This document discusses different types of variables and methods for summarizing data through graphs and numerical summaries. It covers categorical and quantitative variables, and how to represent them through frequency tables, pie charts, bar graphs, dot plots, stem-and-leaf plots and histograms. It also discusses measures of central tendency including the mean, median and mode, and how the mean and median can differ based on a distribution's shape. Outliers are also introduced.
The document is a presentation on machine learning and simple linear regression. It introduces the concepts of a regression model, fitting a linear regression line to data by minimizing the residual sum of squares, and using the fitted line to make predictions. It discusses representing the linear regression model as an equation relating the output variable (y) to the input or feature (x), with parameters (w0, w1) estimated from training data. The parameters can be estimated by taking the gradient of the residual sum of squares and setting it equal to zero to find the optimal values for w0 and w1 that best fit the data.
1) The document discusses evidence for evolution including fossil records, homology, biogeography, and genetics. Fossil records show extinction, origins of new groups, and changes over time.
2) Homology provides evidence of common ancestry through similar structures in different species. Biogeography, such as Darwin's observations of island species, also supports evolution.
3) Darwin developed the theory of evolution by natural selection, which proposes that genetic variations arise by mutation and populations evolve through natural selection of inheritable traits that increase survival and reproduction.
This document covers organizing and summarizing data. It discusses qualitative and quantitative variables and different types of data like discrete and continuous. It explains how to create frequency distributions and relative frequency distributions to organize qualitative and quantitative data. Different charts like pie charts, bar charts and histograms are introduced as visual ways to represent categorized or grouped data. Procedures and examples are provided for creating these distributions and charts. Key terms involved in grouping quantitative data using limits or cutpoints are also defined.
1. The document discusses different methods for summarizing and visualizing data, including frequency distributions, histograms, and other statistical graphs.
2. Key points include how to construct a frequency distribution and calculate measures like class width and frequency.
3. Different types of graphs are explained like histograms, dot plots, bar graphs, and more, noting how each can be used to understand patterns in the data.
4. Guidelines are provided for determining what makes a good or bad graph, such as using accurate scales and not distorting the data.
Introduction to biostatistics. This lecture was given as a part of the Introduction to Epidemiology & Community Medicine Course given for third-year medical students.
This chapter discusses descriptive statistics including organizing and graphing qualitative and quantitative data, measures of central tendency, and measures of dispersion. It covers frequency distributions, histograms, polygons, measures of central tendency (mean, median, mode), measures of dispersion (range, variance, standard deviation), skewness, and cumulative frequency distributions. The objectives are to describe and interpret graphical displays of data, compute various statistical measures, and identify shapes of distributions.
This document summarizes a seminar on data and graphs. It discusses various topics related to data collection and representation, including primary and secondary data, methods of data collection, frequency distribution tables, different types of graphs such as bar diagrams, histograms, pie charts and frequency curves. The key points covered are methods of collecting primary data, ways to prepare frequency distribution tables, and different types of bar diagrams, histograms and frequency curves used to represent data visually.
Multiple Linear Regression Applications in Real Estate Pricinginventionjournals
In this paper, we attempt to predict the price of a real estate individual homes sold in North West Indiana based on the individual homes sold in 2014. The data/information is collected from realtor.com. The purpose of this paper is to predict the price of individual homes sold based on multiple regression model and also utilize SAS forecasting model and software. We also determine the factors influencing housing prices and to what extent they affect the price. Independent variables such square footage, number of bathrooms, and whether there is a finished basement,. and whether there is brick front or not and the type of home: Colonial, Cotemporary or Tudor. How much does each type of home (Colonial, Contemporary, Tudor) add to the price of the real estate
The document is a presentation on machine learning and simple linear regression. It introduces the concepts of a regression model, fitting a linear regression line to data by minimizing the residual sum of squares, and using the fitted line to make predictions. It discusses representing the linear regression model as an equation relating the output variable (y) to the input or feature (x), with parameters (w0, w1) estimated from training data. The parameters can be estimated by taking the gradient of the residual sum of squares and setting it equal to zero to find the optimal values for w0 and w1 that best fit the data.
