The total number and percentage of private enterprises owned by men or women, by age group of primary owner and enterprise size.

This table presents data on the distribution of business sizes (micro, small, and medium) across two genders of entrepreneurs (man and woman), using counts and percentages to illustrate the breakdown.Key Insights:Microbusinesses dominate the dataset, accounting for the vast majority of businesses. Women represent 69.2% of this category, while men make up 30.8%.Small businesses are more evenly split, with 54.4% women and 45.6% men.Medium Businesses are male-dominated, with men accounting for 61.5% while women represent 38.5%.The gender distribution across all business sizes shows that women are the majority (67.7%), while men make up 32.3%.

Aha's Nenu Super Woman dataset

With 64 fields/features and 38 rows.

Dataset has below features/columns:

• Season Number - Season number
• Startup Name - Startup company name
• Season Start - Season first aired date
• Season End - Season last aired date
• Episode Number - Episode number within the season
• Anchor - Name of the anchor
• Pitch Number - Overall pitch number
• Industry - Industry name or type
• Business Description - Business Description
• Company Website - Company Website URL
• Entrepreneur Names - Name of the entrepreneurs
• Number of Presenters - Number of presenters
• Pitchers Average Age - All pitchers average age, <30 young, 30-50 middle, >50 old
• Started in - Year in which startup was started/incorporated
• Yearly Revenue - Yearly revenue, in lakhs INR, -1 means negative revenue, 0 means pre-revenue
• Monthly Sales - Total monthly sales, in lakhs
• Gross Margin - Gross margin/profit of company, in percentages
• Net Margin - Net margin/profit of company, in percentages
• SKUs - Stock Keeping Units, at the time of pitch
• Original Ask Amount - Original Ask Amount, in lakhs INR
• Original Offered Equity - Original Offered Equity, in percentages
• Valuation Requested - Valuation Requested, in lakhs INR
• Received Offer - Received offer or not, 1-received, 0-not received
• Accepted Offer - Accepted offer or not, 1-accepted, 0-rejected
• Total Deal Amount - Total Deal Amount, in lakhs INR
• Total Deal Equity - Total Deal Equity, in percentages
• Total Deal Debt - Total Deal Debt, in lakhs INR
• Debt Interest - Debt interest rate, in percentages
• Deal Valuation - Deal Valuation, in lakhs INR
• Number of Angels in deal - Number of sharks involved in deal
• Investment Amount Per Angel - Investment Amount Per Angel
• Equity Per Angel - Equity Per Angel
• Deal has conditions - Deal has conditions or not?
• Has Patents - Pitcher has Patents? yes/no
• Royalty deal - Deal has royalty or not?
• Super Woman Fund - Super Woman fund, in lakhs INR
• Deepa Investment Amount - Deepa Investment Amount, in lakhs INR
• Deepa Investment Equity - Deepa Investment Equity, in percentages
• Deepa Debt Amount - Deepa Debt Amount, in lakhs INR
• Renuka Investment Amount - Renuka Investment Amount, in lakhs INR
• Renuka Investment Equity - Renuka Investment Equity, in percentages
• Renuka Debt Amount - Renuka Debt Amount, in lakhs INR
• Sridhar Investment Amount - Sridhar Investment Amount, in lakhs INR
• Sridhar Investment Equity - Sridhar Investment Equity, in percentages
• Sridhar Debt Amount - Sridhar Debt Amount, in lakhs INR
• Rohit Investment Amount - Rohit Investment Amount, in lakhs INR
• Rohit Investment Equity - Rohit Investment Equity, in percentages
• Rohit Debt Amount - Rohit Debt Amount, in lakhs INR
• Sindhura Investment Amount - Sindhura Investment Amount, in lakhs INR
• Sindhura Investment Equity - Sindhura Investment Equity, in percentages
• Sindhura Debt Amount - Sindhura Debt Amount, in lakhs INR
• Sudhakar Investment Amount - Sudhakar Investment Amount, in lakhs INR
• Sudhakar Investment Equity - Sudhakar Investment Equity, in percentages
• Sudhakar Debt Amount - Sudhakar Debt Amount, in lakhs INR
• Karan Investment Amount - Karan Investment Amount, in lakhs INR
• Karan Investment Equity - Karan Investment Equity, in percentages
• Karan Debt Amount - Karan Debt Amount, in lakhs INR
• Deepa Present - Whether Deepa present in episode or not
• Renuka Present - Whether Renuka present in episode or not
• Sridhar Present - Whether Sridhar present in episode or not
• Rohit Present - Whether Rohit present in episode or not
• Sindhura Present - Whether Sindhura present in episode or not
• Sudhakar Present - Whether Sudhakar present in episode or not
• Karan Present - Whether Karan present in episode or not

