Expert Statistics Assignment Help That Transforms Data Into Insights

Directions in programming and security of information for the data scientist Quotes Python is a good programming language choice for data scientists to learn. According to Bansal and Srivastava (2018), “This language can support different styles of programming including structural and object-oriented. Other styles can be used, too. Coding and debugging is (sic) easy to do in python, mainly because of ‘nice’ syntax.” (p.16). In addition to its advantages in flexible programming, python has other features that make it a good choice. “Tools like Python and SPSS are free of cost and their data handling capabilities are also quite good. However, career in SPSS that is less impressive. It offers very few jobs and salary is not much.” (Bansal & Srivastava, 2018, p.17). The flexible programming capabilities, the cost, and the demand for python programmers as shown by number of jobs and salary make python a great choice for data scientists. Paraphrase Protection of the confidentiality, integrity, and access to sensitive information such as credit card and other financial information, are vital to cyber security. The three components that comprise the cyber defense triad; mandatory access control (MAC), limiting subversion, and verifiability are not sufficient, but are necessary for securing information. Incorporation of the elements of the cyber defense triad into an operating system (OS) is critical for a secure cyber system as an operating system that does not include these elements is a non-trustworthy operating system and thus not scientifically possible of securing information. One way to provide such security to an OS is to include a “Reference Monitor” as an aspect of the OS that will allow for the building of secure cyber systems. A Reference Monitor can address the cyber defense triad in that it enforces security policy on all references to information. It also limits subversion by being tamper-proof, and it provides an audit trail for verifiability. One way to implement the Reference Monitor is by a security kernel. This implementation can then evaluate all requested access to information by a user or a program (client) as to whether that client has authorized access to that information based on a reference check of the client’s clearance against the authorization access label of the information desired Only allowing access to those who match (Schell, 2016). Summary Security of sensitive data will never be efficient until system architectures are designed with security firmly integrated to create a trustworthy system. A trustworthy system would include three critical properties: protection against subversion, mandatory access control (MAC), and is verifiable. Current approaches to operating system design and management involve nearly endless patching of the system following the discovery of vulnerabilities. This reliance on monitoring and surveillance, whether intentional or not highlights the fact that systems were not designed with security as a firm requirement and that there are as yet numerous holes that can be exploited. An answer to this problem would be to integrate a security kernel into the design of an operating system. This approach has had success in the past, but have fallen out of use, although some still exists such as the Intel IA32 architecture. The Reference Monitor is one such security kernel. It would ensure increased security as it would provide for MAC policy implementation for every reference to information aiding in prevention of subversion. Such an implementation provides for the integrity and confidentiality of information. As the security kernel is an integral part of the OS and supporting hardware it is tamper-proof. As every reference for information is monitored, the security kernel provides for verifiability. It may take some-time to design and build such a security kernel, however once completed it can be implemented in a wide variety of systems providing for an economical use (Schell, 2016).   References: Bansal, A. & Srivastava, S. (2018, March). Tools used in data analysis: A comparative study. International Journal of Recent Research Aspects, 5 (1), 15-18. Schell, R. R. (2016). Cyber defense triad for where security matters. Communications of the ACM, 59(11), 20–23.

