggregated values over one minute leading up to the user (or expert) label. Time of day was represented as a set of sinusoidal features to maintain the periodic nature. We did not use raw location values here, to preserve user privacy and because the values do not easily generalize between individuals. Instead, we defined a person’s home as the location visited most often at the beginning of each day. We then extracted the Haversine distance and trigonometric bearing from the person’s home location. References [1] O. Napoli et al., “A benchmark for domain adaptation and generalization in smartphone-based human activity recognition,” Scientific Data, vol. 11, p. 1192, 2024. [2] A. Reiss, D. Stricker, and G. Hendeby, “Towards robust activity recognition for everyday life: Methods and evaluation,” in Pervasive Computing Technologies for Healthcare, 2013, pp. 25–32. [3] S. Chan et al., “CAPTURE-24: A large dataset of wrist-worn activity tracker data collected in the wild for human activity recognition,” Nature Scientific Data, vol. 11, p. 1135, 2024. [4] Y. Vaizman, K. Ellis, and G. Lanckriet, “Recognizing detailed human context in the wild from smartphones and smartwatches,” IEEE Pervasive Computing, vol. 16, no. 4, pp. 62–74, 2017. [5] C. Sudlow, J. Gallacher, N. Allen, V. Beral, P. Burton, and J. Danesh, “UK Biobank: An open access resource for identifying the causes of a wide range of complex diseases of middle and old age,” PLoS Medicine, vol. 12, no. 3, p. 1001779, 2015. [6] P. M. Butler, J. Yang, R. Brown, M. Hobbs, and A. Becker, “Smartwatch- and smartphone-based remote assessment of brain health and detection of mild cognitive impairment,” Nature Medicine, 2025, doi: https://doi.org/10.1038/s41591-024-03475-9.

Other-Current-Assets Time Series for Apple Inc. Apple Inc. designs, manufactures, and markets smartphones, personal computers, tablets, wearables, and accessories worldwide. The company offers iPhone, a line of smartphones; Mac, a line of personal computers; iPad, a line of multi-purpose tablets; and wearables, home, and accessories comprising AirPods, Apple TV, Apple Watch, Beats products, and HomePod. It also provides AppleCare support and cloud services; and operates various platforms, including the App Store that allow customers to discover and download applications and digital content, such as books, music, video, games, and podcasts, as well as advertising services include third-party licensing arrangements and its own advertising platforms. In addition, the company offers various subscription-based services, such as Apple Arcade, a game subscription service; Apple Fitness+, a personalized fitness service; Apple Music, which offers users a curated listening experience with on-demand radio stations; Apple News+, a subscription news and magazine service; Apple TV+, which offers exclusive original content; Apple Card, a co-branded credit card; and Apple Pay, a cashless payment service, as well as licenses its intellectual property. The company serves consumers, and small and mid-sized businesses; and the education, enterprise, and government markets. It distributes third-party applications for its products through the App Store. The company also sells its products through its retail and online stores, and direct sales force; and third-party cellular network carriers, wholesalers, retailers, and resellers. Apple Inc. was founded in 1976 and is headquartered in Cupertino, California.

The Apple Store is a chain of retail stores owned and operated by Apple Inc. The stores sell various Apple products, including Mac personal computers, iPhone smartphones, iPad tablet computers, Apple Watch smartwatches, Apple TV digital media players, software, and both Apple-branded and selected third-party accessories.

hi from tokyo! I am Daisuke Ishii. I am happy to provide my own AppleWatch health data for open research. if you find some disease trend in data, let me know. my email : dai@jenio.co Follow my Twitter https://twitter.com/ishiid

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me ; 44years old, born in 1975, male, married, japanese, 183cm, 87kg, no kids i do hot yoga twice a week i drink a bottle of beer every 2days i do not eat too much. bad habit is eating ramen noodle since I run my AI startup, I am busy and work in weekend too. maybe too much desk work.

===

data

heartrate stepcount walk and run basal(=basic) energy burned active energy burned exercise time stand hour heart rate variability

===

related arxiv paper : example heart rate machine learning - please try other key word as well

https://www.google.com/search?q=arxiv+machine+learning+hear+rate&oq=arxiv+machine+learning+hear+rate+&aqs=chrome..69i57.7773j0j1&sourceid=chrome&ie=UTF-8

apple watch data analys - medium https://www.google.com/search?sxsrf=ALeKk010KZuIsKnRWwz6eAcTj3xM0XPOAg%3A1592670185682&ei=6TfuXouWKZb_wAOa1bqoDg&q=apple+watch+data+analysis+medium&oq=apple+watch+data+analysis+medium&gs_lcp=CgZwc3ktYWIQAzIFCCEQoAE6BAgAEEM6BQgAEJECOgIIADoGCAAQFhAeOgQIABANOggIIRAWEB0QHjoHCCEQChCgAVCIugJYheYCYJzoAmgCcAB4AIABhwGIAaAbkgEFMTcuMTeYAQCgAQGqAQdnd3Mtd2l6&sclient=psy-ab&ved=0ahUKEwjLrqXl5pDqAhWWP3AKHZqqDuUQ4dUDCAw&uact=5

apple watch data export - medium https://www.google.com/search?sxsrf=ALeKk02FeRJTkvBIG75tQUvkkKbFQYdwAA%3A1592670232662&ei=GDjuXu74J4aHoATqpoeIBA&q=apple+watch+data+export+medium&oq=apple+watch+data+export+medium&gs_lcp=CgZwc3ktYWIQAzoHCAAQRxCwAzoECCEQClDkzi9Y4NcvYNbZL2gBcAB4AIABfogBkQWSAQMxLjWYAQCgAQGqAQdnd3Mtd2l6&sclient=psy-ab&ved=0ahUKEwju5Nj75pDqAhWGA4gKHWrTAUEQ4dUDCAw&uact=5

