M1 - Discussion

3/29/24, 8:58 AM Topic: M1: Discussion https://learn.maricopa.edu/courses/1329851/discussion_topics/7550052 1/14 This is a graded discussion: 50 points possible due Mar 29 M1: Discussion 10 Press ALT + F8 to see a list of keyboard shortcuts  Reply  Attach  Data Analytics - Fundamentals Discussion Instructions: Write a discussion post, minimum of 400 words, that answers the following key questions: 1. What types of analytics are government and companies using and how? 2. What technologies are required for data analytics to work? 3. What is the data analytics toolbox and what does it contain? (be sure to reference the textbook when providing your response to this question) 4. What are some of the core programming approaches and libraries that can be used to perform analysis of data sets (Please reference the key reading article - Siddiqui, Alkadri & Khan, 2017). To earn maximum points for this discussion, you need to include the following: (30 points for initial post, 20 points for response post) At least three total scholarly references (including one reference from the chapter reading), you may read other related articles that you find on your own. At least one response, minimum of 200 words, to a peer's posting, with points made that drive the original discussion forward. Rubric: 30 points for a 400 word or more initial discussion post. 20 points for a 200 word or more response post. Posts that fail to meet the minimum word requirement will result in a 50% point penalty. Grammatical and/or spelling errors will have up to a 50% point penalty, depending on the severity. Missing scholarly reference section from the initial post will result in a 10 point reduction. Search entries or author Unread    Subscribe  Subscribed Cancel Post Reply

3/29/24, 8:58 AM Topic: M1: Discussion https://learn.maricopa.edu/courses/1329851/discussion_topics/7550052 2/14 Luisa Sikalu (https://learn.maricopa.edu/courses/1329851/users/3885007) Saturday There are three types of analytics businesses and companies uses , which are Descriptive , Predictive and Presc  There are three types of analytics businesses and companies uses , which are Descriptive , Predictive and Prescriptive Analytics. Descriptive analytics is the analyzing and summarizing historical data to provide intelligence on what has happened in the past. Predictive Analytics is the use of historical data to to predict what is about to happen in the future. Prescriptive analytics takes descriptive and predictive to the next level by suggesting what actions to perform to achieve an outcome. Descriptive analytics uses KPI - Key Performance Indicators tool or also known as Management Cockpits. It is a measurable metrics unit used to evaluate the performance of the company employees and department. Descriptive analytic also uses subscriptions tools, whereas the users can sign up to have reports and dashboards pushed to their emails on a periodic schedule. Predictive analytics uses more sophisticated techniques tool like statistics and AI machine learning and extrapolation techniques. For example - Diagnostic tools, Business Alert tools, Anamoly Detection, and Propensity Models tools. Prescriptive analytics uses more assertive and direct tools, it tells users what to do. For example - Systematic Optimization, Recommendation Systems and Autonomous Agents tools The Data Analytic Toolbox encompasses a wide range of software, programming languages and statistical methods that are used to extract meaningful information from data. For example - Microsoft Excel Spreadsheet, Business Intelligence, Low code analytics, Code-Based analytics There are a lot of great analytical tools for data and they are categorized in three groups, Programming languages, statistical solutions and visualization knowledge. One of the famous data analyzing language which data scientist uses to focus on their research is PYTHON. Python as a lot of useful library which makes it the preferred choice for developing analytical algorithms and exploring hidden facts in the data. Python has emerged as a popular programming language for data analysis due to its simplicity, versatility and a rich ecosystems of libraries and frameworks. R language is another widely used open-source programming language for statistics and data science. It is designed to do any kind of statistical computation by using functional based syntax or program based code with very powerful debugging facilities. References Siddiqui, Tamanna, et al. “Review of Programming Languages and Tools for Big Data Analytics.” View of Review of Programming Languages and Tools for Big Data Analytics , www.ijarcs.info/index.php/Ijarcs/article/view/3578/3488. Accessed 23 Mar. 2024. Santo, Chris, (2024, March 22), Module 1 PowerPoint presentation: Fundamental of Data Analytic and Programming. [ Understanding Data Analytics]. Accessed 23 Mar. 2024.