With an average of 256 programming languages, data scientists have a wide variety of languages to use in their analytical tasks. Some programming languages work best for software application development, other work best for development of games while others work best for data science. Statistical programming languages are high-level programming languages that enable data scientist to create code that is independent of the type of computer. Statistical programming languages are used to analyze data sets and help analysts to generate useful information from big data. Some of the most familiar statistical programming languages include R programming language, SQL, BASH, MATLAB, PHP, Ruby, Go, SAS, Java and Python (Tippmann, 2015). First, statistical programming languages and tools provide data analysts with an effective platform representing, modelling and processing massive amounts of data. The exponential growth in the internet, information systems, communication networks and social media platforms have enabled data scientists to collect massive amounts of structured, semi- structured and unstructured data. Statistical programming languages provide tools and means to derive knowledge, insights and conclusions from the data that may not be in the data set directly. Secondly, statistical programming languages such as R and Python provide algorithms that help computer scientists to process raw data. These languages offer several analytical methods that reveal interesting patterns and trends from big data. Data scientists use numerous algorithms for machine learning, Artificial Intelligence and predictive analysis. Some of the algorithms include K Nearest Neighbors, K means clustering, Linear Regression and Logistic Regression. Statistical programming languages are also significantly crucial for statistical modelling models. Experts define statistical models as mathematical models that embodies statistical assumptions concerning the generation of sample data, or similar data gathered from larger populations. Statistical modelling helps data scientists to encode information extracted from the data more effectively and quickly. The accuracy of the conclusions drawn from big data analysis is of paramount importance in the digital world. Statistical programming languages provide a standard form of processing data, thus allowing data scientists from across the globe to collaborate in data processing tasks. The standardization of data analysis also enables the comparison of the results obtained and to determine the accuracy of the knowledge generated (Tippmann, 2015). Despite the complexity of some programming languages such as R, they are relatively easy to use once the analysts understand the syntax of the language. It is relatively easy to generate mine insights from data when using statistical programming languages. Lastly, statistical programming languages allow swift and quick mining of big data. Several programming languages enhance the development of communities that consist of leading statisticians, data analysts and data scientists from different parts of the globe. Some Advantages And Disadvantages The R Programming Language Has Over The Other Main Statistical Programming Languages R is one of the most popular statistical programming language for statistical modelling and analysis. Like any other programming language, R has its advantages and disadvantages. Advantages Of R Open-source – R is an open-source programming language. Anyone can work with R and its tools without the need of paying a fee or acquiring a license. Software developers and data scientists from around the world are allowed to participate in the development of R by customizing the existing packages, resolving issues and developing new packages. Exemplary support for data wrangling – R offers packages such as readr and dplyr that are used to transform unstructured and semi-structured data into a structured form. The Array of Packages – R has a vast array of packages in the CRAN repository that appeal to all the areas of industry (Gardener, 2012). Highly Compatible – R is highly compatible with other high-level programming languages such as C, C++ and Python. R can also be effectively integrated with existing information systems such as databases and Hadoop. Platform independent – R is a cross-platform programming language. This means that R can work effectively with Mac, Linux and Windows operating systems. Continuously growing – R is a constantly growing language that provides updates whenever a new feature is added. This is the main language why R is the most dominant among all the other developing statistical programming languages. Disadvantages Of R Programming Languages Basic security – R programming language lacks underlying security, an essential feature that is offered by other programming languages such as Python. As a result, R cannot be embedded into web-applications. Complicated language – R is one of the most challenging languages to learn, especially for users who do not have experience in programming (Matloff, 2011). Lesser speed- Due to the complexity of R programming language, programmers with limited experience in R may find it challenging to implement algorithms across the different packages. Data Handling – the physical memory of R stores more objects and utilizes more memory as compared to other statistical programming languages such as Python.   References Gardener, M. (2012). Beginning R: The statistical programming language. John Wiley & Sons. Matloff, N. (2011). The art of R programming: A tour of statistical software design. No Starch Press. Tippmann, S. (2015). Programming tools: Adventures with R. Nature News, 517(7532), 109.

