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Data Skills For Business Assignment Sample

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Data Skills For Business Assignment Sample

Introduction

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Part A

1. Importance of data accuracy in business

Data accuracy is important in every business because inaccurate data can be leads to false predictions. Accurate data increase the efficiency level along with the self-confidence level which is necessary to enhance business productivity (Akhtar et al. 2019). In order to reduce unusual costs as well as minimize marketing difficulties, accurate data is necessary.

Data accuracy enables better decision making

Inaccurate data can be leads to faulty predictions and that can be created major losses to the business. Profitability is the major reason for false predictions of data which is necessary to identify and minimize them according to the predicted outcomes. Self-confidence level can easily be increased by a better decision-making process, which easily enhances the productivity of a business (Radovilsky et al. 2018). That helps to reduce unusual costs of business and high-quality data is necessary which enables a better decision-making process. The process of decision-making easily mitigates major business risks which is necessary to business can improve their efficiency level. High-quality data is important to produce the better output for a business. Data accuracy is a necessary component to enhancing more value of the entire organization and that can easily mitigate long-term business risk.

High-quality data is represented and maintain correct values and creates huge opportunity in the future. There are several benefits of data accuracy such as minimizing major errors, establishing innovative policies, building trust, and improving the decision-making process. There are some difficulties which as the market changes as per the customers' needs which sometimes respond to more responsibilities in the business (Mikalef et al. 2018). Maintaining the efficiency level of the business organization is necessary, which measures major errors and minimizes long-time difficulties. In order to, adopt standard accuracy is necessary for the business which always maintains data accuracy. Data accuracy is necessary to increase business sustainability and it is easy to make new shreds of evidence to achieve long-term business goals. Sometimes logical as well as empirical shreds of evidence are necessary for the business which is always determining important business characteristics to find out some reliable sources.

Give accurate information to the customers

An accurate information process is mandatory to improve sales volume which is monitored by the real time information process. Sometimes consistent experiences by set realistic pieces of information that easily track real-time information process in the business. Feedback from the customer is necessary and sometimes accurate information processes can easily monitor by the data accuracy level. Appropriate information process is necessary for the business which is necessary to increase more business demands. Positive opportunities, as well as brand awareness, are also increased by accurate information, which is help to maximize limited future opportunities (Fleaca & Stanciu, 2019). Data accuracy is necessary to develop companies’ business profit and that helps to know budget planning about the business process. Accuracy is important in the business which is maintained by the customers' information process. Sometimes duplicate leads are involved by the inaccurate data set.

An accurate information process is necessary for the business during the time of customers' purchasing. The accurate information process is based on the quality of the product but sometimes data accuracy can be played an efficient role to attract potential customers. Sometimes big data professionalism is necessary and that can be involving the customer feedback process, which is necessary to improve business sustainability along with brand awareness (De Mauro et al. 2018). Information from the dataset is necessary to enhance more values, predicted with the investigation process which easily identifies customers' requirements. In order to, increase brand awareness of the business is necessary which is important in the business and data accuracy is another way which increases brand awareness.

Improves productivity and marketing compliance

Improves efficiency level as well as automate errors reports are necessary in the business which involves accurate data. Accurate research for the dataset is necessary and which is still eliminates wrong conclusions. Data management helps to minimize potential errors and established a decision-making process which improves business sustainability. Data accuracy refers to a high-quality framework of the business and easily improves customers retention process (Bencheva & Stoeva 2018). Data accuracy is minimizing an ineffective decision-making process which is necessary for the business. In order to, minimize potential errors data accuracy is important and that can easily reduce the unusual costs of the business. Data accuracy is important and it is necessary to make high-quality data included with transparency. Data accuracy is sometimes maintained by a better decision-making process which is necessary to increase brand awareness of a business.

Marketing compliance, as well as increased business productivity, is necessary which is involved by the data accuracy. Transparency as well as the accuracy of the data set is necessary6 which is easily enhancing brand awareness. Accurate information is necessary for individual customers and trust must be increased due to accurate data. Individual customers are blessed, and brand awareness is also increased the consumers' trust. Data accuracy helps to maintain profit and efficiency functions which are involved by data accuracy (Dong & Triche 2020). Accuracy helps to know companies' budget plans and also reduce unusual expenses of the business. Skills and knowledge are also important, which is increased brand awareness and that can easily maintain important characteristics to maintain sustainability and reliability. Sometimes inaccurate data creates false predictions which are needed to minimize by a better decision-making process.

