Tesla Inc.’s most recent quarterly vehicle production volume came to nearly ******* units. Tesla's production level in the first quarter of 2023 decreased by some **** percent quarter-on-quarter and by approximately **** percent year-on-year. Growth amid crisis It was anticipated that the coronavirus outbreak in China would affect the productivity of Tesla's Shanghai factory. However, Tesla's output reached almost ******* vehicles in the first two quarters of 2020. As the virus began to spread to the American continent, work at the U.S. factory in Fremont, California was stopped. The plant's reopening in May was met with criticism but contributed to the over ****** units that were produced in the second quarter of 2020. Tesla witnessed production growth in all subsequent quarters. The company's output level reached a new record in the fourth quarter of 2024. Leading the electric vehicle market Tesla produced over **** million vehicles in 2024, a *** percent decrease on the company's stellar 2023, which had been driven to a large extent by Model 3 and Model Y production and sales figures. The Tesla Model 3 was the world’s best-selling plug-in electric vehicle in 2020 and 2021. In 2024, it faced tough competition from other Tesla models, including the Model Y and the refreshed Model S Plaid, and came third in the bestseller ranking.

United States Electric Vehicle Sales: Year to Date: Tesla: Tesla Cybertruck data was reported at 6,406.000 Unit in Mar 2025. This records a decrease from the previous number of 38,965.000 Unit for Dec 2024. United States Electric Vehicle Sales: Year to Date: Tesla: Tesla Cybertruck data is updated quarterly, averaging 11,558.000 Unit from Mar 2024 (Median) to Mar 2025, with 5 observations. The data reached an all-time high of 38,965.000 Unit in Dec 2024 and a record low of 2,803.000 Unit in Mar 2024. United States Electric Vehicle Sales: Year to Date: Tesla: Tesla Cybertruck data remains active status in CEIC and is reported by Cox Automotive. The data is categorized under Global Database’s United States – Table US.RA008: Electric Vehicle Sales: by Brand and Model: ytd.

A digital record of all Tesla fires - including cars and other products, e.g. Tesla MegaPacks - that are corroborated by news articles or confirmed primary sources. Latest version hosted at https://www.tesla-fire.com.

This dataset encompasses about 60 individual drives of a 2020 Tesla Model 3 with Autopilot in its relevant operational domain covering more than 1,000 miles. The majority of the data was collected during highway and suburban driving. Information collected includes vehicle CAN data as well as Lidar and camera data from a vehicle mounted sensor array. Vehicle CAN data and information on traffic surrounding the Ego-vehicle derived from the sensor array are postprocessed and merged to provide one combined CVS data file per drive.

Tesla Stock Dataset from 2010 to 2021

Date: Represents the date at which the share is traded in the stock market.

Open: Represents the opening price of the stock at a particular date. It is the price at which a stock started trading when the opening bell rang.

Close: Represents the closing price of the stock at a particular date. It is the last buy-sell order executed between two traders. The closing price is the raw price, which is just the cash value of the last transacted price before the market closes.

High: The high is the highest price at which a stock is traded during a period. Here the period is a day.

Low: The low is the lowest price at which a stock is traded during a period. Here the period is a day.

Adj Close: The adjusted closing price amends a stock's closing price to reflect that stock's value after accounting for any corporate actions. The adjusted closing price factors in corporate actions, such as stock splits, dividends, and rights offerings.

Volume: Volume is the number of shares of security traded during a given period of time. Here the security is stock and the period of time is a day.

Sources: Investopedia

Quarterly data on vehicle registration by fuel type, vehicle type and number of vehicles, Canada, the provinces, census metropolitan areas and census sub-divisions.

The Tesla Trading Signal dataset provides a structured analysis of sentiment, events, and narratives influencing Tesla’s equity performance. The most recent update issues a SELL signal with 85% confidence, reflecting a sharp deterioration in market outlook. Key factors driving this bearish signal include: Legal and regulatory pressures: Ongoing lawsuits, workplace safety concerns, and heightened scrutiny from Italian regulators. Brand image challenges: Public protests against CEO Elon Musk and growing reputational risks. Operational risks: A major vehicle recall weighing on investor confidence. Mixed news sentiment: While the Cybertruck’s safety rating and expansion plans in India offered optimism, they were overshadowed by persistent negative developments. The dataset also highlights top themes such as regulatory environment, legal challenges, and macroeconomic drivers. Influential events include intensified European regulatory scrutiny, reinforcing Tesla’s near-term downside risk. For systematic and quantitative traders, this dataset provides structured equity intelligence, mapping how legal, regulatory, and sentiment-driven narratives function as leading indicators for stock performance and volatility.

We present an ultra-high resolution MRI dataset of an ex vivo human brain specimen. The brain specimen was donated by a 58-year-old woman who had no history of neurological disease and died of non-neurological causes. After fixation in 10% formalin, the specimen was imaged on a 7 Tesla MRI scanner at 100 µm isotropic resolution using a custom-built 31-channel receive array coil. Single-echo multi-flip Fast Low-Angle SHot (FLASH) data were acquired over 100 hours of scan time (25 hours per flip angle), allowing derivation of synthesized FLASH volumes. This dataset provides an unprecedented view of the three-dimensional neuroanatomy of the human brain. To optimize the utility of this resource, we warped the dataset into standard stereotactic space. We now distribute the dataset in both native space and stereotactic space to the academic community via multiple platforms. We envision that this dataset will have a broad range of investigational, educational, and clinical applications that will advance understanding of human brain anatomy in health and disease.

