Upskilling Non-Tech Professionals with Data Science

Jan 08, 2026

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Build data literacy as a non-tech professional with a data science course in Bangalore and stay competitive in a data-driven job market.

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Upskilling Non-Tech Professionals with Data Science

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Upskilling & Data Literacy Why Non-Tech Professionals Need Data Skills
What is Data Literacy? Data literacy is the ability to read, understand, analyze, and communicate data effectively for informed decision-making.
Why Upskilling Matters Data-driven workplaces demand analytical thinking. This is why non-technical specialists are adopting data science upskilling: ● ● ● Career security: Data-literate workers find it harder to be automated. Improved decision-making: Data decreases guesses and bias. Career development: Data skills provide access to leadership and cross-functional positions. Relevance to industry: Analytics is now essential to almost every industry. ●
Who Should Learn Data Science? Data science is valuable for non-technical professionals across roles like ● Sales teams to forecast demand and improve conversions ● HR professionals to track workforce analytics and retention ● Finance professionals for predictive analysis and risk assessment ● Operations managers to optimize processes and efficiency ● Product managers to make data-backed product decisions ● Business leaders and consultants to support strategic planning
Key Skills to Learn Basic data understanding and data-driven thinking. • Reading and interpreting dashboards and reports. • Data visualization for clear insights and storytelling. • Fundamentals of Excel, SQL, and analytics tools. • Identifying patterns, trends, and anomalies in data. • Asking the right business questions using data. • Communicating insights effectively to technical teams. • Applying data insights to support better decision-making. •
Learning Path in Bangalore ● ● Start with foundational data literacy and analytics concepts Enroll in a structured data science course in Bangalore designed for non-tech professionals Learn practical tools like Excel, SQL, and visualization platforms Work on real-world business case studies and projects Opt for flexible learning modes such as weekend or online classes Gain mentorship from industry experts and practitioners Build confidence in using data for everyday business decisions ● ● ● ● ●
Career Benefits ● ● ● ● ● ● ● Make smarter, data-driven business decisions Improve job performance and workplace impact Open opportunities for role expansion and career growth Gain confidence in collaborating with technical teams Stay relevant in AI- and analytics-driven industries Strengthen leadership and strategic decision-making skills Enhance employability across multiple business domains
Conclusion Data literacy is no longer optional. Upskilling in a data science course in Bangalore empowers non-tech professionals to make informed decisions, stay competitive, and grow confidently in data-driven careers.

Teaching Students Research and Information Literacy Skills

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Teaching Higher Order Literacy Skills through the LNF at KS3

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Challenge Yourself 8.4 In this project, you will use scenarios and a scenario summary to analyze car purchasing optio