3/29/24, 8:58 AM Topic: M1: Discussion https://learn.maricopa.edu/courses/1329851/discussion_topics/7550052 4/14 Micah Galvan (https://learn.maricopa.edu/courses/1329851/users/3941514) Monday The types of analytics governments and companies use are Descriptive, Predictive, and Prescriptive analytics. A  1. The types of analytics governments and companies use are Descriptive, Predictive, and Prescriptive analytics. According to page 2 of the textbook, Descriptive analytics is used by methodologies that concentrate on presenting historical data to make it readable and applicable to business needs. Using summary statistics (such as average, median, and variance) and straightforward transformations and aggregations (such as indices, counts, and sums), they respond to the general question "What happened?" at the end by presenting the data in tables and other visual aids. The solo report is the quintessential (and most fundamental) deliverable in the descriptive analytics camp. It can take the form of a file in a portable format (the most common formats are Excel spreadsheets and PDF documents), which is regularly sent by email or uploaded to a shared repository. On page 3, predictive analytics aims to answer the curious questions that naturally arise after discovering past events, such as "Why did it happen?" and "What will happen now?" These approaches go beyond simply recounting historical events using more advanced methods like artificial intelligence. We can interpret the causal links underlying our data through its use and conclude what the future will likely hold. On page 4, Prescriptive analytics responds to all business managers' most critical question: "What should be done?" This allows data to be transformed into a suggested course of action. Prescriptive analytics is undoubtedly more direct and assertive than descriptive and predictive analytics, giving us instructions on what to do while providing insights and information about our business. 2. Physical Infrastructure, Data Platform, & Applications are the data analytics technologies required to do work. On page 11, it is possible to touch physical infrastructure. Mainframe computers and servers that store and process data make up this system. Organizations have two options: they can either develop and manage their physical infrastructure, typically housed in corporate data centers, or they can use cloud providers, renting out the resources they need. A data platform organizes the data logically inside the infrastructure using the available processing power and data architecture. At the platform level, data becomes practically merged on a more straightforward and aesthetically pleasing perspective, even stored in multiple databases. Applications are how user-facing apps are developed using data analytics techniques. Applications harness the capabilities of the underlying platform and the organized data to serve users in various ways. While some systems (such as business intelligence) offer user interfaces for users to explore data, interpret it, and find insights, others (such as advanced analytics) allow more experienced users to go beyond the data and create forecasts or recommendations. 3. The data analytics toolbox is the most essential GIS task that may be carried out with a substantial collection of tools. It contains Spreadsheets, Business Intelligence, Low-Code, and Code-Base Analytics. On page 13, spreadsheets are almost universally used due to their enhanced portability and ease of use, which make it easier to share data with peers. Nearly anyone can enter a Microsoft Excel file and add basic formula calculations, as can anyone using OpenOffice Calc, an open-source competitor, or a cloud-based program like Google Sheets. Because of their high degree of graphic flexibility, they can also help develop straightforward, one-off data visualizations necessary for daily data presenting requirements. The best tools for creating i t ti d hb d d hi ti t d d t i li ti b i i t lli t Y

3/29/24, 8:58 AM Topic: M1: Discussion https://learn.maricopa.edu/courses/1329851/discussion_topics/7550052 5/14 Edited by Micah Galvan (https://learn.maricopa.edu/courses/1329851/users/3941514) on Mar 25 at 10:44am interactive dashboards and sophisticated data visualizations are business intelligence systems. You can formulate user-friendly data apps to democratize data and make it available to the public using tools such as TIBCO Spotfire, Tableau, QlikView/Qlik Sense, Microsoft Power BI, and others. Are the best tools for making interactive dashboards and complex data visualizations? You can develop user-friendly data apps to democratize data and make it available to the public using tools such as TIBCO Spotfire, Tableau, QlikView/Qlik Sense, Microsoft Power BI, and others. On page 14, Low-code analytics lets you quickly create powerful analytics workflows without writing code. Their "secret" is their workflow-based user interface, which allows you to quickly construct a fully functional analytics application by creating a flow chart with incremental data transformation steps and modifiable modeling modules. Using data science-friendly languages like Python, R, and Scala, code-based analytics involves writing code. A data scientist can utilize the many machine learning libraries developed in these languages to create highly personalized and effective analytics solutions. Once integrated, these can be scaled throughout the organization as required for real-time applications. 