Time series data for the statistic Starting a business: Time - Women (days) and country Cameroon. Indicator Definition:The time for women captures the median duration that business incorporation experts indicate is necessary for five female married entrepreneurs to complete all procedures required to start and operate a business with minimum follow-up and no extra payments. It is calulared in calendar days. The time estimates of all procedures are added to calculate the total time required to start and operate a business, taking into account simultaneity of processes. It is assumed that the minimum time required for each procedure is one day, except for procedures that can be fully completed online, for which the time required is recorded as half a day.The indicator "Starting a business: Time - Women (days)" stands at 14.00 as of 12/31/2019. Regarding the One-Year-Change of the series, the current value is equal to the value the year prior.The 1 year change in percent is 0.0.The 3 year change in percent is -17.65.The 5 year change in percent is -17.65.The 10 year change in percent is -61.11.The Serie's long term average value is 27.12. It's latest available value, on 12/31/2019, is 48.37 percent lower, compared to it's long term average value.The Serie's change in percent from it's minimum value, on 12/31/2018, to it's latest available value, on 12/31/2019, is +0.0%.The Serie's change in percent from it's maximum value, on 12/31/2003, to it's latest available value, on 12/31/2019, is -68.89%.

Average percentage of women and men in management positions, by North American Industry Classification System (NAICS), business employment size, type of business, business activity and majority ownership, first quarter of 2025.

Time series data for the statistic Starting a business: Time - Women (days) and country Dominican Republic. Indicator Definition:The time for women captures the median duration that business incorporation experts indicate is necessary for five female married entrepreneurs to complete all procedures required to start and operate a business with minimum follow-up and no extra payments. It is calulared in calendar days. The time estimates of all procedures are added to calculate the total time required to start and operate a business, taking into account simultaneity of processes. It is assumed that the minimum time required for each procedure is one day, except for procedures that can be fully completed online, for which the time required is recorded as half a day.The indicator "Starting a business: Time - Women (days)" stands at 16.50 as of 12/31/2019. Regarding the One-Year-Change of the series, the current value is equal to the value the year prior.The 1 year change in percent is 0.0.The 3 year change in percent is -10.81.The 5 year change in percent is 13.79.The 10 year change in percent is 3.12.The Serie's long term average value is 30.68. It's latest available value, on 12/31/2019, is 46.21 percent lower, compared to it's long term average value.The Serie's change in percent from it's minimum value, on 12/31/2011, to it's latest available value, on 12/31/2019, is +13.79%.The Serie's change in percent from it's maximum value, on 12/31/2003, to it's latest available value, on 12/31/2019, is -79.11%.

Time series data for the statistic Starting a business: Time - Women (days) and country Portugal. Indicator Definition:The time for women captures the median duration that business incorporation experts indicate is necessary for five female married entrepreneurs to complete all procedures required to start and operate a business with minimum follow-up and no extra payments. It is calulared in calendar days. The time estimates of all procedures are added to calculate the total time required to start and operate a business, taking into account simultaneity of processes. It is assumed that the minimum time required for each procedure is one day, except for procedures that can be fully completed online, for which the time required is recorded as half a day.The indicator "Starting a business: Time - Women (days)" stands at 6.50 as of 12/31/2019. Regarding the One-Year-Change of the series, the current value is equal to the value the year prior.The 1 year change in percent is 0.0.The 3 year change in percent is 8.33.The 5 year change in percent is 8.33.The 10 year change in percent is -7.14.The Serie's long term average value is 17.62. It's latest available value, on 12/31/2019, is 63.11 percent lower, compared to it's long term average value.The Serie's change in percent from it's minimum value, on 12/31/2013, to it's latest available value, on 12/31/2019, is +8.33%.The Serie's change in percent from it's maximum value, on 12/31/2003, to it's latest available value, on 12/31/2019, is -91.67%.

