Le marché mondial des solutions d'analyse de données cliniques est évalué à 5.01 milliards USD en 2024 et devrait croître de 8.48 milliards USD d'ici 2034 à un TCAC de 6.8 % de 2025 à 2034.

Analysis of ‘Unemployment and mental illness survey’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/michaelacorley/unemployment-and-mental-illness-survey on 28 January 2022.

--- Dataset description provided by original source is as follows ---

Context

This is a paid research survey to explore the linkage between mental illness and unemployment. NAMI has conducted multiple surveys verifying the high unemployment rate among those with mental illness, but this is the only survey to date which targets causation (why they are unemployed). Statistical significance of the variance has long since been proven by previous, larger samples.

You are free to visualize and publish results, please just credit me by name.

Collection methodology

I received several messages about methodology of collection because various people would like to use this data for papers.

• I paid respondents on Survey Monkey in a general population sampling. I did not target any specific demographic as not to get skewed results. Survey Monkey stratifies the sample according to certain characteristics like income and location.

• I know that the general population sampling went well because the number of people self identifying as having a mental illness is consistent with larger samples.

• Although we disqualified people without a mental illness, they were still given the complete survey. That means that the data contains sampling of people with and without mental illness and a yes/no indicator.

Potential area's to investigate

• Linkage between unemployment and education level
• The effect of a gap on your resume on income level
• Symptom/side effects impact on employment
• The effectiveness of social welfare programs
• The linkage between gaps in your resume and hospitalizations due to mental illness

Content

***Sample size:** n = 334; 80 w/ mental illness - this proportion is approximately equal to estimates of the general population diagnosed with mental illness (typically estimated at 20-25% according to various studies).*

Questions:

I identify as having a mental illness Response
Education  Response
I have my own computer separate from a smart phone Response
I have been hospitalized before for my mental illness  Response
How many days were you hospitalized for your mental illness Open-Ended Response
I am currently employed at least part-time Response
I am legally disabled  Response
I have my regular access to the internet  Response
I live with my parents Response
I have a gap in my resume  Response
Total length of any gaps in my resume in months.  Open-Ended Response
Annual income (including any social welfare programs) in USD  Open-Ended Response
I am unemployed Response
I read outside of work and school  Response
Annual income from social welfare programs Open-Ended Response
I receive food stamps  Response
I am on section 8 housing  Response
How many times were you hospitalized for your mental illness  Open-Ended Response

I have one of the following issues in addition to my illness:
  Lack of concentration
  Anxiety
  Depression
  Obsessive thinking
  Mood swings
  Panic attacks
  Compulsive behavior
  Tiredness

Age Response
Gender Response
Household Income  Response
Region Response
Device Type Response

Important data transformation note

When comparing the actual rate to government statistics, it is important to take into account the labor force participation rate (the % of people who are legally considered to be in the workforce). People not included in the unemployment statistic, like discouraged workers (for example the mentally ill) will be "not participating" in the workforce.

Other studies

1. Nami: https://www.nami.org/Press-Media/Press-Releases/2014/Mental-Illness-NAMI-Report-Deplores-80-Percent-Une

2. NIH: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4182106/

--- Original source retains full ownership of the source dataset ---

[Rapport de plus de 228 pages] La taille du marché mondial de l'analyse des données du réseau intelligent devrait passer de 5.39 milliards USD en 2022 à 13.26 milliards USD d'ici 2030, à un TCAC de 11.78 % de 2023 à 2030

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.

CV Financial Building Momentum? (CVBF)

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 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.

SIM Grupo Simec S.A.B. de C.V. American Depositary Shares

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

[228+ Seiten Bericht] Der globale Markt für Smart Grid-Datenanalysen wird voraussichtlich von 5.39 Milliarden USD im Jahr 2022 auf 13.26 Milliarden USD im Jahr 2030 wachsen, bei einer CAGR von 11.78 % von 2023-2030

[Rapport de plus de 212 pages] La taille du marché mondial du traitement du langage naturel devrait passer de 4,251.50 13,277 millions USD à 2028 20.90 millions USD d'ici 2022, à un TCAC de 2028 % de XNUMX à XNUMX

La taille et la part de marché sont classées selon Deployment Type (On-Premise, Cloud-Based) and Application (Reporting and Visualization, Data Mining, Predictive Analytics, Statistical Analysis, Big Data Analytics) and End-User (BFSI, Healthcare, Retail, Manufacturing, Telecommunications) and régions géographiques (Amérique du Nord, Europe, Asie-Pacifique, Amérique du Sud, Moyen-Orient et Afrique).

La taille du marché mondial de la sécurité des Big Data devrait passer de 22.84 milliards USD en 2023 à 28.99 milliards USD en 2032, à un TCAC de 12.10 % entre 2024 et 2032.

La taille du marché mondial des logiciels de confidentialité des données devrait passer de 2.92 milliards de dollars en 2023 à 99.14 milliards de dollars en 2032, à un TCAC de 42.30 % entre 2024 et 2032

La taille du marché mondial de l'analyse client devrait passer de 12.76 milliards USD en 2023 à 78.67 milliards USD en 2032, à un TCAC de 22.40 % entre 2024 et 2032.

Die Marktgröße und der Anteil sind kategorisiert nach Market Research Services (Industry Analysis, Consumer Insights, Competitive Analysis, Market Forecasting, Brand Research) and Data Analytics (Predictive Analytics, Descriptive Analytics, Prescriptive Analytics, Data Mining, Statistical Analysis) and Consulting Services (Strategic Consulting, Operational Consulting, Financial Consulting, Marketing Consulting, Technology Consulting) and geografischen Regionen (Nordamerika, Europa, Asien-Pazifik, Südamerika, Naher Osten & Afrika)

Die Marktgröße und der Anteil sind kategorisiert nach Data Collection Tools (Surveys, Interviews, Focus Groups, Observational Research, Online Analytics) and Data Analysis Tools (Statistical Analysis Software, Predictive Analytics, Data Visualization Tools, Text Analytics, Sentiment Analysis) and Reporting Tools (Dashboard Software, Business Intelligence Platforms, Reporting Automation Tools, Data Storytelling Tools, Visualization Software) and geografischen Regionen (Nordamerika, Europa, Asien-Pazifik, Südamerika, Naher Osten & Afrika)