Tableau and Power BI are some of the popular business intelligence (BI) and data visualization apparatuses capable of helping organizations especially hospitals and clinics in analyzing and presenting their data (information) for better analysis and decision-making (Milligan et al., 2022). Whereas the mentioned BIs have analogous objectives, they vary in terms of characteristics, proficiencies, price rating, and user experience. Below represent a chart comparing the benefits and challenges of adopting Tableau and Power BI.   Criteria Tableau Power BI   Benefits This BI is highly robust and flexible data visualization (Jena, 2019). Tableau is well-known for its effortlessness of data retrieval and exploration Power BI affords a seamless integration with Microsoft tools like Excel, Azure, and Sharepoint (Lyon, 2019).   Power BI's "Q&A" feature permits users to ask queries in basic language and collect visualizations  as  responses  increasing  its accessibility for simple users. Challenges Tableau can be Costly, particularly for minor business organization or individual users. Licensing costs can increase quickly if the organization needs Power BI presents a free version, but more advanced features need subscription which can escalate cost (Lyon, 2019). Power BI can have restrictions when connecting to a given data databases making it challenging     a Tableau need advanced features (Jena, 2019). Data preparation is time- consuming procedure in Tableau. Cleaning and structuring data to satisfy the tool's necessities is challenging for complex datasets. for    health    organization    with    various         data ecosystems (Lyon, 2019).   While Power BI is very customizable, making wide customizations to need knowledge of “Power Query formula language (M)” or “DAX (Data Analysis Expressions),” which might be challenging for some individuals (Lyon, 2019). Cost Tableau is expensive, particularly for smaller businesses or users. Licensing costs cumulate quickly, specifically if the business or an individual requires advanced features (Jena, 2019). Power BI is a free version, nonetheless the more advanced features require a person or business to subscribe monthly which triggers more cost especially when new features are added (Lyon, 2019). Licensing Licensing for Tableau if according to the pricing model of the user (Jena, 2019). For example, the user can decide to purchase Tableau models such as Tableau Server, online, Tableau prep, and Tableau desktop. Licensing is according to the price of the models (features of Power BI). For instance, Power BI pro, Premium, and Embedded (Lyon, 2019). Number of Users Tableau is an extra scalable choice and can house a greater Power BI is better suitable for small to mid-sized health teams due to its limited scalability for large     number of users, making it appropriate for small and large health organizations (Jena, 2019). healthcare teams (Lyon, 2019).   Tableau permits seamless integration with cloud services, SQL Server, Oracle, Amazon Redshift and Google BigQuery (Jena, 2019). Power BI is part and parcel of Microsoft ecosystem making it easier seamlessly integrates with SharePoint, Excel, Azure, and Dynamics 365 (Lyon, 2019). Vendor Tableau is recognized for its robust customer support. They deliver numerous support options like phone, email support and online knowledge platform (Jena, 2019) Power BI belongs to Microsoft ecosystem benefiting from Microsoft's all-embracing support infrastructure (Lyon, 2019). Microsoft offers quality customer support such as online documentation, forums, and a community and users  can  get  assistance  and  share  their experiences with Power BI. Part Two Based on the above analysis and presentation, I select Tableau as the best BI for our organization due to many reasons. For example, Tableau possesses excellent data visualization proficiencies. According to Milligan et al. (2020), Tableau endows an extensive variety of charts, graphs, and collaborative dashboards helping users and organization to communicate effectively and analyze data comprehensively. The idea is significant for the organization by enabling the organization to arrive at making informed decisions on clarity and visually attractive insights. On the other hand, Tableau possess a user-friendly interface which improves it accessibility to simple and technical users. The idea helps in attaining a quicker adoption across in the organization’s department (Arfat et al., 2020). In turn, the employees of the organization can be able to harness data power without extensive training or IT support. The data expands as the organization advances hence Tableau is appropriate because of its scalability which permits users to manage big datasets and accommodate or house forthcoming growth smoothly. This scalability guarantees that the organization’s investment in data analytics gears are valuable in the long run. According to Cainas et al. (2021), Tableau endows robust data integration competences, allowing employees and organization in general to link to numerous data sources like databases, cloud platforms, and spreadsheets. This present an opportunity to the organization to consolidate and examine information from numerous sources, enabling a holistic view of the organization’s operations (Ahmad et al., 2020). Tableau supports progressive analytics and prognostic modeling through incorporations with statistical apparatuses such as R and Python which can empower the organization to discover profound insights and generate data-driven estimates to inform the organization’s strategic decisions (Ahmad et al., 2020). Finally, Tableau support easy collaboration on data schemes and share understandings with stakeholders. References Ahmad, H. (2020). Tableau for Beginner: Data Analysis and Visualization 101. Haszeli Ahmad. Arfat, Y., Usman, S., Mehmood, R., & Katib, I. (2020). Big data tools, technologies, and applications: A survey. Smart Infrastructure and Applications: Foundations for Smarter Cities and Societies, 453-490. Cainas, J. M., Tietz, W. M., & Miller-Nobles, T. (2021). KAT Insurance: Data analytics cases for introductory accounting using Excel, Power BI, and/or Tableau. Journal of Emerging Technologies in Accounting, 18(1), 77-85. Jena, B. (2019). An Approach for Forecast Prediction in Data Analytics Field by Tableau Software. International Journal of Information Engineering & Electronic Business, 11(1). Lyon, W. (2019). Microsoft Power BI Desktop: A free and user-friendly software program for data visualizations in the Social Sciences. Historia, 64(1), 166-171. Milligan, J. N., Hutchinson, B., Tossell, M., & Andreoli, R. (2022). Learning Tableau 2022: Create effective data visualizations, build interactive visual analytics, and improve your data storytelling capabilities. Packt Publishing Ltd.