2. Process of increasing data accuracy 

Creates centralized dataset

A centralized dataset is necessary and that can create a blueprint of an organization and limitations are accurately find out by that. Sometimes marketing as well as product reporting process is necessary which is developed by the data accuracy and also monitoring installed collectors by widely using accurate data set. Business of central administration can easily control individual administrators which is necessary to increase business sustainability. In order to, connect a business with the central server, centralized data set is necessary and that can be enhancing organizational accuracy by modifying location as well as accesses to the internet servers (Ciampi et al. 2021). Data accuracy is important but there are several processes which are increased data accuracy such as standardized data entry process, specific data entry process, keeping sync with the data set, and capturing the data results. 

Centralized data set is necessary to increase business sustainability and that can increase business capabilities by the innovation process. In order to explore the data set is mandatory to continuously monitor business activities and a better decision-making process is necessary to increase accuracy. In order to move the digitalization process, the data accuracy level is increased by using innovative business strategies. In order to, maintain the highest data accuracy is necessary to find out and connect individual chunk files, but digital disruption involves major difficulties during the same time. Set data quality which is necessary to achieve appropriate quality and avoid overloading of data is mandatory to increase data accuracy. Improving data accuracy is important which easily identifies the major reasons, and how to increase the accuracy level (Grover et al. 2018). Identifying the right directions of the business is necessary which is improved by the data accuracy level.

Set data quality goals

Setting positive goals of the data quality is necessary is important to deliver the expected results of the business. Individual business organizations need to avoid data overloading which can be harmful to the entire business process and data accuracy does not maintain by that. Identifying the error data report is important and can be easily reviewed by the advanced software and quality professionals trying to find out the error reports (Sousa & Rocha 2019). Always adopting standard accuracy is necessary for the business and that can easily increase the accuracy level of the data set. Always identify a positive work environment which is necessary to minimize external threats to the data.

Find out inaccurate data sources

Identifying inaccurate data sources is necessary for the business to increase data accuracy and minimize major errors. Identifying the errors in data sources is mandatory and that can be improves the process of data accuracy. High-quality data is necessary which is easily enhancing the major opportunities in the future (Sousa & Rocha 2019). Individual managers need to minimize data overloading which creates excessive procure and the quality of the data also decreased. Individual data entries are followed with the important teams to reduce major errors and set accuracy levels.

Always review the data sources

Continuous review of the dataset is necessary which is important to find out and minimize inaccurate data. Identifying the process of data correctness is mandatory and that can be increased by continuous review of the data set. Identifying an accurate data set is mandatory which is necessary but during the digital disruption accurate data are facing major errors and performance has decreased due to the same reason (Badawi et al. 2019). In order to, identify the accurate data set is mandatory in the business which is necessary to increase efficiency level.

PART B

Calculate the mean, median, mode, maximum, minimum, range, variance and standard deviation

CULC is a company which provides leisure services and tour packages for different age groups people. As per the view of Anderson et al. (2018), in this assessment, the owner wants to ascertain the company analysis through the statistical measures, for these different measures, are evaluated by the marketing manager of the firm. The CEO of the company wants to know the leisure for different age categories in the country by determining the data on average gross income and expenditure. If spending on leisure increases with higher gross income, then the changes in the overall business plan are reflected in this assessment. As per the view of Auler et al. (2020), the given is used to calculate the mean, “median, mode, maximum, minimum, range, variance and standard deviation”. The two items that are given show ‘Average gross income and Spending on leisure”.

All the statistical measures are evaluated using the formula in excel and the values are interpreted in the next part. As per the view of Sinyuk et al. (2020), the data that are calculated, show accurate values and provide help to make effective and better decisions related to the new service development. As the firm opens for tours and travelling, the marketing managers must develop promotional activities to get accurate ranges from the obtained numerical values.