4. The core programming approaches and libraries that can be used to perform analysis of data sets are Python & R language. According to the article on page 2, Python is a well-known language for data analysis that data scientists utilize extensively in their studies. This language is the best option for creating analytical algorithms and discovering the hidden facts in the data because of its high-level interactive features and scientific ecosystem library. R language is an open-source, incredibly flexible computer language for data research and statistics. Most data scientists in big data-related industries like government, business, and industry use the R environment and packages. An extensive collection of functions and packages for data analysis jobs, with some available out of the box and the remainder as open-source. References: Santo, Chris, (2024, March 22), Module 1 PowerPoint presentation: Fundamental of Data Analytic and Programming. [ Understanding Data Analytics]. Accessed 23 Mar. 2024. Siddiqui, Tamanna, et al. “Review of Programming Languages and Tools for Big Data Analytics.” View of Review of Programming Languages and Tools for Big Data Analytics , www.ijarcs.info/index.php/Ijarcs/article/view/3578/3488. Accessed 23 Mar. 2024. De Mauro, A., Marzoni, F., & Walter, A. J. (n.d.). Data Analytics Made Easy: Analyze and present data to make informed decisions without writing any code (1st ed.). Packt Publishing Ltd.

3/29/24, 8:58 AM Topic: M1: Discussion https://learn.maricopa.edu/courses/1329851/discussion_topics/7550052 7/14 Keynner Blas Villalva (https://learn.maricopa.edu/courses/1329851/users/3294562) Wednesday Data Analytics - Fundamentals Discussion 1. There are 3 types of data analytics: descriptive, predictive, and pre…  Data Analytics - Fundamentals Discussion 1. There are 3 types of data analytics: descriptive, predictive, and prescriptive analytics. Descriptive analytics focuses on describing or summarizing historical data to gain insights into what has happened in the past. Companies use it to look at a snapshot of what has already occurred. As stated in the book Data Analytics Made Easy in Chapter 1 - “They answer the generic question ‘what happened?’ by leveraging summary statistics (like average, median, and variance) and simple transformations and aggregations (like indices, counts, and sums), ultimately displaying the results through tables and visuals.” Companies can benefit from this type of analytics by understanding past performances, identifying trends, making informed decisions, and monitoring Key Performance Indicators (KPIs.) The way companies can deliver this is by standalone reports (PDFs documents or Excel worksheets), and interactive dashboards (web-based interface from which they are guided through their data of interest.) Predictive analytics focuses on forecasting future outcomes based on historical data and statistical algorithms. It gathers past data and from there makes predictions of what might happen in the future. For example, in the book Data Analytics Made Easy in Chapter 1 - “Predictive analytics focuses on answering the natural follow-up questions that you have after learning what happened in the past, such as: why did it happen? And what will happen now? “Companies use predictive analytics to forecast future trends, predict customer behavior, detect fraud, and risk assessments, optimize operations, and diagnose medical and healthcare. Prescriptive analytics compared to descriptive and predictive analytics is the most advanced form of data analysis. It focuses on providing specific recommendations or actions to optimize outcomes. As mentioned in Chapter 1 of book Data Analytics Made Easy - “prescriptive analytics transforms data into a recommended course of action, by answering the ultimate question every business manager has: what should be done?” Companies utilize this form of analytics by simulating a wide range of alternative scenarios and employing systematic optimization methods. Another method used is by performing recommendation systems (providing users with recommendations on products.) 