https://theacademicessays.com/downloads/challenge-yourself-8-4-in-this-project-you-will-use-scenarios-and-a-scenario-summary-to-analyze-car-purchasing-options-the-data-for-this-project-were-downloaded/ Challenge Yourself 8.4 In this project, you will use scenarios and a scenario summary to analyze car purchasing options. The data for this project were downloaded from: https://www.fueleconomy.gov/feg/download.shtml You will use custom filtering to find the data you are interested in and copy it from this very large data set to another worksheet. You will then use Advanced Filter, conditional formatting, sorting, and subtotals to further analyze this subset of data. You will manipulate a PivotTable created from the main vehicle data set. Finally, you will use Solver to solve a business problem to help you pay for your new car. Skills needed to complete this project: â€¢ Creating What-If Analysis Scenarios â€¢ Creating Scenario Summary Reports â€¢ Creating a Custom Filter â€¢ Using Advanced Filter â€¢ Creating New Conditional Formatting Rules â€¢ Sorting Data on Multiple Criteria â€¢ Adding Subtotals â€¢ Changing the Look of a PivotTable â€¢ Filtering PivotTable Data â€¢ Activating the Solver Add-Inn â€¢ Using Solver Step 1 Download 1. Open the start file EX2016-ChallengeYourself-8-4. The file will be renamed automatically to include start file your name. Change the project file name if directed to do so by your instructor, and save it. 2. If the workbook opens in Protected View, click the Enable Editing button in the Message Bar at the top of the workbook so you can modify the workbook. 3. You want to purchase a new car. Your current car is worth $3,500 as a trade-in. The base sticker price on the car you want is $24,595. The first financing offer from the dealer is 1.9% APR for 24 months, with no cash back. The original financing offer terms are listed in cells A3:B9 on the Car Loan worksheet. Cell B10 contains a formula with a PMT function to calculate the monthly payment. Use what-if analysis tools to compare financing options. Create scenarios to compare financing options for the car loan. a. Create names for the following cells on the Car Loan worksheet to make the scenarios easier to follow: Name cell B3: Price Name cell B4: TradeIn Name cell B5: CashBack Name cell B6: Loan Name cell B8: Months Name cell B9: APR Name cell B10: Payment Name cell B12: TotalCost 1 b. Create a scenario named Original Financing to save the original values in cells B3, B5, B8, and B9. c. Create a second scenario named Intermediate Car. Change the values as follows: Cell B3: 33999 Cell B5: 1500 Cell B8: 36 Cell B9: 2.4% d. Show the Intermediate Car scenario to verify your data entry. e. Create a third scenario named Luxury Car. Change the values as follows: Cell B3: 62700 Cell B5: 2500 Cell B8: 60 Cell B9: 3.9% f. Show the Luxury Car scenario to verify your data entry. 4. Create a scenario summary report to compare the results for cells B10 (the monthly payment) and B12 (the total cost of the car including interest). 5. The data in the MPG Data worksheet were downloaded from the Web site https://www.fueleconomy.gov/feg/download.shtml. The greenhouse gas scores range from 0 to 10, where 10 is best. The vehicles with the best scores on both air pollution and greenhouse gas receive the SmartWay designation. Use custom filtering to find cars in this data set that meet your criteria and then copy the smaller data set to another worksheet where you can work with it further. a. Go to the MPG Data worksheet, and display the AutoFilter arrows for the data set. b. Create a custom number filter to show only rows where the greenhouse gas score is greater than 8. c. Create another custom number filter to show only rows where the highway MPG is greater than 35. d. Create a custom text filter to show only rows where the Trans column contains the word auto. e. Create a custom text filter to show only rows where the value in the Fuel column equals Electricity or Gasoline. f. At this point, the list of more than 2,000 cars should be filtered down to just 48 records. g. Copy the filtered data to cell A1 in the My Car Data worksheet. Be sure to include the heading row, but do not include the All Models title. Paste keeping the source column widths. 6. On the My Car Data worksheet, use Advanced Filter to copy a subset of data to another location on this worksheet. a. Copy cells A1:P1 and paste them below the data set in row 52. b. In cell A51, type: Criteria c. Apply the Accent 4 cell style to cell A51. d. Set up the criteria in the row(s) below row 52. Find cars where the value in the Fuel column is Gasoline and the value in the Hwy MPG column is greater than 35. 2 e. In cell A55, type: Filtered Data f. Apply the Accent 4 cell style to cell A55. g. Autofit column A. h. Use Advanced Filter to find the rows that meet the criteria and copy the filtered data to another location beginning in cell A56. i. Sixteen rows should have been copied from the main data set to cells A57:P72. 7. Format the filtered data set (cells A56:P72) as a table. Use White, Table Style Light 1, or Table Style Light 1. Be careful not to include row 55 in the table data range. 8. Insert a new column to the left of the Greenhouse Gas Score column. Hint: Be sure to add a column to the worksheet, not just the table. a. Copy the values from cells M57:M72 to N57:N72. b. Type this column label in cell N56: Cmb MPG Icon 9. Apply a new custom conditional formatting rule to cells N57:N72 to show the 4 Ratings icon set. Show the icon only. 10. Continue working with the filtered data that has the conditional formatting applied. Sort the table first alphabetically by the values in the Model column and then by icon in the Cmb MPG Icon column so the Signal Meter With Four Filled Bars icon appears at the top. 11. Go back to the main data set at the top of the My Car Data worksheet and add subtotals for each change in Model to calculate the average for the following: Air Pollution Score, City MPG, Hwy MPG, and Cmb MPG. 12. Collapse the data to show just the total rows. 13. Go to the MPG PivotTable worksheet. This sheet includes a PivotTable created from the data on the MPG Data worksheet. a. Refresh the PivotTable data. b. Apply the White, Pivot Style Medium 1, or Pivot Style Medium 1 Quick Style to the PivotTable. c. Display a slicer for the SmartWay field and show only data where the SmartWay value is Elite. 14. To pay for the new car, youâ€™ve decided to start a bakery business out of your home kitchen. You have limited capital and capacity, so you need to make wise decisions about which products to bake each day. Go to the Bakery Business worksheet. Cell B1 uses a SUMPRODUCT formula to calculate profit. Carefully review the assumptions and formulas in this worksheet before entering the following Solver parameters: a. Find the maximum possible profit (cell B1) by changing the values in cells C7:C10. b. The working hours required (B3) must be less than or equal to the number of working hours in the day (D3). c. The total cost of ingredients (B4) must be less than or equal to the available capital (D4). 3 d. For each item in the range C7:C10, the number of items baked cannot exceed the maximum capacity available per day as defined in cells F7:F10. This restriction requires four separate constraints in the Solver Parameters dialog. e. The items in the variable cell range C7:C10 must be whole numbers. f. There should be a total of seven constraints. g. Run Solver and accept the Solver solution. 15. Save and close the workbook. 16. Upload and save your project file 17. Submit project for grading. 4