From the data, it can be stated that the average mean stands at an average rate. it is evaluated by dividing the whole data set by its units. As per the author Bailer-Jones et al. (2021), the mean value is defined as the average value from which the marketing manager of CULC Holiday Company can get the data easily. The values of the statistical measures are derived by using the formula by comparing the data that are given as “Average Gross Income (in USD) and Average Spending on Leisure (in USD)”. These measures can interpret effective results from where the analysis must be derived from the products of the company. As stated by Börner et al. (2018), the data can also use to predict the sales and demand of the customers. The main importance of this calculation is that it shows the difference between contradictory and reasonable results. If the company spends more with a higher grosser income, then there will be no extra losses incurred to the firm. 

Strengths and weaknesses of the company as per the Interpretation of these measurements

The statistical analysis shows accurate data that can be helpful in gaining market insights. As mentioned by Büntgen et al. (2021), at the same time, it improves the efficiency and potentiality of the firm by deriving remarkable changes. From the above calculation, it can be said that the mean value is obtained at 26,591, it is obtained by evaluating the whole data set by its units. The median value is obtained at 47,500 where the two data sets are aligned in the same units. As opined by Carter et al. (2021), the value of mode is 0, this is because there are no repetitive no. in the entire data set. The value of the maximum is stated at 57,970, as it is the highest frequency present in the whole data set. The minimum value is obtained at 1,300; this is the Average Spending on Leisure between the age groups of 15-19.

The value of the range 56,670 is evaluated on the gross income, where they obtained value of the range shows the linear variability of the data set. The variance is obtained at 566644208.7, it shows at the age group of 40-44 and 45-49 have the highest variability. The covariance is valued at 17111908.26, where the joint variability of the two variables or random data is evaluated. As per the view of Corbet et al. (2018), if the highest variable changes its position, then automatically the other variable will change or shift its position. The standard deviation is valued at 23804.28; it shows the dispersion of the data to obtain the mean value for the entire data set. Here the value of standard deviation is high, by this, it can be stated that the data are more spread out from the given ranges.

The value of correlation is obtained at 0.749; this shows that the relationship between the two variables has an accurate degree of impactful coordination. As per the author De Mauro et al. (2018), the given two variables “Average Gross Income and Average Spending on Leisure” show positive value where they can move in the same direction. The Correlation Coefficient is valued at 718.85; it shows the linear relationship between the two variables. It also shows the strength that is used to make the relationship more effective in the two stated variables. Its weaknesses can be shown by the determination of the mode and median value which shows negative returns in near future. From the above graph, it can be stated that the downward sloping of the line curve in the spending value. As narrated by Deja et al. (2021), this shows the spending value is more than the income. Therefore, the company needs to reduce its expenses to save income to get future acquisitions.

Calculation of the Correlation Coefficient between the sales amounts and marketing expenses

From the above calculation of coefficient between expenses on marketing and the number of sales can be ascertained. The total income from leisure is valued at 535,160 and the spending total is valued at 49,840. As per the view of Forlani et al. (2018), the sales are assumed to be the difference between income and spending and the marketing expenses are assumed to be half of the spending range. From these assumed values the correlation coefficient is obtained by dividing the covariance with standard deviations. The values are interpreted as covariance is obtained at 769053565.3 this is ascertained by evaluating the expenses on marketing and sales. The standard deviation value is obtained at 96698.8249 with the help of the same assumed formula. As opined by Kiss & Schmuck (2021), using both these variables the “Correlation Coefficient between the sales amounts and marketing expenses” is valued at 7953.08. This positive value enumerates the highly positive relationship between the two variables of the provided data set.

Scatter plot

A Scatter plot is the visual representation of two variables in a data set. As stated by Luz et al. (2020), in this the dependent variable is income and the independent variable is sales. This is because the sales values are not dependent on the firm potentially, it mainly depends on the consumer's choices. In the context of income, it is totally interdependent on other factors of production or company. If the company wants to produce more than the income level of the firm can be high. As narrated by Malkawi & Khayrullina (2021), at the same time if the consumer choices do not satisfy the products, then the company's gross income level can be down to a great extent. In the company, CULC, the scatter plot is drawn to ascertain the “trend (best fit) line” in the current market scenario of the country UK.

From the above graph, it can be stated that the variables plot shows the contradictory connection between the two variables. As per the author Malmia et al. (2019), the two mentioned variables relate to the spending and income value show the same directions and ensure the same values. This scatter diagram is made according to the Cartesian system where the two-axis shows two different variables. It has various aspects and the best-fitted line which shows the trend in the last two years.