1) The document discusses evidence for evolution including fossil records, homology, biogeography, and genetics. Fossil records show extinction, origins of new groups, and changes over time.
2) Homology provides evidence of common ancestry through similar structures in different species. Biogeography, such as Darwin's observations of island species, also supports evolution.
3) Darwin developed the theory of evolution by natural selection, which proposes that genetic variations arise by mutation and populations evolve through natural selection of inheritable traits that increase survival and reproduction.
This document covers organizing and summarizing data. It discusses qualitative and quantitative variables and different types of data like discrete and continuous. It explains how to create frequency distributions and relative frequency distributions to organize qualitative and quantitative data. Different charts like pie charts, bar charts and histograms are introduced as visual ways to represent categorized or grouped data. Procedures and examples are provided for creating these distributions and charts. Key terms involved in grouping quantitative data using limits or cutpoints are also defined.
1. The document discusses different methods for summarizing and visualizing data, including frequency distributions, histograms, and other statistical graphs.
2. Key points include how to construct a frequency distribution and calculate measures like class width and frequency.
3. Different types of graphs are explained like histograms, dot plots, bar graphs, and more, noting how each can be used to understand patterns in the data.
4. Guidelines are provided for determining what makes a good or bad graph, such as using accurate scales and not distorting the data.
Introduction to biostatistics. This lecture was given as a part of the Introduction to Epidemiology & Community Medicine Course given for third-year medical students.
This chapter discusses descriptive statistics including organizing and graphing qualitative and quantitative data, measures of central tendency, and measures of dispersion. It covers frequency distributions, histograms, polygons, measures of central tendency (mean, median, mode), measures of dispersion (range, variance, standard deviation), skewness, and cumulative frequency distributions. The objectives are to describe and interpret graphical displays of data, compute various statistical measures, and identify shapes of distributions.
This document summarizes a seminar on data and graphs. It discusses various topics related to data collection and representation, including primary and secondary data, methods of data collection, frequency distribution tables, different types of graphs such as bar diagrams, histograms, pie charts and frequency curves. The key points covered are methods of collecting primary data, ways to prepare frequency distribution tables, and different types of bar diagrams, histograms and frequency curves used to represent data visually.
Multiple Linear Regression Applications in Real Estate Pricinginventionjournals
In this paper, we attempt to predict the price of a real estate individual homes sold in North West Indiana based on the individual homes sold in 2014. The data/information is collected from realtor.com. The purpose of this paper is to predict the price of individual homes sold based on multiple regression model and also utilize SAS forecasting model and software. We also determine the factors influencing housing prices and to what extent they affect the price. Independent variables such square footage, number of bathrooms, and whether there is a finished basement,. and whether there is brick front or not and the type of home: Colonial, Cotemporary or Tudor. How much does each type of home (Colonial, Contemporary, Tudor) add to the price of the real estate
Multiple Linear Regression Applications in Real Estate Pricinginventionjournals
This document describes using multiple linear regression to predict real estate prices. House price data from 480 homes sold in Indiana in 2014 is used. Independent variables like size, number of bedrooms/bathrooms, and whether there is a basement are considered. Correlations between variables are examined. An initial regression model is developed using all potential predictors. The best fitting model is found to use only homeowner association (HOA) fees as a predictor, with the equation Price=312638+17.854Hoa.
The document discusses various methods for summarizing and visualizing data, including frequency distributions, histograms, and other statistical graphs. It provides definitions and examples of key concepts such as frequency tables, class boundaries, histograms, and scatter plots. Guidelines are given for properly constructing graphs and avoiding misleading visualizations.
This document discusses summarizing and graphing data. It covers frequency distributions, histograms, and other statistical graphics. Frequency distributions organize large data sets into categories with frequencies. Histograms are a graphical representation of a frequency distribution, using bars to show frequencies for categories along the horizontal axis. Other topics include relative frequency distributions, cumulative frequencies, and interpreting these graphs to understand properties of the underlying data such as its shape and outliers.