Altman-Zscore Time Series for Apple Inc. Apple Inc. designs, manufactures, and markets smartphones, personal computers, tablets, wearables, and accessories worldwide. The company offers iPhone, a line of smartphones; Mac, a line of personal computers; iPad, a line of multi-purpose tablets; and wearables, home, and accessories comprising AirPods, Apple Vision Pro, Apple TV, Apple Watch, Beats products, and HomePod, as well as Apple branded and third-party accessories. It also provides AppleCare support and cloud services; and operates various platforms, including the App Store that allow customers to discover and download applications and digital content, such as books, music, video, games, and podcasts, as well as advertising services include third-party licensing arrangements and its own advertising platforms. In addition, the company offers various subscription-based services, such as Apple Arcade, a game subscription service; Apple Fitness+, a personalized fitness service; Apple Music, which offers users a curated listening experience with on-demand radio stations; Apple News+, a subscription news and magazine service; Apple TV, which offers exclusive original content and live sports; Apple Card, a co-branded credit card; and Apple Pay, a cashless payment service, as well as licenses its intellectual property. The company serves consumers, and small and mid-sized businesses; and the education, enterprise, and government markets. It distributes third-party applications for its products through the App Store. The company also sells its products through its retail and online stores, and direct sales force; and third-party cellular network carriers and resellers. The company was formerly known as Apple Computer, Inc. and changed its name to Apple Inc. in January 2007. Apple Inc. was founded in 1976 and is headquartered in Cupertino, California.

Abhishek’s Apple Health CSV Dataset

About: This dataset is a cleaned export of my personal Apple Health data collected through my iPhone and Apple Watch over several years. I parsed the raw HealthKit XML export and transformed it into a ready-to-use CSV so that students, developers, and data scientists can easily practice real-world health data analysis.

What’s included:

Heart Rate: 800,000+ records Steps & Walking Metrics: Step count, distance, walking speed, stride length, gait symmetry Calories: Active & basal energy burned Sleep Analysis: Core, in bed, deep sleep patterns Vitals: Body mass index, weight, height, oxygen saturation, headphone audio exposure Timestamps: Creation, start, and end dates & times in local timezone Device & Source: Source app, device info, source version Columns: type, sourceName, sourceVersion, unit, m_creationDate, m_creationTime, m_creationTime_am_pm, m_creationTimeZone, m_startDate, m_startTime, m_startTime_am_pm, m_startTimeZone, m_endDate, m_endTime, m_endTime_am_pm, m_endTimeZone, value, device

Why I shared this: I believe real, authentic data helps people learn better. This dataset is perfect for:

Time series analysis Data cleaning & transformation practice Exploratory data analysis (EDA) Visualizing health & wellness trends Building forecasting models Personal Note: This is my real data, and I’ve kept my name to give it a personal touch. Sensitive info like GPS or location data has been removed. Please use it respectfully, for non-commercial, educational, and research purposes only.

License: This dataset is shared under CC BY-NC-SA 4.0 — you must give credit, can’t use it commercially, and must share any derivatives under the same license.

Ebit Time Series for Apple Inc. Apple Inc. designs, manufactures, and markets smartphones, personal computers, tablets, wearables, and accessories worldwide. The company offers iPhone, a line of smartphones; Mac, a line of personal computers; iPad, a line of multi-purpose tablets; and wearables, home, and accessories comprising AirPods, Apple Vision Pro, Apple TV, Apple Watch, Beats products, and HomePod, as well as Apple branded and third-party accessories. It also provides AppleCare support and cloud services; and operates various platforms, including the App Store that allow customers to discover and download applications and digital content, such as books, music, video, games, and podcasts, as well as advertising services include third-party licensing arrangements and its own advertising platforms. In addition, the company offers various subscription-based services, such as Apple Arcade, a game subscription service; Apple Fitness+, a personalized fitness service; Apple Music, which offers users a curated listening experience with on-demand radio stations; Apple News+, a subscription news and magazine service; Apple TV, which offers exclusive original content and live sports; Apple Card, a co-branded credit card; and Apple Pay, a cashless payment service, as well as licenses its intellectual property. The company serves consumers, and small and mid-sized businesses; and the education, enterprise, and government markets. It distributes third-party applications for its products through the App Store. The company also sells its products through its retail and online stores, and direct sales force; and third-party cellular network carriers and resellers. The company was formerly known as Apple Computer, Inc. and changed its name to Apple Inc. in January 2007. Apple Inc. was founded in 1976 and is headquartered in Cupertino, California.

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.

Context

Apple has become a household name now a days as many people are using Apple products such as Iphone, Ipad, Apple watch etc. Apple recently became the only company to hit the 2 Trillion dollar mark which is a really great feat. But to be this big Apple had to start somewhere, even in stock market. So here we have the complete Data of Apple stock from its start from 1980 to 2020.

Content

This data set has 7 columns with all the necessary values such as opening price of the stock, the closing price of it, its highest in the day and much more. It has date wise data of the stock starting from 1980 to 2020.

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