This dataset is about books. It has 1 row and is filtered where the book is Jump start your business brain : win more, lose less and make more money, with your sales, marketing and business development. It features 7 columns including author, publication date, language, and book publisher.

Corporate Profits in the United States increased to 3266.20 USD Billion in the second quarter of 2025 from 3203.60 USD Billion in the first quarter of 2025. This dataset provides the latest reported value for - United States Corporate Profits - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

This dataset is about books. It has 2 rows and is filtered where the book is Money and how to make more of it. It features 7 columns including author, publication date, language, and book publisher.

This dataset is about books. It has 1 row and is filtered where the book is A richer you : how to make the most of your money. It features 7 columns including author, publication date, language, and book publisher.

This dataset is about book subjects. It has 2 rows and is filtered where the books is Work From Home : How to make money working at home - and get the most out of life. It features 10 columns including number of authors, number of books, earliest publication date, and latest publication date.

About Dataset

Context

Analytics refers to the methodical examination and calculation of data or statistics. Its purpose is to uncover, interpret, and convey meaningful patterns found within the data. Additionally, analytics involves utilizing these data patterns to make informed decisions. It proves valuable in domains abundant with recorded information, employing a combination of statistics, computer programming, and operations research to measure performance.

Businesses can leverage analytics to describe, predict, and enhance their overall performance. Various branches of analytics encompass predictive analytics, prescriptive analytics, enterprise decision management, descriptive analytics, cognitive analytics, Big Data Analytics, retail analytics, supply chain analytics, store assortment and stock-keeping unit optimization, marketing optimization and marketing mix modeling, web analytics, call analytics, speech analytics, sales force sizing and optimization, price and promotion modeling, predictive science, graph analytics, credit risk analysis, and fraud analytics. Due to the extensive computational requirements involved (particularly with big data), analytics algorithms and software utilize state-of-the-art methods from computer science, statistics, and mathematics.

Data Dictionary

Content

This Dataset consists of salaries for Data Scientists, Machine Learning Engineers, Data Analysts, and Data Engineers in various cities across India (2022).

-Salary Dataset.csv -Partially Cleaned Salary Dataset.csv

Acknowledgements

This Dataset is created from https://www.glassdoor.co.in/. If you want to learn more, you can visit the Website.

Dominican Republic number dataset helps in many ways to gain huge amounts from business. Besides, this Dominican Republic number dataset is a very valuable directory that you can buy from us at a minimal cost. In addition, it creates many business chances because this country is rich in multiple sectors. Additionally, this directory makes all businesses more famous, competitive, and useful. For instance, this Dominican Republic number dataset builds new opportunities to do business in your selected places. Yet, the vendors can give sales promotions and make huge money from this lead. This time, they can join with the selected group of clients quickly. Overall, it provides the long-term success of your company or business. Dominican Republic phone data is a powerful way to connect many clients. Our Dominican Republic phone data can assist in getting speedy feedback from the public. In other words, our expert unit supplies this cautiously according to your needs. However, the List To Data website is the perfect source to get upgraded sales leads. Thus, check out the packages to find the one that works best for you and watch your business succeed. Moreover, the Dominican Republic phone data is perfect for sending text messages or making phone calls to potential new clients to make deals. By getting this people easily can reach out to people in this area and get positive results from the marketing. Likewise, this library retains millions of phone numbers from different businesses and people. Dominican Republic phone number list transforms your business into a profitable venture. Finding real contacts is very important because the Dominican Republic phone number list helps you reach a genuine audience, saving you time. Even, this List To Data helps you attach with many people quickly and boosts your marketing efforts. In addition, the Dominican Republic phone number list is a great source of earning from B2B and B2C platforms. The Dominican Republic’s economy is strong and diverse, with important sectors like technology, finance, and tourism. Besides, the country’s economy is persisting to grow. In the end, everyone should buy our contact data to earn a massive amount of profit from your targeted locations.

This dataset is about book subjects. It has 2 rows and is filtered where the books is The individual investor revolution : seize your new powers of investing & make more money in the market. It features 10 columns including number of authors, number of books, earliest publication date, and latest publication date.

This dataset is about books. It has 1 row and is filtered where the book is How to think about money : make smarter financial choices and squeeze more happiness out of your cash. It features 7 columns including author, publication date, language, and book publisher.