This document discusses exponential and logistic modeling. Exponential functions can model unrestricted growth, while logistic functions model restricted growth like disease spread. Constant percentage rate and exponential population models are presented. Examples show determining growth rates from functions, modeling bacteria growth, and using regression to model US population growth exponentially over time. Logistic modeling is also discussed through an example of modeling rumor spread through a school.
This document discusses various numerical descriptive techniques used for summarizing and describing quantitative data, including:
- Measures of central location (mean, median, mode) and how to calculate them
- Measures of variability (range, variance, standard deviation) and how they are used to quantify the dispersion of data around the mean
- Other concepts like percentiles, the empirical rule, Chebyshev's theorem, and box plots. Examples are provided to illustrate how to apply these techniques to sample data sets.
This document discusses frequency distributions and methods for exploring distribution shape. It covers stem-and-leaf plots, histograms, frequency tables, and additional charts. Frequency distributions describe how often values occur in a data set. Distribution shape is characterized by symmetry, modality, skewness, and kurtosis. Common graphical methods to examine shape include stem-and-leaf plots, histograms, frequency polygons, bar charts, and pie charts. Frequency tables list frequencies, relative frequencies, and cumulative frequencies of data grouped in class intervals.
The document provides an introduction to panel data analysis. It defines time series data, cross-sectional data, and panel data, which combines the two. Panel data has advantages over single time series or cross-sectional data like more observations, capturing heterogeneity and dynamics. Panel data can be balanced or unbalanced, and micro or macro. The document demonstrates structuring panel data in Excel for empirical analysis in Eviews, including an activity to arrange time series data into a panel data format.
This document provides step-by-step examples for determining a line of best fit from a scatter plot and using the line of best fit to make predictions. It explains how to construct a scatter plot, draw a line that best represents the data, write the equation in slope-intercept form, and use the equation to predict values. The examples illustrate how to find the slope and y-intercept, write the line of best fit equation, and make conjectures for data points not explicitly in the original data set.
Descriptive statistics are used to summarize and describe data through tables, graphs, and numerical measures. Key methods include frequency distributions and histograms to describe categorical variables, measures of center such as the mean and median, measures of variability such as range and standard deviation, and bivariate descriptions to analyze relationships between two variables using scatterplots, contingency tables, correlations, and regression. The goal is to concisely portray patterns in the data through graphical and numerical summaries.
Forecasting Stock Market using Multiple Linear Regressionijtsrd
This document discusses using multiple linear regression to predict stock market prices based on interest rates and unemployment rates. It presents sample data and uses the statistical software SPSS and Python to conduct a multiple linear regression analysis. The analysis finds that interest rates and unemployment rates significantly influence stock market prices, with rates explaining 90% of price variance. The regression output is used to generate an equation to forecast stock prices based on interest and unemployment rate values.
MSC III_Research Methodology and Statistics_Descriptive statistics.pdfSuchita Rawat
This document discusses key concepts in research methodology and statistics. It defines statistics as dealing with the collection, analysis, and interpretation of quantitative and qualitative data. It then discusses various types of graphs used to visually represent data, such as bar graphs, pie charts, histograms, boxplots, and scatterplots. It also defines common measures of central tendency (mean, median, mode), dispersion (range, variance, standard deviation, IQR), and skewness.
This document discusses various methods of presenting statistical data, including tabulation, graphs, and diagrams. It describes frequency distribution tables, histograms, frequency polygons, frequency curves, cumulative frequency diagrams, line charts, scatter diagrams, bar diagrams, pie charts, pictograms, and map diagrams. The key methods are:
1. Tabulation involves organizing data into frequency distribution tables to group observations.
2. Graphs such as histograms, frequency polygons, and frequency curves can be used to present quantitative continuous data visually.
3. Diagrams including bar diagrams, pie charts, and pictograms present qualitative discrete data. Map diagrams show geographic distributions.