Shark Tank India Data set.

Shark Tank India - Season 1 to season 4 information, with 80 fields/columns and 630+ records.

All seasons/episodes of 🦈 SHARKTANK INDIA 🇮🇳 were broadcasted on SonyLiv OTT/Sony TV.

Here is the data dictionary for (Indian) Shark Tank season's dataset.

• Season Number - Season number
• Startup Name - Company name or product name
• Episode Number - Episode number within the season
• Pitch Number - Overall pitch number
• Season Start - Season first aired date
• Season End - Season last aired date
• Original Air Date - Episode original/first aired date, on OTT/TV
• Episode Title - Episode title in SonyLiv
• Anchor - Name of the episode presenter/host
• Industry - Industry name or type
• Business Description - Business Description
• Company Website - Company Website URL
• Started in - Year in which startup was started/incorporated
• Number of Presenters - Number of presenters
• Male Presenters - Number of male presenters
• Female Presenters - Number of female presenters
• Transgender Presenters - Number of transgender/LGBTQ presenters
• Couple Presenters - Are presenters wife/husband ? 1-yes, 0-no
• Pitchers Average Age - All pitchers average age, <30 young, 30-50 middle, >50 old
• Pitchers City - Presenter's town/city or place where company head office exists
• Pitchers State - Indian state pitcher hails from or state where company head office exists
• Yearly Revenue - Yearly revenue, in lakhs INR, -1 means negative revenue, 0 means pre-revenue
• Monthly Sales - Total monthly sales, in lakhs
• Gross Margin - Gross margin/profit of company, in percentages
• Net Margin - Net margin/profit of company, in percentages
• EBITDA - Earnings Before Interest, Taxes, Depreciation, and Amortization
• Cash Burn - In loss in current year; burning/paying money from their pocket (yes/no)
• SKUs - Stock Keeping Units or number of varieties, at the time of pitch
• Has Patents - Pitcher has Patents/Intellectual property (filed/granted), at the time of pitch
• Bootstrapped - Startup is bootstrapped or not (yes/no)
• Part of Match off - Competition between two similar brands, pitched at same time
• Original Ask Amount - Original Ask Amount, in lakhs INR
• Original Offered Equity - Original Offered Equity, in percentages
• Valuation Requested - Valuation Requested, in lakhs INR
• Received Offer - Received offer or not, 1-received, 0-not received
• Accepted Offer - Accepted offer or not, 1-accepted, 0-rejected
• Total Deal Amount - Total Deal Amount, in lakhs INR
• Total Deal Equity - Total Deal Equity, in percentages
• Total Deal Debt - Total Deal debt/loan amount, in lakhs INR
• Debt Interest - Debt interest rate, in percentages
• Deal Valuation - Deal Valuation, in lakhs INR
• Number of sharks in deal - Number of sharks involved in deal
• Deal has conditions - Deal has conditions or not? (yes or no)
• Royalty Percentage - Royalty percentage, if it's royalty deal
• Royalty Recouped Amount - Royalty recouped amount, if it's royalty deal, in lakhs
• Advisory Shares Equity - Deal with Advisory shares or equity, in percentages
• Namita Investment Amount - Namita Investment Amount, in lakhs INR
• Namita Investment Equity - Namita Investment Equity, in percentages
• Namita Debt Amount - Namita Debt Amount, in lakhs INR
• Vineeta Investment Amount - Vineeta Investment Amount, in lakhs INR
• Vineeta Investment Equity - Vineeta Investment Equity, in percentages
• Vineeta Debt Amount - Vineeta Debt Amount, in lakhs INR
• Anupam Investment Amount - Anupam Investment Amount, in lakhs INR
• Anupam Investment Equity - Anupam Investment Equity, in percentages
• Anupam Debt Amount - Anupam Debt Amount, in lakhs INR
• Aman Investment Amount - Aman Investment Amount, in lakhs INR
• Aman Investment Equity - Aman Investment Equity, in percentages
• Aman Debt Amount - Aman Debt Amount, in lakhs INR
• Peyush Investment Amount - Peyush Investment Amount, in lakhs INR
• Peyush Investment Equity - Peyush Investment Equity, in percentages
• Peyush Debt Amount - Peyush Debt Amount, in lakhs INR
• Ritesh Investment Amount - Ritesh Investment Amount, in lakhs INR
• Ritesh Investment Equity - Ritesh Investment Equity, in percentages
• Ritesh Debt Amount - Ritesh Debt Amount, in lakhs INR
• Amit Investment Amount - Amit Investment Amount, in lakhs INR
• Amit Investment Equity - Amit Investment Equity, in percentages
• Amit Debt Amount - Amit Debt Amount, in lakhs INR
• Guest Investment Amount - Guest Investment Amount, in lakhs INR
• Guest Investment Equity - Guest Investment Equity, in percentages
• Guest Debt Amount - Guest Debt Amount, in lakhs INR
• Invested Guest Name - Name of the guest(s) who invested in deal
• All Guest Names - Name of all guests, who are present in episode
• Namita Present - Whether Namita present in episode or not
• Vineeta Present - Whether Vineeta present in episode or not
• Anupam ...