Taiwan number dataset will help you generate sales leads. First of all, people can start text with product info and descriptions and send buyers through this dataset. In fact, driving a telemarketing campaign is required at present. Moreover, you can literally call and message with the help of this Taiwan number dataset. Also, the Taiwan number dataset is crucial to let your audience know of the features and uses of your product. Above all, by doing this people can easily increase their marketing area. Even, they can create a bond with tier client and gain their trust with this mobile cell phone number list. Taiwan phone data has the potential to get valuable customers. A businessman will be able to earn more money without spending too much on ads. The SMS marketing plan is the best option, that possible to run promotions cheaply here. So, take the contact number directory at an affordable cost and try it for your help. Taiwan phone data will sustain your telemarketing with useful details. On the other hand, if anyone needs to reach someone as soon as possible, then the phone number is the best choice. Besides, you can directly send messages to their inbox through these datasets. Therefore, the numbers on our Taiwan phone data will aid your marketing efforts greatly. Overall, you can use List To Data for your product publicity so that you can find curious buyers among them. Taiwan phone number list is a top-notch mobile database. Likewise, the List To Data website is obstinate about giving our clients the best service for their money. Mainly, we have organized a 24/7 active support group to ensure that. You can ask them anything about this package, or even bring 95% real samples of the lead from them. Both your branding and sales will be enhanced with this Taiwan phone number list. Hence, make a good conclusion for your business and collect this lead right now. Further, the Taiwan phone number list will let you continue to promote any products all across the country. The user count of these platforms is so big that even that provides you with such a big customer base. Clearly, this will surely raise the possibility of finding interested customers for your benefit.

The Starbucks Store Location Dataset was created by Starbucks in order to provide a comprehensive and up-to-date list of all of their store locations around the world. The dataset includes information such as the store's name, address, phone number, and hours of operation. It is available to the public for free on the Starbucks website.

The dataset was created in order to help customers find Starbucks stores near them. It can also be used by businesses and researchers to learn more about Starbucks' global reach. The dataset is updated regularly to ensure that it is always accurate.

The Starbucks Store Location Dataset is a valuable resource for anyone who wants to learn more about Starbucks or find a Starbucks store near them. It is a great example of how data can be used to improve the customer experience and make businesses more efficient.

Here are some of the specific details about the context and inspiration behind the Starbucks Store Location Dataset:

1. Context: Starbucks is a global coffeehouse company with over 30,000 stores in 80 countries. The company has been expanding rapidly in recent years, and it is important for them to have a comprehensive and up-to-date list of all of their store locations.
2. Inspiration: The inspiration for the Starbucks Store Location Dataset came from the company's desire to make it easier for customers to find Starbucks stores near them. The dataset is also used by businesses and researchers to learn more about Starbucks' global reach.
3. Benefits: The Starbucks Store Location Dataset provides a number of benefits for both Starbucks and its customers. For Starbucks, the dataset helps them to improve the customer experience by making it easier for customers to find stores. For customers, the dataset makes it easier to find Starbucks stores near them, which can save them time and money.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F16731800%2F2b87409e296a59d20dab602e6501f340%2Ffile9e063b84e35.gif?generation=1707771596337465&alt=media" alt="">

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F16731800%2F9d574862156fdd14299b6bcdf1d7c0e8%2Ffile9e048912e2.gif?generation=1707771713059014&alt=media" alt="">

US Funds Dataset: Unlocking Insights for Informed Investment Decisions

Exchange-Traded Funds (ETFs) have gained significant popularity in recent years as a low-cost alternative to Mutual Funds. This dataset, compiled from Yahoo Finance, offers a comprehensive overview of the US funds market, encompassing 23,783 Mutual Funds and 2,310 ETFs.

Data

The dataset provides a wealth of information on each fund, including:

General fund aspects: total net assets, fund family, inception date, expense ratios, and more. Portfolio indicators: cash allocation, sector weightings, holdings diversification, and other key metrics. Historical returns: year-to-date, 1-year, 3-year, and other performance data for different time periods. Financial ratios: price/earnings ratio, Treynor and Sharpe ratios, alpha, beta, and ESG scores. Applications

This dataset can be leveraged by investors, researchers, and financial professionals for a variety of purposes, including:

Investment analysis: comparing the performance and characteristics of Mutual Funds and ETFs to make informed investment decisions. Portfolio construction: using the data to build diversified portfolios that align with investment goals and risk tolerance. Research and analysis: studying market trends, fund behavior, and other factors to gain insights into the US funds market. Inspiration and Updates

The dataset was inspired by the surge of interest in ETFs in 2017 and the subsequent shift away from Mutual Funds. The data is sourced from Yahoo Finance, a publicly available website, ensuring transparency and accessibility. Updates are planned every 1-2 semesters to keep the data current and relevant.