From the above table, it can be stated that the company need to evaluate its spending to ensure better outcomes for future sales. The trend values 43,100, 49,665, and 52,565 shows increasing rates. As per the author Otum & Atah (2021), this is because the systematic risks of the company are above the risk rate of return of the company. It can be stated that the company needs to evaluate the leisure incomes in the age group of 60-64. This is precise people at this age mainly need a proper atmosphere to go on a vacation. This is because the choices and requirements of people change as per the market.

Recommendation on marketing expenses

From the market scenario data evaluation from questions 3 and four, it can be stated that the marketing expenses must be reduced to get the gross income in the next years at an increased rate. As stated by Ritter & Pedersen (2020), this is because the firm news to analyze the market conditions before assuming the trends in the customer requirements. The Correlation Coefficient shows the effective relationship between the two tested variables where the firm can choose the proper investment aspects (Sen-Crowe et al. 2021). From the above-stated data, it can be interpreted that the marketing manager can suggest to the CEO of the firm the further expansion and growth segments of the organizations. The statistical data are calculated as per the given data set for the company where the gross incomes and the money spent on providing leisure’s for the people.

The next acquisition and further evaluation of the data can be stated the increasing value for the firm may increase the worth but at the same time. As per the view of Sinyuk et al. (2020), if the increasing value of the coefficients exceeds the ranges, then there is no chance of getting more income. It is to be recommended that marketing expenses can be adjusted to the expected sales. This is done to reduce the expenses; the retained income can be used in the promotional activities of the company. As the company deals in facilitating holiday services, it can more focus on its promotional activities to increase its service sales (Smaldone et al. 2022). If the sales are increased simultaneously the company's worth will be increased. The abovementioned recommendations are suitable for the different age groups who accompany the firm to get leisure’s facilities at a reasonable rate. 

The stated suggestions and the strategies must be ascertained by the marketing manager to get averages from the financial statements. If the firm wants to increase its spending with a higher income, then its working capital may get reduced. As more liquidity hampers the further acquisitions of the firm. Therefore, balancing spending and retaining the ascertain income can get better results for the company.

Part C

Question 1: What are the factors influencing buyers' decisions of a customer?

  1. The availability of the product
  2. Maintain social and economic status
  3. Identify occupation and consumers' lifestyles
  4. Including personal factors

Question 2: What are the factors influencing consumer behavior?

  1. Self-interest
  2. Identify the major barriers
  3. Influencing purchasing decisions
  4. Perceptions of demographic and cultural changes

Question 3: What are the factors that influence purchasing decisions?

  1. Increase profitability
  2. Maintaining social lifestyles
  3. Minimize economical obligations
  4. All of the above

Question 4: Identify the major reason to improve customers' behavior?

  1. Positive attitude and customers perceptions
  2. Better decision-making process
  3. Identify cultural changes
  4. None of above

Question 5: What factors are affecting consumers' purchasing power?

  1. Quality level of the product
  2. High pricing strategy
  3. Lower demand for the product
  4. All of the above

Question 6: What are the reasons to develop a customer retention process?

  1. Maintain high quality products with a low pricing strategy
  2. High price level with low quality
  3. Selecting alternative products
  4. All of the above

Question 7: what are the factors to minimize crisis context?

  1. Factors related to the decision-making skills
  2. Behavioral factors
  3. Environmental factors
  4. None of above

Question 8: How do improve customers purchasing decisions?

  1. Using alternative evaluation
  2. Purchase decision and post-purchase evaluation
  3. Problem recognition
  4. None of evaluation

Question 9: What is the reason to improve customer retention?

  1. Creates customers communities
  2. Measures customer's lifetime value
  3. Improves customer’s experience
  4. None of above

Question 10: How do social factors are influencing consumers buying decisions?

  1. Cultural and social factors
  2. Expectations regarding future income
  3. Consumer's credit and liquid assets
  4. None of above

Question 11: What are the factors that affect consumers' positive mindset?

  1. Wider social context
  2. Identify symbolic needs
  3. The quality level of the product
  4. Lack of business sustainability

Question 12: What are the decisions affected by the environmental changes?