Dataset Summary About this data: MWBE is a federal program administered through each state. Each state individually establishes its own certification program and requirements. In 2018, the City of Rochester set new goals for the use of minority and women owned businesses (MWBEs) on City contracts. The City of Rochester is committed to providing opportunities for MWBE businesses to participate in and become an integral part of the City's procurement process. This table has information on agreements between primary contractors and consultants (primes) and the City of Rochester, as well as the subcontractors used by those primes. This report pulls information on contracts with payments only. Recently entered agreements may be excluded if there have not been any payments to contractors yet. Data Dictionary:ContractNumber: Unique number assigned to the prime contract in the City of Rochester's financial system. ContractTitle: Title of the agreement between the prime contractor and the City of Rochester. ContractValue: Total value of the agreement between the prime contractor and the City of Rochester. DiversityGoal: This is the percentage of the total value of the agreement that the prime contractor intends to award to minority-owned, women-owned or disadvantaged business entities. Whether or not an MWBE or DBE sub-contractor will count towards this calculation is determined by the prime contractors’ selection when entering sub-contractors into the B2GNow system. AssignedDepartment: The City of Rochester department or bureau responsible for managing the project. ContractType: Agreements are grouped into types depending on what the City is purchasing through the contract. Terms are agreements between the City of Rochester and a contractor to provide a product or service for a set amount of time, or term. Construction is for a set project to build, renovate or update City buildings, properties and infrastructure. Professional Services are agreements for services which require special skills, knowledge, training, expertise, or a high degree of creativity. TierSortOrder: B2GNow generated number assigned to sub-contractors on a project. The numbers are assigned starting at 1 in the order the sub-contractors are entered into the system by the prime contractor. VendorType: This indicates if the business is the prime or sub-contractor. Prime: The business who has made an agreement directly with the City of Rochester to complete a project or provide goods and services. Sub-Contractor: Business hired by the prime contractor or consultant to help complete the agreement with the City of Rochester. BusinessName: Name of the company. GoalType: Indicates if the business is certified as a minority or woman owned business or a certified disadvantaged business entity. Businesses may be certified as both minority and women owned businesses. If businesses have dual certification, their participation is counted to either MBE or WBE goals, based on the selection made by the prime contractor. Blank – This business is not certified. DBE – This business is certified as a disadvantaged business entity (DBE) and their agreement will count toward DBE participation goals. The disadvantaged business enterprise program is administered by the federal Department of Transportation. MBE – This business is New York State certified minority-owned business. WBE – This business is New York State certified woman-owned business. ForCredit: Yes or Blank, indicating whether a certified firm will count toward the project’s participation goals. Ethnicity: Indicates ethnicity or race of MWBE and DBE business owners. Gender: Indicates gender of MWBE and DBE business owners. TotalAward: Total value of agreement between either the prime and City of Rochester or the sub-contractor and the prime. AwardShare: This is an adjustment to show the amount of the contract that will be performed by the business less any sub-contracting agreements. It is calculated differently for Primes and Sub-Contractors. For Primes: SubcontractValue = Total Award – Sum of Sub-Contractor Agreement Values. For Sub-contractors: Award Share = Total Award TotalPayment: The total amount paid to date for the agreement. City: City of the primary business address. State: State of the primary business address. ZIP: ZIP code of the primary business address. Source: This information is pulled from B2GNow, the City of Rochester’s platform for tracking prime contractor and prime consultants’ payments to sub-contractors and their use of MWBEs and DBEs on City contracts. The City began using B2GNow for new contracts in 2019. All agreements with MWBE and DBE goals were entered into B2GNow beginning in 2020. All public works consulting contracting with MWBE goals began being entered in 2021. Data from 2019-2020 may not capture the full use of MWBE and DBE contractors. Last Update: June 30, 2022

Number of Women Allocated to Each Treatment by Region.

