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...

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

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.

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.

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.

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) ...

This was a exiting case study for the Google Data Analytics Certification 2023. I choose to do the Case Study 2, the goal was as a business analyst for a small health tracker company how can we use the data from Fitbit users to inform a decision for growth when comparing it to one of Bellabeat's products. I included apple watch users since the data did appear limited in the sample size being 33 participants and with the apple watch users the sample size went up to 59 participants.

I have included my notes from data cleaning process and a power point on my findings and recommendation.

Datasets were not my own and belong to Datasets - ‘FitBit Fitness Tracker Data’ by Mobius, 2022, https://www.kaggle.com/datasets/arashnic/fitbit License: CC0: Public Domain, sources: https://zenodo.org/record/53894#.X9oeh3Uzaao - ‘Apple Watch and Fitbit data’ by Alejandro Espinosa, 2022, https://www.kaggle.com/datasets/aleespinosa/apple-watch-and-fitbit-data, License: CC0: Public Domain, sources: https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/ZS2Z2J

Abstract

This project contains acceleration (in units of g) and heart rate (bpm, measured from photoplethysmography) recorded from the Apple Watch, as well as labeled sleep scored from gold-standard polysomnography. Data were collected at the University of Michigan from June 2017 to March 2019, and there are 31 subjects in total. Code to read and process these files is available on GitHub. The paper corresponding to the work is Walch et al., "Sleep stage prediction with raw acceleration and photoplethysmography heart rate data derived from a consumer wearable device", SLEEP (2019).

Background

There are a number of consumer wearable devices purporting to track sleep on the market; however, the algorithms used to score sleep in these devices are rarely disclosed. On top of that, the raw sensor data from the devices are not usually available for use outside the manufacturer. This limits the usefulness of these devices in research and the clinic. We wrote an app to extract heart rate and accelerometer data from the Apple Watch. We collected data via the Apple Watch using this app from people undergoing polysomnography, as well as in the week leading up to the sleep lab recording. We then looked at the contributions of motion, heart rate, and a proxy for the circadian clock to the ability of classifiers to score sleep.

Data Description

The following types of data are provided:

• motion (acceleration): Recorded from the Apple Watch and saved as txt files with the naming convention '[subject-id-number]_acceleration.txt' Each line in this file has the format: date (in seconds since PSG start), x acceleration (in g), y acceleration, z acceleration
• heart rate (bpm): Recorded from the Apple Watch and saved as txt files with the naming convention '[subject-id-number]_heartrate.txt' Each line in this file has the format: date (in seconds since PSG start), heart rate (bpm)
• steps (count): Recorded from the Apple Watch and saved in the format '[subject-id-number]_steps.txt Each line in this file has the format: date (in seconds since PSG start), steps (total in bin from this timestamp to next timestamp)
• labeled sleep: Recorded from polysomnography and saved in the format '[subject-id-number]_labeled_sleep.txt' Each line in this file has the format: date (in seconds since PSG start) stage (0-5, wake = 0, N1 = 1, N2 = 2, N3 = 3, REM = 5)

Citation

Walch, O. (2019). Motion and heart rate from a wrist-worn wearable and labeled sleep from polysomnography (version 1.0.0). PhysioNet. https://doi.org/10.13026/hmhs-py35.

https://doi.org/10.1093/sleep/zsz180

Goldberger, A., Amaral, L., Glass, L., Hausdorff, J., Ivanov, P. C., Mark, R., ... & Stanley, H. E. (2000). PhysioBank, PhysioToolkit, and PhysioNet: Components of a new research resource for complex physiologic signals. Circulation [Online]. 101 (23), pp. e215–e220.