Content

There's the data of last 5 years of Tesla stock price having attributes such as date itself, it's opening bid price, high and low of the days, close price and the volume of trade.

Certain questions can be answered using the dataset such as:

Q: Enhance the data quality by adding "percent change" attribute (as compared to last day close price of-coarse) Q: How the stock price was impacted in the wake of COVID Pandemic (which came at significant level around 1st week of Mar 2020 onwards) Q: At what days of the week it shows uptrend & downtrend more often (if it shows any such specific trend at all) Q: When it showed dramatic bullish trend and the possible potential reason behind it?

Kindly upvote if it helps. Will be appreciated. Thank You Happy Learning ^_^

Context

This is a dataset for Tesla Stock Price Prediction taken from https://www.quandl.com/tools/python

Content

What's inside is more than just rows and columns. Make it easy for others to get started by describing how you acquired the data and what time period it represents, too.

Acknowledgements

We wouldn't be here without the help of others. If you owe any attributions or thanks, include them here along with any citations of past research.

Inspiration

Your data will be in front of the world's largest data science community. What questions do you want to see answered?

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

Tesla Stock: An Analysis of its Growth, Volatility, and Future Prospects

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

This dataset is a copy of a subset of the full Stanford Cars dataset

• Stanford Cars Dataset, Stanford AI
• Stanford Cars Dataset, Papers with Code

The original dataset contained 16,185 images of 196 classes of cars.

The classes are typically at the level of Make, Model, Year, e.g. 2012 Tesla Model S or 2012 BMW M3 coupe in the original dataset, and in this subset of the full dataset (v3, TestData and v4, original_raw-images).

v4 (original_raw-images) contains a generated version of the original, raw images, without any modified classes

v8 (classes-Modified_raw-images) contains a generated version of the raw images, with the Modify Classes preprocessing feature used to remap or omit the following classes: 1. bike, moped --remapped to--> motorbike 2. cng, leguna, easybike, smart fortwo Convertible 2012, and all other specific car makes with named classes (such as Acura TL Type-S 2008) --remapped to--> vehicle 3. rickshaw, boat, bicycle --> omitted

v9 (FAST-model_mergedAllClasses-augmented_by3x) contains a generated version of the raw images, with the Modify Classes preprocessing feature used to remap or omit the following classes: 1. bike, moped --remapped to--> motorbike 2. cng, leguna, easybike, smart fortwo Convertible 2012, and all other specific car makes with named classes (such as Acura TL Type-S 2008) --remapped to--> vehicle 3. rickshaw, boat, bicycle --> omitted

v10 (ACCURATE-model_mergedAllClasses-augmented_by3x) contains a generated version of the raw images, with the Modify Classes preprocessing feature used to remap or omit the following classes: 1. bike, moped --remapped to--> motorbike 2. cng, leguna, easybike, smart fortwo Convertible 2012, and all other specific car makes with named classes (such as Acura TL Type-S 2008) --remapped to--> vehicle 3. rickshaw, boat, bicycle --> omitted

Citation:

3D Object Representations for Fine-Grained Categorization Jonathan Krause, Michael Stark, Jia Deng, Li Fei-Fei 4th IEEE Workshop on 3D Representation and Recognition, at ICCV 2013 (3dRR-13). Sydney, Australia. Dec. 8, 2013. pdf BibTex slides

Context

3D object representations are valuable resources for multi-view object class detection and scene understanding. Fine-grained recognition is a growing subfield of computer vision that has many real-world applications on distinguishing subtle appearances differences. This cars dataset contains great training and testing sets for forming models that can tell cars from one another. Data originated from Stanford University AI Lab (specific reference below in Acknowledgment section).

Content

The Cars dataset contains 16,185 images of 196 classes of cars. The data is split into 8,144 training images and 8,041 testing images, where each class has been split roughly in a 50-50 split. Classes are typically at the level of Make, Model, Year, ex. 2012 Tesla Model S or 2012 BMW M3 coupe.

Acknowledgements

Data source and banner image: http://ai.stanford.edu/~jkrause/cars/car_dataset.html contains all bounding boxes and labels for both training and tests.

If you use this dataset, please cite the following paper:

3D Object Representations for Fine-Grained Categorization

Jonathan Krause, Michael Stark, Jia Deng, Li Fei-Fei

4th IEEE Workshop on 3D Representation and Recognition, at ICCV 2013 (3dRR-13). Sydney, Australia. Dec. 8, 2013.

Inspiration

• Can you form a model that can tell the difference between cars by type or colour?
• Which cars are manufactured by Tesla vs BMW?

The dataset contains information about world's biggest companies.

Among them you can find companies founded in the US, the UK, Europe, Asia, South America, South Africa, Australia.

The dataset contains information about the year the company was founded, its' revenue and net income in years 2018 - 2020, and the industry.

I have included 2 csv files: the raw csv file if you want to practice cleaning the data, and the clean csv ready to be analyzed.

The third dataset includes the name of all the companies included in the previous datasets and 2 additional columns: number of employees and name of the founder.

In addition there's tesla.csv file containing shares prices for Tesla.