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A Study on Issues, Challenges and Application in Data Science

Data science, also known as data driven science, is an interdisciplinary field about scientific methods, processes, and systems to extract knowledge or insights from data in various forms, either structured or unstructured, similar to data mining.Data science is about dealing with large quality of data for the purpose of extracting meaningful and logical results conclusions patterns. It's a newly emerging field that encompasses a number of activities, such as data mining and data analysis. It employs techniques ranging from mathematics, statistics, and information technology, computer programming, data engineering, pattern recognition and learning, visualization, and high performance computing. This paper gives a clear idea about the different data science technologies used in Big data Analytics. Data science is a concept to unify statistics, data analysis and their related methods in order to understand and analyze actual phenomena with data. It employs techniques and theories drawn from many fields within the broad areas of mathematics, statistics, information science, and computer science, in particular from the subdomains of machine learning, classification, cluster analysis, data mining, databases, and visualization.Data Science is much more than simply analysing data. There are many people who enjoy analysing data who could happily spend all day looking at histograms and averages, but for those who prefer other activities, data science offers a range of roles and requires a range of skills. Data science includes data analysis as an important component of the skill set required for many jobs in the area, but is not the only skill. In this paper the authors effort will concentrated on to explore the different issues, implementation and challenges in Data science. Mukul Varshney | Shivani Garg | Jyotsna | Abha Kiran Rajpoot "A Study on Issues, Challenges and Application in Data Science " Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-1 | Issue-5 , August 2017, URL: https://www.ijtsrd.com/papers/ijtsrd2340.pdf Paper URL: http://www.ijtsrd.com/computer-science/other/2340/a-study-on-issues-challenges-and-application-in-data-science-/mukul-varshney

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We Consider Open Data To Be Part Of A Broader Trend