This chapter discusses descriptive statistics and numerical measures used to describe data. It will cover computing and interpreting the mean, median, mode, range, variance, standard deviation, and coefficient of variation. It also explains how to apply the empirical rule and calculate a weighted mean. Additionally, it discusses how a least squares regression line can estimate linear relationships between two variables. The goals are to be able to compute and understand these common descriptive statistics and measures of central tendency, variation, and shape of data distributions.
This document discusses descriptive statistics which are used to describe characteristics of a sample dataset. It covers topics such as frequency distributions, measures of central tendency, measures of dispersion, the normal curve, z-scores, sampling error, confidence intervals, and degrees of freedom. Descriptive statistics are used to initially describe variables in quantitative research and for descriptive research purposes.
Modeling Social Data, Lecture 6: Regression, Part 1jakehofman
This document discusses regression analysis as presented by Jake Hofman of Columbia University. It defines regression as understanding how a response variable varies across subgroups based on predictor variables. The goals of regression are to describe outcomes under different conditions, predict future outcomes, and explain associations between predictors and outcomes. Examples shown include comparing SAT score distributions between ethnic groups and examining the relationship between SAT scores and household income. The framework for regression involves specifying the outcome and predictors, defining a loss function, fitting the model to minimize loss, and assessing performance.
Application of panel data to the effect of five (5) world development indicat...Alexander Decker
This document discusses applying a panel data model to analyze the effect of 5 world development indicators (WDI) on GDP per capita for 20 African Union countries from 1981 to 2011. It introduces panel data modeling and the fixed effects model specifically. The fixed effects model is estimated using least squares dummy variable regression to account for country-specific effects. The results of analyzing the relationship between GDP per capita and the 5 WDI (exchange rate, money supply, inflation, natural resources, foreign investment) using this fixed effects panel data model are then presented.
Application of panel data to the effect of five (5) world development indicat...Alexander Decker
This document discusses the application of panel data analysis to examine the effect of 5 world development indicators (WDI) on GDP per capita for 20 African Union countries from 1981 to 2011. It presents the panel data model, describes the methodology used as fixed effects regression, and provides sample output of the panel data format and regression results. The key world development indicators examined are official exchange rate, broad money, inflation rate, total natural resources rents, and foreign direct investment.
This document provides an overview of statistics and probability as taught in a lecture. It begins by defining statistics as the science of drawing conclusions about phenomena from sample data. Some key points:
- Statistics has many applications across various disciplines.
- The course will cover descriptive statistics, probability, and inferential statistics over 15 lectures.
- Students will complete homework assignments and take midterm and final exams to be graded on their understanding.
- The goal is for students to learn statistical techniques to make data-driven decisions in their fields of study.
Enhanced Screen Flows UI/UX using SLDS with Tom KittPeter Caitens
Join us for an engaging session led by Flow Champion, Tom Kitt. This session will dive into a technique of enhancing the user interfaces and user experiences within Screen Flows using the Salesforce Lightning Design System (SLDS). This technique uses Native functionality, with No Apex Code, No Custom Components and No Managed Packages required.
Multiple Linear Regression Applications in Real Estate Pricinginventionjournals
This document describes using multiple linear regression to predict real estate prices. House price data from 480 homes sold in Indiana in 2014 is used. Independent variables like size, number of bedrooms/bathrooms, and whether there is a basement are considered. Correlations between variables are examined. An initial regression model is developed using all potential predictors. The best fitting model is found to use only homeowner association (HOA) fees as a predictor, with the equation Price=312638+17.854Hoa.
The document discusses various methods for summarizing and visualizing data, including frequency distributions, histograms, and other statistical graphs. It provides definitions and examples of key concepts such as frequency tables, class boundaries, histograms, and scatter plots. Guidelines are given for properly constructing graphs and avoiding misleading visualizations.
This document discusses summarizing and graphing data. It covers frequency distributions, histograms, and other statistical graphics. Frequency distributions organize large data sets into categories with frequencies. Histograms are a graphical representation of a frequency distribution, using bars to show frequencies for categories along the horizontal axis. Other topics include relative frequency distributions, cumulative frequencies, and interpreting these graphs to understand properties of the underlying data such as its shape and outliers.