  • Self-adopting
  • Decision-making process
  • Climate change
  • None of above

Self-interest, as well as demographic perceptions, is necessary for the business which easily minimize major barriers. Identifying the major barriers of the business is mandatory and that can be enhancing numerous business opportunities. The product, as well as service review, is necessary for the business which is important to identify the major barriers of business. The customer retention process is mandatory in the business to identify or influence customers' decision-making process (KarpoviÄ 2020). Consumers’ retention is important to maximize organizational long-term opportunities. The challenges are identified by the big data analysis which is necessary to minimize long-term risk.

Sometimes social as well as cultural changes are necessary in the business to minimize long term business opportunities. A Decision-making process is necessary and that can minimize the organizational crisis (Vassakis, Petrakis & Kopanakis, 2018). Identifying the business objectives is mandatory and sometimes necessary influencers are trying to identify the customer retention process. Individual customers are changing their perception which is important to identify the positive requirements of the business. Leadership styles influence, as well as a variety of cognitive policies, is necessary to find out numerous solutions for the business.

Reference list

Akhtar, P., Frynas, J. G., Mellahi, K., & Ullah, S. (2019). Big data?savvy teams’ skills, big data?driven actions and business performance. British Journal of Management30(2), 252-271. Retrieved from: http://oro.open.ac.uk/56760/1/Akhtar%20et%20al%20%28BJM%202018%29%20Big%20data%20and%20performance.pdf

Anderson, S. J., Chandy, R., & Zia, B. (2018). Pathways to profits: The impact of marketing vs. finance skills on business performance. Management Science64(12), 5559-5583. Retrieved from: https://ruj.uj.edu.pl/xmlui/handle/item/276585

Auler, A. C., Cássaro, F. A. M., Da Silva, V. O., & Pires, L. F. (2020). Evidence that high temperatures and intermediate relative humidity might favor the spread of COVID-19 in tropical climate: A case study for the most affected Brazilian cities. Science of the Total Environment729, 139090. Retrieved from: https://www.sciencedirect.com/science/article/pii/S0048969720326073

Badawi, S., Reyad, S., Khamis, R., Hamdan, A., & Alsartawi, A. M. (2019). Business education and entrepreneurial skills: Evidence from Arab universities. Journal of Education for Business94(5), 314-323. Retrieved from: https://www.researchgate.net/profile/Allam-Hamdan/publication/330146911_Business_education_and_entrepreneurial_skills_Evidence_from_Arab_universities/links/5c53d457458515a4c74e709c/Business-education-and-entrepreneurial-skills-Evidence-from-Arab-universities.pdf

Bailer-Jones, C. A. L., Rybizki, J., Fouesneau, M., Demleitner, M., & Andrae, R. (2021). Estimating distances from parallaxes. V. Geometric and photogeometric distances to 1.47 billion stars in gaia early data release 3. The Astronomical Journal161(3), 147. Retrieved from: https://www.research.manchester.ac.uk/portal/en/publications/empoderadata-sharing-a-successful-workplacement-data-skills-training-model-within-latin-america-to-develop-capacity-to-deliver-the-sdgs(81040465-03f9-4cfd-a547-1b629d94aad3).html

Bencheva, N., & Stoeva, T. (2018). Key Skills and Competences for Social Business Advisors: Views from Experts. International Journal of Organizational Leadership7, 413-425. Retrieved from: http://www.ijol.cikd.ca/article_60282_309becfce57563065d573946288cf959.pdf

Börner, K., Scrivner, O., Gallant, M., Ma, S., Liu, X., Chewning, K., ... & Evans, J. A. (2018). Skill discrepancies between research, education, and jobs reveal the critical need to supply soft skills for the data economy. Proceedings of the National Academy of Sciences115(50), 12630-12637. Retrieved from: https://www.pnas.org/doi/abs/10.1073/pnas.1804247115

Büntgen, U., Urban, O., Krusic, P. J., Rybní?ek, M., Kolá?, T., Kyncl, T., ... & Trnka, M. (2021). Recent European drought extremes beyond Common Era background variability. Nature Geoscience14(4), 190-196. Retrieved from: https://journals.sagepub.com/doi/abs/10.1177/1046878120972458