We consider open data to be part of a broader trend towards u201copen governmentu201d where open data combines with social media, mobile technology and other feedback mechanisms to transform the relationship governments have with citizens, delivering better, more relevant public services (which we could broadly term u201ccitizen-centricu201d open data). Open data also has the power to improve individualsu2019 lives through private or third sector innovation on the back of publicly-available data sets, resulting in valuable services and economic growth (which we could term u201cconsumer-centricu201d open data). Our analysis of these global trends and our experience has underlined that the US government is right to think of transparency and open data as an evolutionary process that should adapt flexibly to changing priorities. Public sector managers and citizens have changed the way they use the available information: - Citizen-user feedback has diminished over time, but there is increasing external interest in more specialized data sets. - Performance dashboards provided public servants with a good start to allow better management and comparison between agencies, and they now have appetite for more detailed analytic capabilities to conduct sophisticated analysis and drive change at agencies. Where the publication of raw data sets has been useful for specialist developers, in our experience it has not always reached its full potential. Experience so far shows that data is most useful when it is released in a consumable fashion, and usability (for both the specialist developer audience as well as the non-specialist citizen audience) should be a key consideration in making open data real. Under-exploitation of the data already in the public domain may be for two reasons: the data is not always in a standardized format, and the market for the information may be underdeveloped. Rather, information should be easily accessible, well organized, intuitive to use and understand and data sets should be complete. In order to realize the full potential of open data u2013 for both citizens and entrepreneurs (and to minimize the burden on public services), we think it is important to: - Compromise for short-term publication, but focus on rigorous data hygiene standards for data currently being collected: The data is only as good as its quality. Inaccurate datasets can be at best misleading and at worst, harmful. Data quality is central to the open data agenda u2013 but we agree that the cost of completely u201ccleanu201d data may be prohibitive, and therefore a compromise needs to be found between quality, cost and timeliness. - Reduce, reuse, recycle: the burden on public service providers can be minimized by prioritizing the publication of data sets that are already being collected u2013 for example management information about service quality and outcomes rather than collecting new data sets. Provision of context will also be important for the publication of service performance data so they can fully understand this information and to avoid any misinterpretation of this data by the public. - Make it valuable for citizens: Open data has been most empowering and successful both in the US and internationally when it has been real for citizens, i.e. where individuals can relate to the information provided and can affect real change. Successive examples show that open data that is provided at a local (e.g. city), neighborhood and even street level can be most effective. - Standardize for easy comparability and interoperability: standardized datasets in consistent formats will enable citizens / users / developers to compare and analyze the information easily u2013 this is particularly important for organizations like police or local authorities where citizens may want to benchmark organizations against their peers. - Turn data into intelligence: Many public sector organizations are data-rich, but marshaling the different data sets to create usable, analytical intelligence to provide insights (either for managers, commissioners or the public) can be more challenging. Widespread adoption of analytic and predictive modelling techniques that analyze and visualize information would provide more easily consumable insights for those inside and outside organizations. - Make targeted investment to turn u201cdatau201d into a u201cserviceu201d: the Government should make limited and targeted investments to transform a few crucial datasets into u201cservicesu201d by making them easily consumable u2013 good examples are mash-ups like the crime maps. This is particularly important for the citizen-centric open data agenda and will help establish a market and stimulate demand for further publication of datasets. As part of the prioritization process and as discussed in our overarching perspective, we think the government is right to think about transparency and open data as an evolutionary process that responds to changing needs (from users, developers and public services) over time. Based on our experience we have developed a framework for approaching open data in this way and adapting to user needs as they become more sophisticated and which could be useful in developing an organizationu2019s open data strategy and identifying the steps that should be prioritized. In some circumstances it may be prohibitively expensive to gather and process a raw dataset u2013 so it may be worth assessing the viability of encouraging the private sector to pay and commercialize the data. Under such circumstances Government should consider making it an obligation for the purchaser to publish the dataset in its raw format. If the Government is able to make targeted investments to turn the data into a service, there may be scope to sell these services to the public and to create a revenue stream. Although beyond the scope of the consultation, the Government may wish to consider charging for access to anonymized datasets if direct commercial gain may result u2013 for example pharmaceutical firms and health data. The proposals should also apply to non-public sector providers of frontline public services such as charities or private sector businesses for the transparency agenda to be meaningful and accountability to be real. It would also help build the case for mixed provision of public services. We would urge the government to implement measures to ensure consistency of data collected and its presentation to allow comparison across providers. One approach may be to include data publication as an assessment criterion in tenders to incentivize providers. As a further step, the Government may want to consider the feasibility of making data collection and consistent presentation a contractual obligation. If this were pursued, it is worth considering transition arrangements for current contracts until the contract is completed. Although the legal and regulatory framework exists, it will be important to give further consideration to guidelines and standards to protect personal data under the open data agenda. These should include clear guidance on what constitutes personal data in this context (for example taking into account the many decision notices under the Freedom of Information Act) and standards for anonymization and pseudonymization (which have great potential in the context of health data). Individuals should be protected on a level playing field and consideration given to how data sets could potentially be merged or reconstituted across government so standards should be consistent and robust. To improve accountability and public service performance, the publication of existing management information should be prioritized u2013 which should not place an additional burden on organizations at they will be collecting and reporting this information as a matter of course. We do, however, recognize that different levels of management information are currently collected; from operational data, through to service performance measures, and then output and outcome information. We recommend prioritizing publication of outcome, output and service performance measures. Most operational data is unlikely to be meaningful to the public and will need contextualizing to ensure it is not misinterpreted. An effective way to ensure open data standards are embedded would be to make them a contractual obligation. In addition, we agree with the governmentu2019s aim to ensure the public sector is an intelligent, demanding customer. As part of this, it would be helpful to create focus groups that allow suppliers and other stakeholders to contribute to open data standards in a productive way. Jeff C. Palmer is a teacher, success coach, trainer, Certified Master of Web Copywriting and founder of https://Ebookschoice.com. Jeff is a prolific writer, Senior Research Associate and Infopreneur having written many eBooks, articles and special reports. Source: https://ebookschoice.com/we-consider-open-data-to-be-part-of-a-broader-trend/