This document discusses exponential and logistic modeling. Exponential functions can model unrestricted growth, while logistic functions model restricted growth like disease spread. Constant percentage rate and exponential population models are presented. Examples show determining growth rates from functions, modeling bacteria growth, and using regression to model US population growth exponentially over time. Logistic modeling is also discussed through an example of modeling rumor spread through a school.
This document discusses various numerical descriptive techniques used for summarizing and describing quantitative data, including:
- Measures of central location (mean, median, mode) and how to calculate them
- Measures of variability (range, variance, standard deviation) and how they are used to quantify the dispersion of data around the mean
- Other concepts like percentiles, the empirical rule, Chebyshev's theorem, and box plots. Examples are provided to illustrate how to apply these techniques to sample data sets.
This document discusses frequency distributions and methods for exploring distribution shape. It covers stem-and-leaf plots, histograms, frequency tables, and additional charts. Frequency distributions describe how often values occur in a data set. Distribution shape is characterized by symmetry, modality, skewness, and kurtosis. Common graphical methods to examine shape include stem-and-leaf plots, histograms, frequency polygons, bar charts, and pie charts. Frequency tables list frequencies, relative frequencies, and cumulative frequencies of data grouped in class intervals.
The document provides an introduction to panel data analysis. It defines time series data, cross-sectional data, and panel data, which combines the two. Panel data has advantages over single time series or cross-sectional data like more observations, capturing heterogeneity and dynamics. Panel data can be balanced or unbalanced, and micro or macro. The document demonstrates structuring panel data in Excel for empirical analysis in Eviews, including an activity to arrange time series data into a panel data format.
This document provides step-by-step examples for determining a line of best fit from a scatter plot and using the line of best fit to make predictions. It explains how to construct a scatter plot, draw a line that best represents the data, write the equation in slope-intercept form, and use the equation to predict values. The examples illustrate how to find the slope and y-intercept, write the line of best fit equation, and make conjectures for data points not explicitly in the original data set.
Descriptive statistics are used to summarize and describe data through tables, graphs, and numerical measures. Key methods include frequency distributions and histograms to describe categorical variables, measures of center such as the mean and median, measures of variability such as range and standard deviation, and bivariate descriptions to analyze relationships between two variables using scatterplots, contingency tables, correlations, and regression. The goal is to concisely portray patterns in the data through graphical and numerical summaries.
Forecasting Stock Market using Multiple Linear Regressionijtsrd
This document discusses using multiple linear regression to predict stock market prices based on interest rates and unemployment rates. It presents sample data and uses the statistical software SPSS and Python to conduct a multiple linear regression analysis. The analysis finds that interest rates and unemployment rates significantly influence stock market prices, with rates explaining 90% of price variance. The regression output is used to generate an equation to forecast stock prices based on interest and unemployment rate values.
MSC III_Research Methodology and Statistics_Descriptive statistics.pdfSuchita Rawat
This document discusses key concepts in research methodology and statistics. It defines statistics as dealing with the collection, analysis, and interpretation of quantitative and qualitative data. It then discusses various types of graphs used to visually represent data, such as bar graphs, pie charts, histograms, boxplots, and scatterplots. It also defines common measures of central tendency (mean, median, mode), dispersion (range, variance, standard deviation, IQR), and skewness.
This document discusses various methods of presenting statistical data, including tabulation, graphs, and diagrams. It describes frequency distribution tables, histograms, frequency polygons, frequency curves, cumulative frequency diagrams, line charts, scatter diagrams, bar diagrams, pie charts, pictograms, and map diagrams. The key methods are:
1. Tabulation involves organizing data into frequency distribution tables to group observations.
2. Graphs such as histograms, frequency polygons, and frequency curves can be used to present quantitative continuous data visually.
3. Diagrams including bar diagrams, pie charts, and pictograms present qualitative discrete data. Map diagrams show geographic distributions.