As of February 2025, it was found that around 14.1 percent of TikTok's global audience were women between the ages of 18 and 24 years, while male users of the same age formed approximately 16.6 percent of the platform's audience. The online audience of the popular social video platform was further composed of 14.6 percent of female users aged between 25 and 34 years, and 20.7 percent of male users in the same age group.

Share of female entrepreneurs by world region.

Frauen in der EU. Themen: am stärksten von der Krise betroffene Bereiche: Lohngefälle zwischen Frauen und Männern und Karriereentwicklung, Arbeit in nicht dem Qualifikationsniveau entsprechenden Berufen, späterer Eintritt junger Hochschulabsolventen ins Berufsleben, Zunahme von Schwarzarbeit und informeller Beschäftigung, Zunahme unsicherer Arbeitsplätze, Zunahme von Teilzeitarbeit, Vereinbarkeit von Privat- und Berufsleben; wichtigste Geschlechterungleichheiten; Geschlechterungleichheiten, die sich durch die Krise am stärksten verschlimmert haben; Aspekte mit größerer Bedeutung bei der Einstellung von Frauen bzw. Männern: Qualifikation, Berufserfahrung, Fremdsprachenkenntnisse, Computerkenntnisse, Anpassungsfähigkeit, Mobilität, Flexibilität in Bezug auf die Arbeitszeiten, Erscheinungsbild, Kinder, Alter, anderes, kein Unterschied; Einschätzung der Effektivität der folgenden Maßnahmen zur Erhöhung des Anteils von Menschen in Arbeit und der Möglichkeit, länger im Arbeitsleben zu verbleiben: Ausbau von Kinderbetreuungseinrichtungen, Verbesserung der Bezahlbarkeit von Kinderbetreuungseinrichtungen, Vereinfachung der Möglichkeit von Arbeiten im Ausland, Unterstützung beim Schritt in die Selbstständigkeit, Förderung regelmäßiger Fortbildungen am Arbeitsplatz; wichtigste Bereiche im Hinblick auf die Krise; in die Wahlprogramme der Kandidaten für die nächsten Europawahlen vorrangig aufzunehmende zu bekämpfende Ungleichheiten: Fortbestehen sexistischer Stereotypen, Lohngefälle zwischen Frauen und Männern, ungleiche Aufgaben- und Verantwortungsverteilung zwischen Frauen und Männern innerhalb der Familie, geringer Frauenanteil in Führungspositionen in Unternehmen, geringer Frauenanteil in Führungspositionen in der Politik, Gewalt gegen Frauen, Frauenhandel und Prostitution, größere Schwierigkeiten für Frauen bei der Vereinbarkeit von Privat- und Berufsleben. Demographie: Alter; Geschlecht; Staatsangehörigkeit; Alter bei Beendigung der Ausbildung; Beruf; berufliche Stellung; Urbanisierungsgrad; Besitz eines Mobiltelefons; Festnetztelefon im Haushalt; Haushaltszusammensetzung und Haushaltsgröße; Region. Zusätzlich verkodet wurde: Befragten-ID; Land; Interviewmodus (Mobiltelefon oder Festnetz); Nationengruppe; Gewichtungsfaktor. Women in the EU. Topics: areas most impacted by the crisis: pay gap between women and men and career development, people working in jobs that do not correspond to their level of qualification, later entrance of young graduates into the job market, increase in informal and undeclared work, increase in insecure work, increase in part-time work, difficulty of reconciling private and working lives; most important gender inequalities; most worsened gender inequalities due to the crisis; aspects of higher importance with regard to employers recruiting a woman compared to a man and vice versa: level of qualifications, professional experience, language skills, computer skills, ability to adapt, ability to be mobile, flexibility in terms of working hours, physical appearance, children, age, other, no difference; assessment of the effectiveness of each of the following measures to get more people into work or to enable them to stay in work until later in life: increase the availability of childcare facilities, increase the affordability of childcare facilities, make it easier for people to work abroad, support people who want to start their own business, promote regular training for people at work; most important areas with regard to the crisis; preferred inequality to be tackled as a priority in the electoral programmes of the candidates for the next European elections: persistence of sexist stereotypes, pay gap between women and men, unequal sharing of responsibilities and tasks in families, small proportion of women in positions of responsibility in companies, small proportion of women in positions of responsibility in politics, violence against women, trafficking in women and prostitution, stronger difficulties for women in reconciling their private and working lives. Demography: age; sex; nationality; age at end of education; occupation; professional position; type of community; own a mobile phone and fixed (landline) phone; household composition and household size; region. Additionally coded was: respondent ID; country; type of phone line; nation group; weighting factor.