What is Data Science? Business Analytics? What are the differences between these terms? What skills set are required for each? Introduction Data science and business analytics almost go hand-in-hand, with their similarity being in the sense that they are needed to collect data, model and interpret it, and to make projections. Data science is the science of extracting knowledge from data. It involves the use of automated methods to analyze massive data amounts and to extract knowledge from them. It seeks to accomplish this by defining and implementing methods and procedures that extract knowledge and information from sets of data (Earnshaw, Dill & Kasik, 2019). Data science analyzes data using the scientific method, and is mostly concerned with the rigorous data analytics-related work. On the other hand, business analytics, which refers to the psychoanalysis of data using arithmetical concepts to draw conclusions and to get insights and solutions, is about simplifying data upon the solving of a data problem and making it more accessible to deliver insights. By business analytics, there is the focus on business using analytics. The first step in the understanding of what business analytics is all about is the definition of business objectives. Once there are objectives, data can be collected, then analyzed and finally visualized. Analytics creates insights when applied to data. Business analytics as a part of analytics but in business circles is concerned with taking the insights from analytics and using them to create value (Liebowitz, 2013). Data science and business analytics are closely-related in terms of their functions, mostly because both need to collect data, the model and interpret it to create solutions and to make projections. There are certain similarities between the two which explains why there are instances during which the two are used interchangeably. The differences between them make them two different domains, especially in professional circles. It is business analytics which leverages knowledge to provide the right data which is a natural input for data science. Business analytics navigates and addresses the organizational challenges that arise in the adoption and use of data science. It means that business analytics in itself is a set of data science. Business analytics also is the end-product of data science and the two are related because business analysts and data scientists use big data to inform decision-makers and organizational stakeholders for optimal results (Provost & Fawcett, 2013). However, there are major differences between them. Business analytics involves the evaluation of collected data, from which actionable insights are developed, insights which are also solutions to specific problems and roadblocks for businesses. On the other hand, data science mainly uses algorithms and statistics plus technology to give actionable insights on structured as well as unstructured data, thereby solving issues like customer behavior, which are on a broader perspective. While business analytics uses structured data mostly, data science uses both structured and unstructured data. Structured data is the highly organized information which can be located within a defined file or record, for example, point of sales, financial, and customer data. It is usually contained in relational spreadsheets and databases, and compiling and preparing, then storing it for purposes such as analysis is relatively easy (Marr, 2015). Business analytics mainly uses this kind of data. Unstructured data on the other hand is the data that does not work so well in formats such as those of databases and spreadsheets, especially because it cannot be easily slotted into columns, fields, and rows. Text heavy data and text files like PDFs and social media posts, graphic images and photos, videos, and websites, and even PowerPoint presentations are examples of the unstructured data that together with structured data are analyzed using algorithms, statistics, and technology under data science (Marr, 2015). Business analytics leans more towards statistics, which is why the majority of the analysis is based on traditional to some digital statistical concepts, unlike data science which requires the data scientist to combine traditional analytics practices with proper computer knowledge and skills such as coding. More so, statistics in the latter follows coding and algorithm building. Another major difference between the two is that while business analytics works on specific business issues and problems, data science studies and works on trends and patterns. It explains why data scientists should be curious, result-oriented, and possess industry- specific knowledge plus communication skills for them to explain the highly technical results to some non-technical counterparts as the business analysts. The data scientists need a strong quantitative background in such fields as statistics and linear algebra as do business analysts, but the former also need a background in programming knowledge, in particular skills in data mining, warehousing, and modeling (Zhu & Xiong, 2015). These skills enable them to build and analyze complex models and quantitative algorithms that help organize and synthesize big data and information from where questions regarding how to drive strategies by solving business problems can be answered. In addition to modeling and coding skills, data