Carter, J., Méndez-Romero, R. A., Jones, P., Higgins, V., & Samartini, A. L. S. (2021). EmpoderaData: Sharing a successful work-placement data skills training model within Latin America, to develop capacity to deliver the SDGs. Statistical Journal of the IAOS, (Preprint), 1-13. Retrieved from: https://www.research.manchester.ac.uk/portal/en/publications/empoderadata-sharing-a-successful-workplacement-data-skills-training-model-within-latin-america-to-develop-capacity-to-deliver-the-sdgs(81040465-03f9-4cfd-a547-1b629d94aad3).html

Ciampi, F., Demi, S., Magrini, A., Marzi, G., & Papa, A. (2021). Exploring the impact of big data analytics capabilities on business model innovation: The mediating role of entrepreneurial orientation. Journal of Business Research123, 1-13. Retrieved from: http://eprints.lincoln.ac.uk/id/eprint/42473/1/JBR_BigDataBMI.pdf

Corbet, S., Lucey, B., Peat, M., & Vigne, S. (2018). Bitcoin Futures—What use are they?. Economics Letters172, 23-27. Retrieved from: https://www.pnas.org/doi/abs/10.1073/pnas.1804247115

De Mauro, A., Greco, M., Grimaldi, M., & Ritala, P. (2018). Human resources for Big Data professions: A systematic classification of job roles and required skill sets. Information Processing & Management54(5), 807-817. Retrieved from: https://lutpub.lut.fi/bitstream/handle/10024/158849/de_mauro_et_al_human_resources_final_draft.pdf?sequence=1&isAllowed=y

De Mauro, A., Greco, M., Grimaldi, M., & Ritala, P. (2018). Human resources for Big Data professions: A systematic classification of job roles and required skill sets. Information Processing & Management54(5), 807-817. Retrieved from: https://www.research.manchester.ac.uk/portal/en/publications/empoderadata-sharing-a-successful-workplacement-data-skills-training-model-within-latin-america-to-develop-capacity-to-deliver-the-sdgs(81040465-03f9-4cfd-a547-1b629d94aad3).html

Deja, M., Januszko-Szakiel, A., Koryci?ska, P., & Deja, P. (2021). The impact of basic data literacy skills on work-related empowerment: the alumni perspective. College & Research Libraries82(5). Retrieved from: https://ruj.uj.edu.pl/xmlui/handle/item/276585

Dong, T., & Triche, J. (2020). A longitudinal analysis of job skills for entry-level data analysts. Journal of Information Systems Education31(4), 312. Retrieved from: http://jise.org/Volume31/n4/JISEv31n4p312.pdf

Fleaca, E., & Stanciu, R. D. (2019). Digital-age learning and business engineering education–a pilot study on students’ E-skills. Procedia manufacturing32, 1051-1057. Retrieved from: https://pubsonline.informs.org/doi/pdf/10.1287/mnsc.2017.2920

Forlani, G., Dall’Asta, E., Diotri, F., Morra di Cella, U., Roncella, R., & Santise, M. (2018). Quality assessment of DSMs produced from UAV flights georeferenced with on-board RTK positioning. Remote Sensing10(2), 311. Retrieved from: https://www.sciencedirect.com/science/article/pii/S0019850119300999

Grover, V., Chiang, R. H., Liang, T. P., & Zhang, D. (2018). Creating strategic business value from big data analytics: A research framework. Journal of management information systems35(2), 388-423. Retrieved from: https://files.stample.com/stample-1564010096810-2868103_1_bda-2018.pdf

KarpoviÄ iÅ, R. (2020). Learning skills for enhancing the use of big data. World Journal on Educational Technology: Current Issues12(1), 23-36. Retrieved from: https://www.researchgate.net/profile/Allam-Hamdan/publication/330146911_Business_education_and_entrepreneurial_skills_Evidence_from_Arab_universities/links/5c53d457458515a4c74e709c/Business-education-and-entrepreneurial-skills-Evidence-from-Arab-universities.pdf

Kiss, T., & Schmuck, R. (2021). A longitudinal study of the skills and attitudes conveyed by two business simulation games in pécs, hungary. Simulation & Gaming52(4), 435-464. Retrieved from: https://journals.sagepub.com/doi/abs/10.1177/1046878120972458

Luz, S., Haider, F., de la Fuente, S., Fromm, D., & MacWhinney, B. (2020). Alzheimer's dementia recognition through spontaneous speech: the ADReSS challenge. arXiv preprint arXiv:2004.06833. Retrieved from: https://arxiv.org/abs/2004.06833 