Conclusion

This comprehensive dataset offers a valuable resource for anyone seeking to gain a deeper understanding of the US funds market. By providing detailed information on a wide range of funds, the dataset empowers investors to make informed decisions and build successful investment portfolios.

Access the dataset and unlock the insights it offers to make informed investment decisions.

About this dataset

Context

The dataset presents median household incomes for various household sizes in Money Creek Township, Minnesota, as reported by the U.S. Census Bureau. The dataset highlights the variation in median household income with the size of the family unit, offering valuable insights into economic trends and disparities within different household sizes, aiding in data analysis and decision-making.

Key observations

• Of the 7 household sizes (1 person to 7-or-more person households) reported by the census bureau, Money Creek township did not include 5, 6, or 7-person households. Across the different household sizes in Money Creek township the mean income is $99,257, and the standard deviation is $53,799. The coefficient of variation (CV) is 54.20%. This high CV indicates high relative variability, suggesting that the incomes vary significantly across different sizes of households.
• In the most recent year, 2021, The smallest household size for which the bureau reported a median household income was 1-person households, with an income of $60,594. It then further increased to $177,675 for 4-person households, the largest household size for which the bureau reported a median household income.

https://i.neilsberg.com/ch/money-creek-township-mn-median-household-income-by-household-size.jpeg" alt="Money Creek Township, Minnesota median household income, by household size (in 2022 inflation-adjusted dollars)">

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Household Sizes:

• 1-person households
• 2-person households
• 3-person households
• 4-person households
• 5-person households
• 6-person households
• 7-or-more-person households

Variables / Data Columns

• Household Size: This column showcases 7 household sizes ranging from 1-person households to 7-or-more-person households (As mentioned above).
• Median Household Income: Median household income, in 2022 inflation-adjusted dollars for the specific household size.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Money Creek township median household income. You can refer the same here

About this dataset

Context

The dataset presents median income data over a decade or more for males and females categorized by Total, Full-Time Year-Round (FT), and Part-Time (PT) employment in Money Creek township. It showcases annual income, providing insights into gender-specific income distributions and the disparities between full-time and part-time work. The dataset can be utilized to gain insights into gender-based pay disparity trends and explore the variations in income for male and female individuals.

Key observations: Insights from 2023

Based on our analysis ACS 2019-2023 5-Year Estimates, we present the following observations: - All workers, aged 15 years and older: In Money Creek township, the median income for all workers aged 15 years and older, regardless of work hours, was $42,604 for males and $39,643 for females.

Based on these incomes, we observe a gender gap percentage of approximately 7%, indicating a significant disparity between the median incomes of males and females in Money Creek township. Women, regardless of work hours, still earn 93 cents to each dollar earned by men, highlighting an ongoing gender-based wage gap.

Surprisingly, within the subset of full-time workers, women earn a higher income than men, earning 1.08 dollars for every dollar earned by men. This suggests that within full-time roles, womens median incomes significantly surpass mens, contrary to broader workforce trends.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. All incomes have been adjusting for inflation and are presented in 2023-inflation-adjusted dollars.

Gender classifications include:

• Male
• Female

Employment type classifications include:

• Full-time, year-round: A full-time, year-round worker is a person who worked full time (35 or more hours per week) and 50 or more weeks during the previous calendar year.
• Part-time: A part-time worker is a person who worked less than 35 hours per week during the previous calendar year.

Variables / Data Columns

• Year: This column presents the data year. Expected values are 2010 to 2023
• Male Total Income: Annual median income, for males regardless of work hours
• Male FT Income: Annual median income, for males working full time, year-round
• Male PT Income: Annual median income, for males working part time
• Female Total Income: Annual median income, for females regardless of work hours
• Female FT Income: Annual median income, for females working full time, year-round
• Female PT Income: Annual median income, for females working part time

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Money Creek township median household income by race. You can refer the same here

Significant fluctuations are estimated for all segments over the forecast period for the revenue. In general, the indicator appears to exhibit a positive trend, with more segments showing increasing values rather than decreasing values until 2030. Among them, the segment Food attains the highest value throughout the entire period, reaching 1.23 trillion U.S. dollars.The Statista Market Insights cover a broad range of additional markets.