This chapter discusses descriptive statistics and numerical measures used to describe data. It will cover computing and interpreting the mean, median, mode, range, variance, standard deviation, and coefficient of variation. It also explains how to apply the empirical rule and calculate a weighted mean. Additionally, it discusses how a least squares regression line can estimate linear relationships between two variables. The goals are to be able to compute and understand these common descriptive statistics and measures of central tendency, variation, and shape of data distributions.
This document discusses descriptive statistics which are used to describe characteristics of a sample dataset. It covers topics such as frequency distributions, measures of central tendency, measures of dispersion, the normal curve, z-scores, sampling error, confidence intervals, and degrees of freedom. Descriptive statistics are used to initially describe variables in quantitative research and for descriptive research purposes.
Modeling Social Data, Lecture 6: Regression, Part 1jakehofman
This document discusses regression analysis as presented by Jake Hofman of Columbia University. It defines regression as understanding how a response variable varies across subgroups based on predictor variables. The goals of regression are to describe outcomes under different conditions, predict future outcomes, and explain associations between predictors and outcomes. Examples shown include comparing SAT score distributions between ethnic groups and examining the relationship between SAT scores and household income. The framework for regression involves specifying the outcome and predictors, defining a loss function, fitting the model to minimize loss, and assessing performance.
Application of panel data to the effect of five (5) world development indicat...Alexander Decker
This document discusses applying a panel data model to analyze the effect of 5 world development indicators (WDI) on GDP per capita for 20 African Union countries from 1981 to 2011. It introduces panel data modeling and the fixed effects model specifically. The fixed effects model is estimated using least squares dummy variable regression to account for country-specific effects. The results of analyzing the relationship between GDP per capita and the 5 WDI (exchange rate, money supply, inflation, natural resources, foreign investment) using this fixed effects panel data model are then presented.
Application of panel data to the effect of five (5) world development indicat...Alexander Decker
This document discusses the application of panel data analysis to examine the effect of 5 world development indicators (WDI) on GDP per capita for 20 African Union countries from 1981 to 2011. It presents the panel data model, describes the methodology used as fixed effects regression, and provides sample output of the panel data format and regression results. The key world development indicators examined are official exchange rate, broad money, inflation rate, total natural resources rents, and foreign direct investment.
This document provides an overview of statistics and probability as taught in a lecture. It begins by defining statistics as the science of drawing conclusions about phenomena from sample data. Some key points:
- Statistics has many applications across various disciplines.
- The course will cover descriptive statistics, probability, and inferential statistics over 15 lectures.
- Students will complete homework assignments and take midterm and final exams to be graded on their understanding.
- The goal is for students to learn statistical techniques to make data-driven decisions in their fields of study.
Enhanced Screen Flows UI/UX using SLDS with Tom KittPeter Caitens
Join us for an engaging session led by Flow Champion, Tom Kitt. This session will dive into a technique of enhancing the user interfaces and user experiences within Screen Flows using the Salesforce Lightning Design System (SLDS). This technique uses Native functionality, with No Apex Code, No Custom Components and No Managed Packages required.
A neural network is a machine learning program, or model, that makes decisions in a manner similar to the human brain, by using processes that mimic the way biological neurons work together to identify phenomena, weigh options and arrive at conclusions.
How Can Hiring A Mobile App Development Company Help Your Business Grow?ToXSL Technologies
ToXSL Technologies is an award-winning Mobile App Development Company in Dubai that helps businesses reshape their digital possibilities with custom app services. As a top app development company in Dubai, we offer highly engaging iOS & Android app solutions. https://rb.gy/necdnt
Unveiling the Advantages of Agile Software Development.pdfbrainerhub1
Learn about Agile Software Development's advantages. Simplify your workflow to spur quicker innovation. Jump right in! We have also discussed the advantages.
Using Query Store in Azure PostgreSQL to Understand Query PerformanceGrant Fritchey
Microsoft has added an excellent new extension in PostgreSQL on their Azure Platform. This session, presented at Posette 2024, covers what Query Store is and the types of information you can get out of it.