Malkawi, E., & Khayrullina, M. (2021). Digital human skills form the corporate economy and business development. Ekonomicko-manazerske spektrum15(1), 64-74. Retrieved from: https://journals.sagepub.com/doi/abs/10.1177/1046878120972458

Malmia, W., Makatita, S. H., Lisaholit, S., Azwan, A., Magfirah, I., Tinggapi, H., & Umanailo, M. C. B. (2019). Problem-based learning as an effort to improve student learning outcomes. Int. J. Sci. Technol. Res8(9), 1140-1143. Retrieved from: https://www.academia.edu/download/60752576/Problem-based-Learning-As-An-Effort-To-Improve-Student-Learning-Outcomes20190930-117399-1pl5fdu.pdf

Mikalef, P., Giannakos, M. N., Pappas, I. O., & Krogstie, J. (2018, April). The human side of big data: Understanding the skills of the data scientist in education and industry. In 2018 IEEE global engineering education conference (EDUCON) (pp. 503-512). IEEE. Retrieved from: https://www.csupom.com/uploads/1/1/4/8/114895679/v16n1p5.pdf

Otum, N. I., & Atah, C. A. (2021). Perceived Strategies for Teaching Business Education Curriculum Content for the Acquisition of Skills Competence. International Journal of Education and Evaluation E, 2489-0073. Retrieved from: https://www.iiardjournals.org/get/IJEE/VOL.%207%20NO.%205%202021/PERCEIVED%20STRATEGIES%20FOR%20TEACHING.pdf

Radovilsky, Z., Hegde, V., Acharya, A., & Uma, U. (2018). Skills requirements of business data analytics and data science jobs: A comparative analysis. Journal of Supply Chain and Operations Management16(1), 82-101. Retrieved from: https://www.csupom.com/uploads/1/1/4/8/114895679/v16n1p5.pdf

Ritter, T., & Pedersen, C. L. (2020). Digitization capability and the digitalization of business models in business-to-business firms: Past, present, and future. Industrial Marketing Management86, 180-190. Retrieved from: https://www.sciencedirect.com/science/article/pii/S0019850119300999

Sen-Crowe, B., Sutherland, M., McKenney, M., & Elkbuli, A. (2021). A closer look into global hospital beds capacity and resource shortages during the COVID-19 pandemic. journal of Surgical Research260, 56-63. Retrieved from: https://www.iiardjournals.org/get/IJEE/VOL.%207%20NO.%205%202021/PERCEIVED%20STRATEGIES%20FOR%20TEACHING.pdf

Sinyuk, A., Holben, B. N., Eck, T. F., Giles, D. M., Slutsker, I., Korkin, S., ... & Lyapustin, A. (2020). The AERONET Version 3 aerosol retrieval algorithm, associated uncertainties and comparisons to Version 2. Atmospheric Measurement Techniques13(6), 3375-3411. Retrieved from: https://www.research.manchester.ac.uk/portal/en/publications/empoderadata-sharing-a-successful-workplacement-data-skills-training-model-within-latin-america-to-develop-capacity-to-deliver-the-sdgs(81040465-03f9-4cfd-a547-1b629d94aad3).html

Smaldone, F., Ippolito, A., Lagger, J., & Pellicano, M. (2022). Employability skills: Profiling data scientists in the digital labour market. European Management Journal. Retrieved from: https://www.sciencedirect.com/science/article/pii/S0019850119300999

Sousa, M. J., & Rocha, Á. (2019). Skills for disruptive digital business. Journal of Business Research94, 257-263. Retrieved from: https://files.stample.com/stample-1564010096810-2868103_1_bda-2018.pdf

Sousa, M. J., & Rocha, Á. (2019). Skills for disruptive digital business. Journal of Business Research94, 257-263. Retrieved from: https://edisciplinas.usp.br/pluginfile.php/4989305/mod_resource/content/1/Souza%20%20Rocha%20%282019%29.pdf

Vassakis, K., Petrakis, E., & Kopanakis, I. (2018). Big data analytics: applications, prospects and challenges. Mobile big data, 3-20. Retrieved from: https://www.academia.edu/download/57651140/BDA_chapter_publ.pdf

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