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In this Data Analytics with R presentation, you will understand why Data Analytics is needed, what Data Analytics is, the Data Analytics Lifecycle, the types of Analytics, the various benefits of using R, and finally, a hands-on demo on Data Analytics. Data analytics is a process to extract meaningful insights from data such as hidden patterns, unknown correlations, market trends, and customer preferences. It is widely used in every company across the globe to derive crucial insights from the generated data. Here, you will understand how to analyze a dataset and discover the hidden patterns using the programming language R. So, let's get started! Why become Data Analyst? By 2020, the World Economic Forum forecasts that data analysts will be in demand due to increasing data collection and usage. Organizations view data analysis as one of the most crucial future specialties due to the value that can be derived from data. Data is more abundant and accessible than ever in todayu2019s business environment. In fact, 2.5 quintillion bytes of data are created each day. With an ever-increasing skill gap in data analytics, the value of data analysts is continuing to grow, creating a new job and career advancement opportunities. The facts are that professionals who enter the Data Science field will have their pick of jobs and enjoy lucrative salaries. According to an IBM report, data and analytics jobs are predicted to increase by 15 percent to 2.72 million jobs by 2020, with the most significant demand for data analysts in finance, insurance, and information technology. Data analysts earn an average pay of $67,377 in 2019 according to Glassdoor. Who should take up this course? Aspiring professionals of any educational background with an analytical frame of mind are best suited to pursue the Data Analyst Masteru2019s Program, including: 1. IT professionals 2. Banking and finance professionals 3. Marketing managers 4. Sales professionals 5. Supply chain network managers 6. Beginners in the data analytics domain 7. Students in UG/ PG programs Learn more at https://www.simplilearn.com/data-analyst-masters-certification-training-course

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Data Entry Service Provider

Data Entry India is a ISO 9001:2008 certified company and provider of Data Entry, Data Conversion, Data Processing, Web Scraping, Online Data Entry Specialist, Offline Data Entry Specialist, Excel Data Entry Representative, Data Extraction , Magento Product Upload Services, eCommerce Product Upload Services , Catalog Data Entry and back office services based in Ahmadabad, India. Our services transcend geographical boundaries as our clients are based in United States of America (USA), Canada, United Kingdom (UK), Spain, Australia (AUS), New Zealand (NZ), Switzerland, France, Netherlands, India, Israel, Japan, Hong Kong and Singapore. They include insurance companies, Universities in USA, UK, Canada, Schools, educational institutions, marketing firms, Magento, Ecommerce , WooCommerce, Shopify, BigCommerce products Data Entry Services, media companies, medical research institutions, retail businesses and trade associations just to mention a few. All Under One Roof And All At One Stop Services in India We provide a wide range of offshore services all under one roof and in a one-stop basis. You no longer have to worry about in house troubles because you are able to outsource data entry and back office services to us. We have 16 years of experience providing the most affordable, flexible and high quality services to our esteemed clients. We keep up to date with the latest technologies which help in the conversion, Contact us for OCR services India, data processing India, web research India, outsource data entry India, outsource data conversion India, outsource data processing India, outsource web research service India, outsource OCR services India, outsource form processing India, form processing India. Avail Data Entry Services at Data Entry India, Data Processing India, Data Conversion India, Data Entry Services India. Data Entry Company India: Data Entry India is a Data Entry Outsourcing Company providing affordable Product Data Entry India, Web Scraping, Data Extraction Magento and e commerce products Data Entry and Catalog Data Entry , Web Research services India, Internet Research India, Data Entry Outsourcing India, Data Entry In India, Online Data Entry India.

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Data-Architect Simulation Questions, Data-Architect Practice Exams Free | Data-Architect Exam Certification

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Test DAS-C01 Price & Amazon DAS-C01 New Question - Reliable DAS-C01 Test Tips

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Measuring Component Sets specify which characteristics and values should be used to group usage data for aggregation purposes.* Measuring Components: These represent "buckets" or unique combinations of dimension values that are generated by Measuring Component Sets. Measuring Components store the aggregated usage data for each combination of dimension values.* Data sources: These store project-provided SQL for getting data from different sources, such as usage subscriptions, measuring components, or virtual meters. 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