Problem Statement

Customer Analysis is a detailed analysis of a company’s customers. It helps a business to better understand its customers and makes it easier for them to modify products according to the specific needs, behaviours and concerns of different types of customers. Customer analysis helps a business to modify its product based on its target customers from different types of customer segments. For example, instead of spending money to market a new product to every customer in the company’s database, a company can analyze which customer segment is most likely to buy the product and then market the product only on that particular segment.

Data Dictionary

ID: Customer's unique identifier Year_Birth: Customer's birth year Education: Customer's education level Marital_Status: Customer's marital status Income: Customer's yearly household income Kidhome: Number of children in customer's household Teenhome: Number of teenagers in customer's household Dt_Customer: Date of customer's enrollment with the company Recency: Number of days since customer's last purchase Complain: 1 if the customer complained in the last 2 years, 0 otherwise MntWines: Amount spent on wine in last 2 years MntFruits: Amount spent on fruits in last 2 years MntMeatProducts: Amount spent on meat in last 2 years MntFishProducts: Amount spent on fish in last 2 years MntSweetProducts: Amount spent on sweets in last 2 years MntGoldProds: Amount spent on gold in last 2 years NumDealsPurchases: Number of purchases made with a discount AcceptedCmp1: 1 if customer accepted the offer in the 1st campaign, 0 otherwise AcceptedCmp2: 1 if customer accepted the offer in the 2nd campaign, 0 otherwise AcceptedCmp3: 1 if customer accepted the offer in the 3rd campaign, 0 otherwise AcceptedCmp4: 1 if customer accepted the offer in the 4th campaign, 0 otherwise AcceptedCmp5: 1 if customer accepted the offer in the 5th campaign, 0 otherwise Response: 1 if customer accepted the offer in the last campaign, 0 otherwise NumWebPurchases: Number of purchases made through the company’s website NumCatalogPurchases: Number of purchases made using a catalogue NumStorePurchases: Number of purchases made directly in stores NumWebVisitsMonth: Number of visits to company’s website in the last month

Perform clustering to summarize customer segments.

The San Francisco Controller's Office maintains a database of budgetary data that appears in summarized form in each Annual Appropriation Ordinance (AAO). This data is presented on the Budget report hosted at http://openbook.sfgov.org, and is also available in this dataset in CSV format. New data is added on an annual basis when the AAO is published for each new fiscal year. Data is available from fiscal year 2010 forward. The City and County of San Francisco's budget is a two-year plan for how the City government will spend money with available resources. In the budget process, a budget is proposed by the Mayor, and then modified and approved by the Board of Supervisors as the Appropriation Ordinance. Each year, the City will update the Budget for the upcoming fiscal year and also set a budget for the subsequent fiscal year, which will be updated and approved in the following year. Enterprise departments do not submit a budget for the second year of the two year budget; rather, estimates of enterprise department budgets in the second year of the budget are incorporated into high-level spending and revenue figures. This dataset and the Appropriation Ordinance departmental views answer the question "How much does each department spend?". To show how much is spent by departments from the General Fund we make the following adjustments to the regular revenues and fund balance & reserves: + Transfers from one department to another (leaving out transfers within the same department) + Recoveries from one department to another (leaving out recoveries within the same department) - GF spent in other funds (this is deducted from GF Sources and added to the other fund's Sources) This is the gross total. By removing the transfers and recoveries that go from one department to the another we see the same net total that is in the Appropriation Ordinance Consolidated Schedule of Sources and Uses. Note that the amount added for transfers into the General Fund that move from one department to another is different than the amount deducted to eliminate the double counting caused by transfers. Transfer Adjustments: To meet accounting needs, money can be moved from one fund or department to another. For example, Public Works provides building maintenance services for the Fire Department for which the Fire Department pays Public Works. To solve this double counting problem, this dataset shows a reduction of $100,000 called Transfer Adjustments (Citywide) to the budgeted spending & revenue for the department providing the service. This lets the dataset display both the gross total of activity for both departments and the net total use of City and County revenues. In the example above, the money is moving both between departments, from Fire to Public Works, and between funds, from General Fund Operating to General Fund Works Orders/Overhead. Transfer Adjustments (Citywide): -Transfer Adjustments (Citywide) are used when money is moved from one department to another. These are deducted from the gross total to create the net total. -Transfer Adjustments are included in the gross total when they are within the same department. A separate sub-object is used to distinguish departmental Transfer Adjustments from Transfer Adjustments (Citywide). -Transfer Adjustments (Citywide) may differ from transfer adjustment lines in other public reports as a result of different approaches used to report transfers; however, the net total will remain the same across this dataset, the Mayor's Budget Book, and the Appropriations Ordinance, with limited exceptions due to error corrections and different methodologies used to present net totals. For more information, contact us. An example of a Transfer Adjustment within a department would be Public Works overhead allocations. Overhead costs cannot easily be isolated to a direct service or unit and so are allocated across those units using accepted accounting methods. Central m