Acknowlegement

Foto von Angus Gray auf Unsplash

🏦 Share prices of technological giants: Meta, Apple, Amazon, Netflix and Google till February 2024

Meta (share prices since 2013 - 02.2024)

Meta Platforms, Inc., doing business as Meta, and formerly named Facebook, Inc., and The Facebook, Inc., is an American multinational technology conglomerate based in Menlo Park, California. The company owns and operates Facebook, Instagram, Threads, and WhatsApp, among other products and services. Meta ranks among the largest American information technology companies, alongside other Big Five corporations Alphabet (Google), Amazon, Apple, and Microsoft. The company was ranked #31 on the Forbes Global 2000 ranking in 2023. https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F10074224%2F0092cfb5e21b4649fead60c2b969b799%2Fmeta.webp?generation=1708923032286895&alt=media" alt="">

Apple (share prices since 1981 - 02.2024)

Apple Inc. (formerly Apple Computer, Inc.) is an American multinational technology company headquartered in Cupertino, California, in Silicon Valley. It designs, develops, and sells consumer electronics, computer software, and online services. Devices include the iPhone, iPad, Mac, Apple Watch, and Apple TV; operating systems include iOS, iPadOS, and macOS; and software applications and services include iTunes, iCloud, and Apple Music.

As of March 2023, Apple was the world's largest company by market capitalization, but it lost this position to Microsoft in January 2024. In 2022, it was the largest technology company by revenue, with US$394.3 billion. As of June 2022, Apple was the fourth-largest personal computer vendor by unit sales, the largest manufacturing company by revenue, and the second-largest manufacturer of mobile phones in the world. It is one of the Big Five American information technology companies, alongside Alphabet (the parent company of Google), Amazon, Meta (the parent company of Facebook), and Microsoft. https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F10074224%2F355fd858e8cf894c85d34012937de7cb%2Fmaxresdefault.jpg?generation=1708925753123441&alt=media" alt="">

Amazon (share prices since 1998 - 02.2024)

Amazon.com, Inc., doing business as Amazon, is an American multinational technology company focusing on e-commerce, cloud computing, online advertising, digital streaming, and artificial intelligence. It is considered one of the Big Five Amhttps://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F10074224%2F8c7eaaa939f4afa87230e4cfc7a73965%2F02.webp?generation=1708925895528644&alt=media" alt="">erican technology companies; the other four are Alphabet (parent company of Google), Apple, Meta (parent company of Facebook), and Microsoft.

Netflix (share prices since 2003 - 02.2024)

Netflix is an American subscription video on-demand over-the-top streaming service. The service primarily distributes original and acquired films and television shows from various genres, and it is available internationally in multiple languages.

Launched on January 16, 2007, nearly a decade after Netflix, Inc. began its pioneering DVD‑by‑mail movie rental service, Netflix is the most-subscribed video on demand streaming media service, with 260.28 million paid memberships in more than 190 countries as of January 2024. By 2022, "Netflix Original" productions accounted for half of its library in the United States and the namesake company had ventured into other categories, such as video game publishing of mobile games via its flagship service. As of October 2023, Netflix is the 24th most-visited website in the world with 23.66% of its traffic coming from the United States, followed by the United Kingdom at 5.84% and Brazil at 5.64%. https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F10074224%2F98710ac371805da8047b14bbd5f73b2e%2Fhppsunsetbronsonicon_1200xx4905-2759-184-0.jpg?generation=1708926012776496&alt=media" alt="">

Google (share prices since 2005 - 02.2024)

Google LLC (Alphabet Inc.) is an American multinational technology company focusing on artificial intelligence, online advertising, search engine technology, cloud computing, computer software, quantum computing, e-commerce, and consumer electronics. It has been referred to as "the most powerful company in the world" and as one of the world's most valuable brands due to its market dominance, data collection, and technological advantages in the field of artificial intelligence. Google's parent company Alphabet Inc. is one of the five Big Tech companies, alongside Amazon, Apple, Meta, and Microsoft. https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F10074224%2F86a517c27caafb1d081b4facc0752a9d%2FGoogle%20HQ.jpg?generation=1708926639236016&alt=media" alt="">