Malibou Pitch Deck For Its €3M Seed Roundsjcobrien
French start-up Malibou raised a €3 million Seed Round to develop its payroll and human resources
management platform for VSEs and SMEs. The financing round was led by investors Breega, Y Combinator, and FCVC.
Photoshop Tutorial for Beginners (2024 Edition)alowpalsadig
Photoshop Tutorial for Beginners (2024 Edition)
Explore the evolution of programming and software development and design in 2024. Discover emerging trends shaping the future of coding in our insightful analysis."
Here's an overview:Introduction: The Evolution of Programming and Software DevelopmentThe Rise of Artificial Intelligence and Machine Learning in CodingAdopting Low-Code and No-Code PlatformsQuantum Computing: Entering the Software Development MainstreamIntegration of DevOps with Machine Learning: MLOpsAdvancements in Cybersecurity PracticesThe Growth of Edge ComputingEmerging Programming Languages and FrameworksSoftware Development Ethics and AI RegulationSustainability in Software EngineeringThe Future Workforce: Remote and Distributed TeamsConclusion: Adapting to the Changing Software Development LandscapeIntroduction: The Evolution of Programming and Software Development
Photoshop Tutorial for Beginners (2024 Edition)Explore the evolution of programming and software development and design in 2024. Discover emerging trends shaping the future of coding in our insightful analysis."Here's an overview:Introduction: The Evolution of Programming and Software DevelopmentThe Rise of Artificial Intelligence and Machine Learning in CodingAdopting Low-Code and No-Code PlatformsQuantum Computing: Entering the Software Development MainstreamIntegration of DevOps with Machine Learning: MLOpsAdvancements in Cybersecurity PracticesThe Growth of Edge ComputingEmerging Programming Languages and FrameworksSoftware Development Ethics and AI RegulationSustainability in Software EngineeringThe Future Workforce: Remote and Distributed TeamsConclusion: Adapting to the Changing Software Development LandscapeIntroduction: The Evolution of Programming and Software Development
The importance of developing and designing programming in 2024
Programming design and development represents a vital step in keeping pace with technological advancements and meeting ever-changing market needs. This course is intended for anyone who wants to understand the fundamental importance of software development and design, whether you are a beginner or a professional seeking to update your knowledge.
Course objectives:
1. **Learn about the basics of software development:
- Understanding software development processes and tools.
- Identify the role of programmers and designers in software projects.
2. Understanding the software design process:
- Learn about the principles of good software design.
- Discussing common design patterns such as Object-Oriented Design.
3. The importance of user experience (UX) in modern software:
- Explore how user experience can improve software acceptance and usability.
- Tools and techniques to analyze and improve user experience.
4. Increase efficiency and productivity through modern development tools:
- Access to the latest programming tools and languages used in the industry.
- Study live examples of applications
Transforming Product Development using OnePlan To Boost Efficiency and Innova...OnePlan Solutions
Ready to overcome challenges and drive innovation in your organization? Join us in our upcoming webinar where we discuss how to combat resource limitations, scope creep, and the difficulties of aligning your projects with strategic goals. Discover how OnePlan can revolutionize your product development processes, helping your team to innovate faster, manage resources more effectively, and deliver exceptional results.
Manyata Tech Park Bangalore_ Infrastructure, Facilities and Morenarinav14
Located in the bustling city of Bangalore, Manyata Tech Park stands as one of India’s largest and most prominent tech parks, playing a pivotal role in shaping the city’s reputation as the Silicon Valley of India. Established to cater to the burgeoning IT and technology sectors
8 Best Automated Android App Testing Tool and Framework in 2024.pdfkalichargn70th171
Regarding mobile operating systems, two major players dominate our thoughts: Android and iPhone. With Android leading the market, software development companies are focused on delivering apps compatible with this OS. Ensuring an app's functionality across various Android devices, OS versions, and hardware specifications is critical, making Android app testing essential.