scientists are required to be of great mathematical understanding and aptitude, conversant with data visualization, and that possess significant business knowledge. This allows them to easily translate data into a particular, understandable narrative, data and results they can use to tell a story and show decision-makers and stakeholders just how much the evidence that is provided by the analyzed data is important. Business analysts should mainly be able to define business requirements using analytics and problem-solving skills. They also need to be effective communicators, and they should have developed process modeling skills that are necessary for roles and responsibilities like forecasting, pricing, budgeting, financial analysis, and the passing of regulations and reporting requirements to decision-makers and stakeholders (Aston University Online, 2019). The analytical strengths, business acumen, and efficiency business analysts need are necessary for them to properly and efficiently discern insights and to help companies operate at peak efficiency through their analysis and description of important guidelines as customer bases and purchasing habits. How can Data Science and/or Business Analytics help a business? How can it enhance a business’s key performance indicators? Discuss briefly. Data Science and Business Analytics in Business The descriptive analytics, predictive, and prescriptive techniques that data scientist and business analyst professionals hold and that make them fit for these or closely-related positions in a business are needed because businesses need to collect, analyze, and understand data about their customers, the market, and their industries to make predictions that improve business performance. Data science plus business analytics equal to business intelligence, which businesses need to make smarter decisions. In the decision-making process, for example, data science ensures that the problems after being understood, and data quantified can be solved using the relevant tools implemented by data scientists and that translate the data into insights for better understanding of the business processes and teams. Companies analyze customer reviews using the analytical tools operated by data scientists from where they find the best fit for their products. It is a function that helps businesses understand and analyze the current market trends, thereby devising products for the right masses using data science tools. Considering the extent to which businesses today are data rich, they need data science to unearth the patterns hidden inside that data, from where they can predict events and make meaningful analysis. For instance, it is possible to use the raw data that is turned into cooked data by data scientists to predict the success rate of their business strategies, and all they need is key metrics identified by data science and business analytics. Predictive analytics that are carried out using tools like IBM SPSS fall under data science, and they help with customer segmentation, market analysis, sales forecasting, and risk assessment purposes. Business analytics helps businesses leverage data they then use to make calculated and data-driven decisions. Business analytics is primarily concerned with statistical analysis from where actionable recommendations result. Business analytics ensure that the data collected by businesses is centralized and cleaned to steer of problems that may result in poor decision- making as duplication. Business analytics tools filter the collected data to remove any instances of incomplete, inaccurate, or inaccurate data. As a category of business analytics, business intelligence analyzes historical data to gain insight into how a team, department, individual employee, or the whole company has performed over a certain time period (Bichler, Heinzl & van der Aalst, 2017). Statistical analysis works closely with business intelligence as the other category through predictive analysis that is carried out using statistical algorithms and tools to make predictions about that business’ future performance. The predictive analysis is based on the historical data collected. Descriptive analytics too fall under business analytics and involve the tracking of key performance indicators to know where business performance currently stands (Evans & Lindner, 2012). Prescriptive analytics on the other hand are those that use past performance to recommend how the business can handle similar situations should they arise in the future. Company data is used to boost process and cost efficiency, to monitor and improve financial performance, and to drive strategy and change, and all these happen when companies use business analytics and data science correctly to make informed decision-making. They improve overall operational efficiency and may make higher revenue in the long-run because of embracing data and analytics-related initiatives (Gavin, 2019). Illustrate with ONE example how data analytics have been used to help a business. Data Analytics in Use Companies today depend on data they collect from mobile devices, applications, websites, and social media platforms. Companies like Airbnb and Netflix among other streaming platforms use data analytics, in particular data science to improve their service delivery. They collect the data generated by users on the apps and websites and on social media platforms, then process and