2. The technologies that are required for data analytics to work are organized into three layers which are stacked upon each other and have been giving the definition of Technology Stack. Each layer relies on the one below, going from bottom to top: the lowest layer is the Physical Infrastructure, the middle layer in the Data Platform, and the top layer is made of Applications. The physical infrastructure layer refers to the underlying hardware components such as servers, storage devices, networking equipment, and data centers. The data platform layer consists of technologies and tools used to manage, store, process, and analyze data within an organization. The Applications layer is where data analytics techniques are incorporated into user-facing software. 3. Data analytics toolbox is a collection of tools used by data analysts to manipulate, analyze, visualize, and interpret data. As the book Data Analytics Made Easy in Chapter 1 states - “By learning how to use and how to effectively combine the few tools we have put in the toolbox, we can become autonomous data analytics practitioners.” Some tools that the book mentions as being qualified to be added to our toolbox are spreadsheets,

3/29/24, 8:58 AM Topic: M1: Discussion https://learn.maricopa.edu/courses/1329851/discussion_topics/7550052 8/14 Edited by Keynner Blas Villalva (https://learn.maricopa.edu/courses/1329851/users/3294562) on Mar 27 at 7:09pm business Intelligence, low-code analytics, and code-based analytics. Some examples of spreadsheets mentioned by the book are excel and google sheets. For Business Intelligence there are tools such as Microsoft Power BI and Tableau. A few examples of low-code analytics are KNIME and RapidMiner. And for code-based analytics there are IDEs such as Visual Studio and PyCharm. 4. According to the key reading article, some of the core programming approaches that can be used to perform analysis of data sets are Python, and R. For Python some libraries that the article mentions are: numpy, panda and matplotlib. Numpy provides support for large, multi-dimensional arrays and matrices. Panda provides data structures like DataFrames and Series, as well as functions for cleaning, transforming, and analyzing data. Matplotlib offers a wide range of plotting functions to visualize data distributions, relationships, and trends. According to article Top 15 R Libraries for Data Science in 2024 by Geeksforgeeks some of the best R libraries are: dplyr, ggplot2, and shiny. Dplyr stands out as a highly favored data manipulation toolkit within R. It boasts five key functions that seamlessly integrate with the group_by() function, facilitating group-wise data manipulation operations. Ggplot2 enables the creation of various visualizations like bar charts, pie charts, histograms, scatterplots, error charts, and more by utilizing a high-level API. Shiny combines the power of R with contemporary web technology, allowing users to effortlessly develop web applications without requiring specialized web development expertise. References: GeeksforGeeks. (2024, March 7). Top 15 R Libraries for Data Science in 2024. GeeksforGeeks. https://www.geeksforgeeks.org/r-libraries-for-data-science/ (https://www.geeksforgeeks.org/r-libraries- for-data-science/) de Mauro, A. (2021). Data Analytics Made Easy: Analyze and present data to make informed decisions without writing any code (Chapter 1). Packt Publishing. Siddiqui, T., Alkadri, M., & Khan, N. A. (2017). Review of Programming Languages and Tools for Big Data Analytics. International Journal of Advanced Research in Computer Science, 8(5). https://www.ijarcs.info/index.php/Ijarcs/article/view/3578/3488 (https://www.ijarcs.info/index.php/Ijarcs/article/view/3578/3488)

3/29/24, 8:58 AM Topic: M1: Discussion https://learn.maricopa.edu/courses/1329851/discussion_topics/7550052 10/14 Anay Bayles (https://learn.maricopa.edu/courses/1329851/users/4505915) Wednesday Governments and corporations are using descriptive, predictive, and prescriptive analytics. Governments, for ex…  Governments and corporations are using descriptive, predictive, and prescriptive analytics. Governments, for example, use descriptive analytics in public services such as transportation, education, and healthcare to aim to improve and optimize where resources are needed. In the healthcare field, Nkwanyana et al. (2023) said analytics is helping to identify import trends in diagnosis, come up with treatment plans, and continue care to improve the quality of clinical care that is provided to patients. Corporations similarly use descriptive analytics to watch sales, operations, and customer trends. With predictive analytics, governments can use forecasting to determine public service needs. Corporations can use predictive analytics to determine when demand will increase or decrease. For prescriptive