Odoo releases a new update every year. The latest version, Odoo 17, came out in October 2023. It brought many improvements to the user interface and user experience, along with new features in modules like accounting, marketing, manufacturing, websites, and more.
The Odoo 17 update has been a hot topic among startups, mid-sized businesses, large enterprises, and Odoo developers aiming to grow their businesses. Since it is now already the first quarter of 2024, you must have a clear idea of what Odoo 17 entails and what it can offer your business if you are still not aware of it.
This blog covers the features and functionalities. Explore the entire blog and get in touch with expert Odoo ERP consultants to leverage Odoo 17 and its features for your business too.
An Overview of Odoo ERP
Odoo ERP was first released as OpenERP software in February 2005. It is a suite of business applications used for ERP, CRM, eCommerce, websites, and project management. Ten years ago, the Odoo Enterprise edition was launched to help fund the Odoo Community version.
When you compare Odoo Community and Enterprise, the Enterprise edition offers exclusive features like mobile app access, Odoo Studio customisation, Odoo hosting, and unlimited functional support.
Today, Odoo is a well-known name used by companies of all sizes across various industries, including manufacturing, retail, accounting, marketing, healthcare, IT consulting, and R&D.
The latest version, Odoo 17, has been available since October 2023. Key highlights of this update include:
Enhanced user experience with improvements to the command bar, faster backend page loading, and multiple dashboard views.
Instant report generation, credit limit alerts for sales and invoices, separate OCR settings for invoice creation, and an auto-complete feature for forms in the accounting module.
Improved image handling and global attribute changes for mailing lists in email marketing.
A default auto-signature option and a refuse-to-sign option in HR modules.
Options to divide and merge manufacturing orders, track the status of manufacturing orders, and more in the MRP module.
Dark mode in Odoo 17.
Now that the Odoo 17 announcement is official, let’s look at what’s new in Odoo 17!
What is Odoo ERP 17?
Odoo 17 is the latest version of one of the world’s leading open-source enterprise ERPs. This version has come up with significant improvements explained here in this blog. Also, this new version aims to introduce features that enhance time-saving, efficiency, and productivity for users across various organisations.
Odoo 17, released at the Odoo Experience 2023, brought notable improvements to the user interface and added new functionalities with enhancements in performance, accessibility, data analysis, and management, further expanding its reach in the market.
DevOps Consulting Company | Hire DevOps Servicesseospiralmantra
Spiral Mantra excels in providing comprehensive DevOps services, including Azure and AWS DevOps solutions. As a top DevOps consulting company, we offer controlled services, cloud DevOps, and expert consulting nationwide, including Houston and New York. Our skilled DevOps engineers ensure seamless integration and optimized operations for your business. Choose Spiral Mantra for superior DevOps services.
https://www.spiralmantra.com/devops/
Alluxio Webinar | 10x Faster Trino Queries on Your Data PlatformAlluxio, Inc.
Alluxio Webinar
June. 18, 2024
For more Alluxio Events: https://www.alluxio.io/events/
Speaker:
- Jianjian Xie (Staff Software Engineer, Alluxio)
As Trino users increasingly rely on cloud object storage for retrieving data, speed and cloud cost have become major challenges. The separation of compute and storage creates latency challenges when querying datasets; scanning data between storage and compute tiers becomes I/O bound. On the other hand, cloud API costs related to GET/LIST operations and cross-region data transfer add up quickly.
The newly introduced Trino file system cache by Alluxio aims to overcome the above challenges. In this session, Jianjian will dive into Trino data caching strategies, the latest test results, and discuss the multi-level caching architecture. This architecture makes Trino 10x faster for data lakes of any scale, from GB to EB.
What you will learn:
- Challenges relating to the speed and costs of running Trino in the cloud
- The new Trino file system cache feature overview, including the latest development status and test results
- A multi-level cache framework for maximized speed, including Trino file system cache and Alluxio distributed cache
- Real-world cases, including a large online payment firm and a top ridesharing company
- The future roadmap of Trino file system cache and Trino-Alluxio integration