analyze it to address requirements. The analytics they use help provide premium services to their customers henceforth. They mainly use machine learning, one of the applications of data analytics and data mining to build specific solutions. Airbnb’s Dataportal, for example, captures guests’ and hosts’ metadata information in a graph that shows resources like users, teams, reports, data tables, dashboards and business outcomes. The manner in which they’re shown to be connected reflects their consumption, production, and association relationships (Rodriguez, 2019). Data analytics has helped Airbnb develop workflow management systems to avoid writing scripts regularly and to have scripts call other scripts. They take advantage of big data and data analytics to predict what consumers may like and the market information, from where their decisions on the products and services they should offer result. Major State your major. Pick the best or top 3 data science or business analytics software relevant to your major and briefly discuss why you think they are relevant? Data science and business analytics are now part of core business activities that their software apply in human resources, my major. More companies now use data to support their evidence-based decision making in human resources. They use the software applications for planning, forecasting, recruitment, development, and the retention of members of staff. Python, a programming language, can now be used for people analytics. Companies now have teams dedicate to people analytics, and are using Python, an open source programming language that is relevant for its wide variety of developers and support, to improve HR functions as collaboration among employees. Python is readable, almost better than Excel, because things on it are laid out clearly (Kohli, 2018). HR in a company can easily carry out analysis and reuse scripts they have saved using it. Python makes it possible to create predictive models and to deriver insights from data. It is scalable and people can analyze large datasets in it. Companies that need to predict employee churn can use Python. Tableau, another modern software for HR analytics, can be used to make hiring, retention, and investment decisions. It is an especially important tool for companies that need to visualize the relationship between HR functions like hours, productivity, and tasks. Visualization helps optimize schedules and resources with greater precision. Its relevance in HR comes from its ability to bring together human resources data in a sleek visual interface, which can best help drive insights. Companies like Walmart have noted that Tableau has provided them with efficient people analytics (Diez, Busssin & Lee, 2019). SAP SuccessFactors, the other business analytics software is used as a talent management suite and is now a major human resources technology component in companies. It works as a software as a service (SaaS) software for human capital management and is best suited for functions of talent management as recruiting, performance management, learning and development, and compensation management (Chang, 2015). It is relevant to business analytics for its functionality in people analytics, for workforce planning, and as a time and attendance software with its hubs like Employee Central serving as human resources systems of record and data repository can store employee information like their addresses, social security numbers, and salary and benefits enrolments. Its workforce analytics uses accurate workforce intelligence to make HR decisions (Yang, Smith & Churin, 2018). References Aston University Online. (2019). Data Science: Business Analytics and Big Data. Retrieved from https://studyonline.aston.ac.uk/news/2019/10/25/data-science-business-analytics-and-big-data Bichler, M., Heinzl, A., & van der Aalst, W. M. (2017). Business analytics and data science: once again? Chang, V. (Ed.). (2015). Delivery and adoption of cloud computing Services in Contemporary Organizations. IGI Global. Diez, F., Bussin, M., & Lee, V. (2019). Fundamentals of HR Analytics: A Manual on Becoming HR Analytical. Bingley: Emerald Publishing Limited. Earnshaw, R. A., Dill, J., & Kasik, D. (2019). Data science and visual computing. Evans, J. R., & Lindner, C. H. (2012). Business analytics: the next frontier for decision sciences. Decision Line, 43(2), 4-6. Gavin, M. (2019). Business Analytics: What it is & why it is important. Retrieved from https://online.hbs.edu/blog/post/importance-of-business-analytics Kohli, S. (2018). Innovative applications of big data in the railway industry. Liebowitz, J. (Ed.). (2013). Business analytics: An introduction. CRC Press. Marr, B. (2015). Big data: Using smart big data, analytics and metrics to make better decisions and improve performance. Chichester: Wiley. Provost, F., & Fawcett, T. (2013). Data science and its relationship to big data and data-driven decision making. Big data, 1(1), 51-59. Rodriguez, J. (2019). How LinkedIn, Uber, Lyft, Airbnb, and Netflix are solving Data Management and Discovery for Machine Learning Solutions. Retrieved from https://towardsdatascience.com/how-linkedin-uber-lyft-airbnb-and-netflix-are-solving-data-management-and-discovery-for-machine-9b79ee9184bb Yang, A., Smith, J., & Churin, A. (2018). SAP SuccessFactors Learning: The Comprehensive Guide (SAP PRESS). SAP PRESS. Zhu, Y., & Xiong, Y. (2015). Towards data science. Data Science Journal, 14