As of January 2024, Instagram was slightly more popular with men than women, with men accounting for 50.6 percent of the platform’s global users. Additionally, the social media app was most popular amongst younger audiences, with almost 32 percent of users aged between 18 and 24 years.

              Instagram’s Global Audience

              As of January 2024, Instagram was the fourth most popular social media platform globally, reaching two billion monthly active users (MAU). This number is projected to keep growing with no signs of slowing down, which is not a surprise as the global online social penetration rate across all regions is constantly increasing.
              As of January 2024, the country with the largest Instagram audience was India with 362.9 million users, followed by the United States with 169.7 million users.

              Who is winning over the generations?

              Even though Instagram’s audience is almost twice the size of TikTok’s on a global scale, TikTok has shown itself to be a fierce competitor, particularly amongst younger audiences. TikTok was the most downloaded mobile app globally in 2022, generating 672 million downloads. As of 2022, Generation Z in the United States spent more time on TikTok than on Instagram monthly.

Notes: Huber-White Standard error in Parentheses. *, **, and *** indicate significance at the 10, 5 and 1 percent levels respectively.Coefficients are from OLS regressions after controlling for marketplace dummies.P-values are for test of equality of treatment effect across subgroups.Impact of Treatment Type on Attendance for Different Subgroups.

Notes: p-value for test of equality of means controls for randomization at the market level.n.a. denotes not applicable since there is no variation in this variable within markets.Verification of Randomization for Individual Characteristics Table.

Instagram’s most popular post

              As of April 2024, the most popular post on Instagram was Lionel Messi and his teammates after winning the 2022 FIFA World Cup with Argentina, posted by the account @leomessi. Messi's post, which racked up over 61 million likes within a day, knocked off the reigning post, which was 'Photo of an Egg'. Originally posted in January 2021, 'Photo of an Egg' surpassed the world’s most popular Instagram post at that time, which was a photo by Kylie Jenner’s daughter totaling 18 million likes.
              After several cryptic posts published by the account, World Record Egg revealed itself to be a part of a mental health campaign aimed at the pressures of social media use.

              Instagram’s most popular accounts

              As of April 2024, the official Instagram account @instagram had the most followers of any account on the platform, with 672 million followers. Portuguese footballer Cristiano Ronaldo (@cristiano) was the most followed individual with 628 million followers, while Selena Gomez (@selenagomez) was the most followed woman on the platform with 429 million. Additionally, Inter Miami CF striker Lionel Messi (@leomessi) had a total of 502 million. Celebrities such as The Rock, Kylie Jenner, and Ariana Grande all had over 380 million followers each.

              Instagram influencers

              In the United States, the leading content category of Instagram influencers was lifestyle, with 15.25 percent of influencers creating lifestyle content in 2021. Music ranked in second place with 10.96 percent, followed by family with 8.24 percent. Having a large audience can be very lucrative: Instagram influencers in the United States, Canada and the United Kingdom with over 90,000 followers made around 1,221 US dollars per post.

              Instagram around the globe

              Instagram’s worldwide popularity continues to grow, and India is the leading country in terms of number of users, with over 362.9 million users as of January 2024. The United States had 169.65 million Instagram users and Brazil had 134.6 million users. The social media platform was also very popular in Indonesia and Turkey, with 100.9 and 57.1, respectively. As of January 2024, Instagram was the fourth most popular social network in the world, behind Facebook, YouTube and WhatsApp.