analytics, governments can propose effective public policies and corporations can optimize their supply chains or promote operation efficiency. For data analytics to work effectively, businesses need big data cloud platforms to store and process their large amount of structured and unstructured data such as Apache Hadoop or SciDB. Businesses also need data warehousing solutions, like Google BigQuery, to manage and store their structured data that allows them to combine data from different sources to use for analysis. Business intelligence tools including Tableau and Power BI are also essential because these tools provide interactive dashboards and visualizations. For predictive and prescriptive analytics, having machine learning frameworks like Scikit Learn to develop and deploy machine learning models. The data analytics toolbox contains a set of tools and techniques such as spreadsheets, business intelligence, low- code analytics, and code-based analytics. The toolbox refers to the tools and techniques users use to collect, process, analyze, and visualize data. The textbook by De Mauro (2021) said knowing how to combine different tools and applications is how we mold data into actual business value. The article by Siddiqui et al. (2017) highlights the different kinds of tools used to perform analysis of data sets and which are more popular than others. Python is one of the most highly used programming languages with data scientists due to its wide array of libraries such as pandas, NumPy, Cython, and so forth. SQL is also essential for use when querying and managing structured data in relational databases. References De Mauro, A. (2021). Data Analytics Made Easy: Analyze and present data to make informed decisions without iti d P kt P bli hi

3/29/24, 8:58 AM Topic: M1: Discussion https://learn.maricopa.edu/courses/1329851/discussion_topics/7550052 11/14 Luisa Sikalu (https://learn.maricopa.edu/courses/1329851/users/3885007) Yesterday Hello Anay Thank you for sharing your thoughts and more ideas about Data Analytic. I totally agree with … writing any code . Packt Publishing. Nkwanyana, A., Mathews, V., Zachary, I., & Bhayani, V. (2023). Skills and competencies in health data analytics for health professionals: a scoping review protocol. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10668260/ Siddiqui, T., Alkadri, M., & Khan, N. (2017). Review of Programming Languages and Tools for Big Data Analytics.  Hello Anay Thank you for sharing your thoughts and more ideas about Data Analytic. I totally agree with you. The field of data analytics is supported by a myriad of tools and technologies designed to facilitate data collection, storage and processing and analysis of data and information. Data visualization tools such as Tableau, Power BI etc, enable data analyst to create interactive and insightful visual representations of data, making it easier to communicate findings and solutions. Data analytic has played a crucial role in improving the efficiency and transparency of government operations. From analyzing data related to public spending, procurement and performance, government can use these data analytic tools to identify areas for cost saving, detect instances of fraud and corruption and many more business related issues. Data analytics also has the potential to transform the way the government engage with people. Through the use of data- driven insights, policymakers can tailor public services to better meet the needs of diverse populations, personalize communication strategies, and solicit feedback to inform decision-making. This not only enhances the overall citizen experience but also promotes a more inclusive and responsive government. In the business world, data analytics has emerged as a game-changer, providing organizations with the tools to gain a competitive edge, drive innovation, and optimize performance. From marketing and sales to operations and finance, data analytics permeates every facet of modern business, enabling companies to make data-driven decisions that lead to improved outcomes. Work cite Data analytics: Leveraging data analytics for improved customer engagement . FasterCapital. (n.d.). https://fastercapital.com/content/Data-analytics--Leveraging-Data-Analytics-for-Improved- Customer-Engagement.html (https://fastercapital.com/content/Data-analytics--Leveraging-Data- Analytics-for-Improved-Customer-Engagement.html) Alam, M. (2024, February 22). What is data-driven decision making in government? definition, implementation, improvement, engagement, challenges, and considerations . IdeaScale. https://ideascale.com/blog/what-is-data-driven-decision-making-in-government/ (https://ideascale.com/blog/what-is-data-driven-decision-making-in-government/)