Data analytics involves the analysis of raw data in order to make conclusions about that information. With data analytics, organizations are increasingly optimizing their business performance, increasing their efficiency, maximizing profits and revenues, and making more strategically-guided business decisions (Broman & Woo, 2018). Data analytics involves many approaches including looking at what will happen in the future, why something happened, what happened in the past, and what should happened next. However, this cannot be achieved without specific tools ranging from databases, programming languages, big data tools, cloud computing, spreadsheets, and self-service data visualization. Using these data analytics tools, organizations are able to convert raw data into information for decision making. Databases Databases are used as data analytics tools in what is known as in-database analytics. This is a data analytics method where an organization analyzes its data within a database in which it is stored. When the data is analyzed within the database, an organization successfully eliminates the problems of having move data from the database to other tools for analysis. Additionally, in using database as a data analytics tool, it means that data analytics logic is built into that specific database instead of having to use a separate data analytics application (D'silva et al., 2018). Programming Languages Although programming languages are known for software and system development, they are also used as data analytics tools. Examples of data analytics programming languages are R and Python which have become must-have for data analysts. These programming languages have huge ranges of resource libraries suitable to various data analytics activities and tasks. For example, pandas and NumPy in Python helps to streamline highly computational data analytics tasks and activities. Additionally, these functions support data manipulation (Harris et al., 2020). Big Data Tools There are many big data tools for data analytics, one of which is Apache Stark. In data analytics, these big data tools are used as data processing frameworks and used for processing big data and machine learning. Designed for analyzing big data, these big data tools help in analyzing data to draw conclusions from the resultant information. Some of the advantages of big data tools for data analytics include fast data analysis, dynamism, and easy to use. However, they may be limited in some data analytics tasks because of disadvantages such as the unavailability of file management system. Additionally, they have a rigid user interface (Cao et al., 2015). Cloud When cloud is used as a data analytics tool, it involves performing data analytics on cloud data in collaboration with a cloud service provider. Cloud-based data analytics is also known as a software-as-a-Service (SaaS). Cloud analytics runs in both public and private clouds and when used, it helps organizations in scaling quickly because it helps in significantly reducing the costs associated with an organization having to perform data analytics using other tools (Cao et al., 2015). Spreadsheets Spreadsheets are some of the most basic data analytics tools and they feature calculations and graph functions which are suitable for data analytics. Regardless of what one specializes and other type of software, a data analyst might need, spreadsheets are a staple data analytics tool. Spreadsheets are mostly used for data wrangling and reporting because of the useful functions and plug-ins which are an advantage to the tool. While spreadsheets are advantageous and, therefore, the reason for their preference, they also have disadvantages like potential for calculation errors and poor handling of big data (Hillier, 2023). Self-Service Data Visualization While most of the popular data analytics tools require that users have knowledge and skills of their use and operation, it is different with self-service data visualization tools. These are the types of data analytics tools that, similar to other tools, help users to select, filter, compare, visualize, and comprehensively data without the need for help from specialized data analysts or specialized and advanced data analytics knowledge and skills. With organizations using these kinds of data analytics tools, it means that anyone at the organization can participate in data analytics (Behera & Swain, 2019).   References Behera, R. K., & Swain, A. K. (2019). Big data real-time storytelling with self-service visualization. Emerging Technologies in Data Mining and Information Security: Proceedings of IEMIS 2018, Volume 2, 405-415. Broman, K. W., & Woo, K. H. (2018). Data organization in spreadsheets. The American Statistician, 72(1), 2-10. Cao, M., Chychyla, R., & Stewart, T. (2015). Big data analytics in financial statement audits. Accounting Horizons, 29(2), 423-429. D'silva, J. V., De Moor, F., & Kemme, B. (2018). AIDA: Abstraction for advanced in-database analytics. Proceedings of the VLDB Endowment, 11(11), 1400-1413. Harris, C. R., Millman, K. J., Van Der Walt, S. J., Gommers, R., Virtanen, P., Cournapeau, D., ... & Oliphant, T. E. (2020). Array programming with NumPy. Nature, 585(7825), 357-362. Hillier, W. (2023). The 9 Best Data Analytics Tools for Data Analysts in 2023. https://careerfoundry.com/en/blog/data-analytics/data-analytics-tools/#microsoft-excel