The market for contemporary authors’ archives in the United States began when research libraries needed to cheaply provide sources for the swelling number of students and faculty following World War II. Soon, the demand for contemporary authors’ archives developed into a multimillion-dollar trade. Writers and their families enjoyed their new opportunity to make money, as did the book dealers and literary agents with the foresight to pivot their businesses to serve living authors. For a while, library directors and curators across the American Midwest and West relished their new-found opportunity increase their prestige by building collections that could compete on equal footing against British and Ivy League holdings. But as the twentieth century progressed, and public interest around celebrity writers grew more frenzied, even the most well-funded institutions found acquiring contemporary literary archives had become cost prohibitive. Researchers began to question how papers came to be housed in locales disconnected from authors’ professional and personal lives. Placing Papers: The American Literary Archives Market is the first book to chart how the market for writers’ papers became overheated to explore what happens when tourists, rather than scholars, become the designated audience for literary archives.

Jordan number dataset provides millions of powerful contacts for direct marketing. Our List To Data unit carefully gathers these leads from multiple trusted sources. Further, you can get all confirmed contact numbers from our site for any business to communicate with new clients. This Jordan number dataset creates significant opportunities for boosting company sales. Likewise, this Jordan number dataset is highly effective for business promotion through cold calls and text messages. That marketing lead gives instant feedback from the consumers and expands contracts. Despite this, we deliver the number directory to you in CSV or Excel form. In addition, anyone can operate it in any CRM software without any trouble. Jordan phone data is a very helpful contact library for SMS and telemarketing. Besides, the cold-calling database plays a vital role in direct business plans. Even, we prioritize security and strictly adhere to all the GDPR statutes. Most importantly, anyone can purchase this without any doubt from List To Data. In fact, you can make your business more famous by increasing productivity. Moreover, the Jordan phone data helps in many ways to earn more money from this country. This country is very wealthy in all those sectors, so you can accept our data package now. This website is the perfect place to collect all authentic client mobile contact numbers. As such, our skilled team is ready to assist you 24/7 in supplying your necessary leads. Jordan phone number list makes your business more profitable in a couple of months. This country has the nominal GDP (US$53 billion) and the most extensive by purchasing power parity (US$140 trillion). In other words, it creates a big possibility to earn more from here. As such agriculture, services, industry, and trade, are the main sources of income in Jordan. Accordingly, you can get their mobile numbers from us for direct calls or SMS marketing. In addition, this Jordan phone number list is far better for your business activities nationwide. Especially, you can do the marketing with this enormous group of people. Actually, it will increase your deals rapidly and expand the company’s wealth. Definitely, as a businessman, you take your needed sales leads from our website at an affordable cost.

Bitcoin Cash is a cryptocurrency that allows more bytes to be included in each block relative to it’s common ancestor Bitcoin. This dataset contains the blockchain data in their entirety, pre-processed to be human-friendly and to support common use cases such as auditing, investigating, and researching the economic and financial properties of the system. This dataset is part of a larger effort to make cryptocurrency data available in BigQuery through the Google Cloud Public Datasets program . The program is hosting several cryptocurrency datasets, with plans to both expand offerings to include additional cryptocurrencies and reduce the latency of updates. You can find these datasets by searching "cryptocurrency" in GCP Marketplace. For analytics interoperability, we designed a unified schema that allows all Bitcoin-like datasets to share queries. Interested in learning more about how the data from these blockchains were brought into BigQuery? Looking for more ways to analyze the data? Check out the Google Cloud Big Data blog post and try the sample queries below to get started. This public dataset is hosted in Google BigQuery and is included in BigQuery's 1TB/mo of free tier processing. This means that each user receives 1TB of free BigQuery processing every month, which can be used to run queries on this public dataset. Watch this short video to learn how to get started quickly using BigQuery to access public datasets. What is BigQuery .