3/29/24, 8:58 AM Topic: M1: Discussion https://learn.maricopa.edu/courses/1329851/discussion_topics/7550052 13/14 Paul Russu (https://learn.maricopa.edu/courses/1329851/users/2266327) Yesterday Data analytics as a field of science has been around for a period of twenty some years, it is comprised o  Data analytics as a field of science has been around for a period of twenty some years, it is comprised of three main branches: descriptive, predictive, and prescriptive. The collection, manipulation, use of different mathematical and computing tools, and extrapolation of data can lead to the prediction of possible events or scenarios. Companies in the various sectors of private industry and the government are using data analytics to study and describe data to improve, enhance, and predict the performance of various social services and business- related activities for their theoretical best outcome. Government agencies, for example, collect data on drug addiction as a nation-wide social problem and attempt to find means and ways to treat addiction, predict and lead to favorable consequences of recovery for people struggling with its various symptoms. Data analytics is used in many other areas of social interest besides healthcare, such as crime control, conservation of resources such as energy resources for example, national spending and allocation of military assets to their best use and design. Predictive analytics helps government agencies to stop or put an end to situations with potential negative outcomes from occurring to begin with, according to (Shaw, 2020). The medical field has seen the use of predictive analytics with the use by patients of devices (such for example blood glucose meters for diabetics that collect blood glucose levels data) that transmit data to the medical provider’s office. The entertainment industry regularly collects data on viewers favorite movies, for example, and uses prescriptive analytic techniques to recommend shows based on the viewers perceived preferences, as a simple example. The field of transportation makes use of predictive analytics analysis to determine for example the best routes to take depending on traffic and weather conditions. The technologies required for data analytics include an actual physical infrastructure made up of servers, the room they are in, connection equipment, etc. Following this is the data platform which can be made up of several layers whose purpose is collect and sort through substantial amounts of data. Applications are finally the outermost top level of all the technologies required to perform data analytics procedures successfully. The data analytics toolbox is the set of applications at the top layer of the data analytics technologies that converts data into viable business results. It covers a complete set of functions: spreadsheets, Business Intelligence tools to enable descriptive analytics, low-code analytics tools that do not require coding, and code- based analytics that use programming languages such as Python that work much easier with data, according to (De Mauro, 2021). Some of the key programming approaches that are used to perform analysis of data sets are: Python, the most well-known of data analysis languages, R language, it is faster than Python in its operation, and SAS software and its language. Some of the Python libraries are: Numpy, Pandas, Matplotlib, IPython, SciPy, Cython according to (Siddiqui, Alkadri, Khan, 2017).

3/29/24, 8:58 AM Topic: M1: Discussion https://learn.maricopa.edu/courses/1329851/discussion_topics/7550052 14/14 Edited by Paul Russu (https://learn.maricopa.edu/courses/1329851/users/2266327) on Mar 28 at 10:26pm  Reply  Attach To conclude, data analytics is a fast-growing field, spurred on its growth by the increasingly developing hi- tech computing industry. It is predicted to grow almost nine times in its global market value within about ten years or so; its current market value is roughly seven billion US dollars according to (Raksha, 2024). Data analytics is most definitely an exciting new technological field of development. References: De Mauro, A. (2021, August). Data Analytics Made Easy. Packt Publishing Ltd. Shaw, G. (2020, November 13). FedTech Magazine. Retrieved from FedTech Magazine web site: https://FedTechMagazine.com/article/2020/11/agencies-can-glimpse-future-predictive-analytics (https://FedTechMagazine.com/article/2020/11/agencies-can-glimpse-future-predictive-analytics) Siddiqui, T., Alkadri, M., Khan, N. (2017, May-June). Review of Programming Languages and Tools for Big Data Analytics. International Journal of Advanced Research in Computer Science. 8(5). 7 Raksha, S. (2024, February 15). DataIntelo. Retrieved from DataIntelo web site: https://dataintelo.com/report/data-analytics-likely-to-reach-market (https://dataintelo.com/report/data- analytics-likely-to-reach-market) Cancel Post Reply