Student Performance & Behavior Dataset

This dataset is real data of 5,000 records collected from a private learning provider. The dataset includes key attributes necessary for exploring patterns, correlations, and insights related to academic performance.

Columns: 01. Student_ID: Unique identifier for each student. 02. First_Name: Student’s first name. 03. Last_Name: Student’s last name. 04. Email: Contact email (can be anonymized). 05. Gender: Male, Female, Other. 06. Age: The age of the student. 07. Department: Student's department (e.g., CS, Engineering, Business). 08. Attendance (%): Attendance percentage (0-100%). 09. Midterm_Score: Midterm exam score (out of 100). 10. Final_Score: Final exam score (out of 100). 11. Assignments_Avg: Average score of all assignments (out of 100). 12. Quizzes_Avg: Average quiz scores (out of 100). 13. Participation_Score: Score based on class participation (0-10). 14. Projects_Score: Project evaluation score (out of 100). 15. Total_Score: Weighted sum of all grades. 16. Grade: Letter grade (A, B, C, D, F). 17. Study_Hours_per_Week: Average study hours per week. 18. Extracurricular_Activities: Whether the student participates in extracurriculars (Yes/No). 19. Internet_Access_at_Home: Does the student have access to the internet at home? (Yes/No). 20. Parent_Education_Level: Highest education level of parents (None, High School, Bachelor's, Master's, PhD). 21. Family_Income_Level: Low, Medium, High. 22. Stress_Level (1-10): Self-reported stress level (1: Low, 10: High). 23. Sleep_Hours_per_Night: Average hours of sleep per night.

The Attendance is not part of the Total_Score or has very minimal weight.

Calculating the weighted sum: Total Score=a⋅Midterm+b⋅Final+c⋅Assignments+d⋅Quizzes+e⋅Participation+f⋅Projects

Dataset contains: - Missing values (nulls): in some records (e.g., Attendance, Assignments, or Parent Education Level). - Bias in some Datae (ex: grading e.g., students with high attendance get slightly better grades). - Imbalanced distributions (e.g., some departments having more students).

Note: - The dataset is real, but I included some bias to create a greater challenge for my students. - Some Columns have been masked as the Data owner requested. "Students_Grading_Dataset_Biased.csv" contains the biased Dataset "Students Performance Dataset" Contains the masked dataset

The number of Instagram users in the United Kingdom was forecast to continuously increase between 2024 and 2028 by in total 2.1 million users (+7.02 percent). After the ninth consecutive increasing year, the Instagram user base is estimated to reach 32 million users and therefore a new peak in 2028. Notably, the number of Instagram users of was continuously increasing over the past years.User figures, shown here with regards to the platform instagram, have been estimated by taking into account company filings or press material, secondary research, app downloads and traffic data. They refer to the average monthly active users over the period and count multiple accounts by persons only once.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).

Introduction

The U.S. job market, with its dynamic trends and fluctuating unemployment rates, serves as an important barometer for the nation's economic health. All rates provided in this dataset are seasonally adjusted. Delving into the intricacies of unemployment rates by age and gender helps researchers, policymakers, and analysts uncover underlying patterns and address potential disparities.

Usage Examples

• Economic Research: Study the historical unemployment trends to gauge economic cycles.
• Policy Making: Inform labor market policies and interventions based on age or gender disparities.
• Business Strategy: Companies can analyze job market conditions when considering expansions or contractions.
• Academic Projects: Students and educators can use the dataset for case studies, dissertations, or classroom projects.

Image Source Photo by Ron Lach : https://www.pexels.com/photo/woman-looking-for-jobs-in-newspaper-9832700/

Dataset Contents

This dataset, sourced from the FRED API, provides: - df_sex_unemployment_rates.csv: A breakdown of U.S. unemployment rates based on gender. - df_unemployment_rates.csv: Unemployment rates categorized by various age groups, ranging from young entrants (ages 16-17) to seasoned professionals (55 and above).

Together, these data files offer a comprehensive insight into the nuances of unemployment in the U.S., highlighting potential disparities in the job market across different age groups and between men and women.