Tag: spreadsheet

Categories
Life Style

How a spreadsheet helped me to land my dream job

[ad_1]

Overhead view of a person working at a laptop on a standing desk.

Searching for jobs can be a daunting endeavour.Credit: PA Images/Alamy

About a year ago, a colleague and I were lamenting the hardships of the academic job market. She had landed a tenure-track position at a prestigious research university the previous year. Now it was my turn. To help smooth the process, she sent me the link to a shared spreadsheet. Little did I know that it would become one of the most precious assets in my job-search toolkit — and academic life in general.

Open to anyone with the link, the spreadsheet — this year entitled ‘2023 – 2024 Management PhD job doc’ — has been passed from generation to generation among graduate students for more than a decade. Its main purpose is to provide an anonymous forum and listings board for job seekers in my field, management. Around May each year, candidates create a new spreadsheet to kick off the job-market season, but links to old spreadsheets are retained so their precious content isn’t lost to future generations.

The spreadsheet uses a tab-based structure. Some tabs provide a question-and-answer forum on a particular area of management; a tab called Catharsis is where academics can share unsettling experiences from their work life and discuss job-market frustrations. Others list open job postings and provide status updates on contributors’ job-hunt processes. And then there’s WWW — the who went where tab, where job seekers’ names are revealed at the end of the academic year to share where they landed after their search. There are also links to useful web resources and, naturally, memes.

If that sounds similar to Slack and other messaging tools, it is. But the spreadsheet is completely anonymous. It is also incredibly flexible, quick to load and easy to search. Plus, researchers are already well versed in spreadsheets — and appreciate the ability to trawl job-search boards while looking as if they’re working.

Resource and sounding board

On a typical day, the spreadsheet has some 30–45 concurrent users, including graduate students and early-career researchers but also hiring-committee members, journal editors and members of editorial boards. This breadth and variety makes the question-and-answer process incredibly effective: users can ask a question and get multiple responses in minutes.

Users are based all over the world, and often discuss how various aspects of academic life compare between geographical locations or according to an institution’s focus — for instance, comparing research-oriented institutions with teaching-oriented or ‘balanced’ ones. Threads might include comparisons of tenure requirements, teaching loads and co-authorship etiquette.

Screengrab of a spreadsheet.

Shared spreadsheets can provide a lightweight group chat and knowledge base for job seekers.Credit: Silvia Sanasi

For job candidates, the spreadsheet is an important source of kinship. But it serves a similar role for more senior faculty members. Users discuss everything from how to handle journal reviews to overcoming methodological or technical issues and the economics of job offers. In this way, the spreadsheet also promotes transparency, providing information about hiring conditions, expectations and compensation. It also helps to reduce ethnic and gender imbalances — because salary guidelines are made public (albeit anonymously) — and to foster awareness of standards in the marketplace.

Community outlet

The spreadsheet helped me to navigate the job market while also learning about the nuts and bolts of my field and of academic life more broadly. Among other things, I learnt how to structure my application package and answer common interview questions, and found out about salary expectations, negotiation tips and the etiquette of interacting with hiring-committee members. Those lessons helped me to land my dream job at my postdoctoral institution, which I accepted last month.

I also routinely consult the spreadsheet to get tips on the review process for specific journals, seek advice on how to handle difficult reviewers and simply rant about rejections. In this way, the spreadsheet makes me feel like part of a community and helps me to find resources on how to become a better researcher, (co-)author, reviewer and colleague. Whatever your field, such a forum can provide important benefits to mental health, which is often strained in academic life. It can also be invaluable for reducing the differences caused by geographical location and resource availability.

The management spreadsheet is not unique. Similar forms of collaboration exist in other domains and should be easy enough to establish in fields where they do not. The biggest challenge is critical mass: this spreadsheet grew out of one of the field’s most-attended conferences and has been promoted year after year, through doctoral consortia and word of mouth. Today, it is self-sustaining.

I hope this article can inspire scholars in other disciplines to adopt similar solutions to help researchers at all levels — from graduate students to senior faculty members — to navigate the difficult life of an academic.

This is an article from the Nature Careers Community, a place for Nature readers to share their professional experiences and advice. Guest posts are encouraged.

Competing Interests

The author declares no competing interests.

[ad_2]

Source Article Link

Categories
Life Style

what I learnt from using a time-tracking spreadsheet

[ad_1]

Close-up a woman looking at her wrist watch while working at a laptop

By monitoring where she spent her time, Megan Rogers could improve her working hours and track her achievements.Credit: Getty

Starting a tenure-track faculty position can be daunting, with several seemingly equally important responsibilities competing for time. These include establishing a laboratory, launching research studies, writing grant applications, publishing papers and preparing and teaching courses. You also have to mentor students, perform service activities and engage in professional-development opportunities. And all of these duties must be juggled while also learning the institution’s norms and procedures.

With this in mind, I began monitoring where I was spending my time and energy in July 2022, just before starting my faculty position at Texas State University in San Marcos. I had two goals: optimizing my work hours and tracking my accomplishments for annual reviews.

I first identified key areas that faculty members typically prioritize for career advancement: ‘research’, ‘teaching’, ‘service’, ‘professional development’ and ‘other’ (predominantly a catch-all term for reading and writing e-mails and organizing projects and weekly to-do lists). I also brainstormed subcategories to get a more granular view of how I was using my time; for example, I grouped ‘grants’ and ‘study execution’ under research, ‘course prep’ and ‘grading’ under teaching and ‘journal review’ and ‘professional organization’ under service. Then, I tracked my productive activities in 30-minute increments throughout my entire first year on the tenure track.

The key word here is ‘productive’. I considered those activities to be work-related tasks that advanced a particular project or initiative, such as writing grants, holding office hours and answering e-mails. But they excluded time spent physically in the office when I wasn’t working (for example, having lunch with colleagues, using social media or staring at the wall, thinking). I intentionally counted only time spent productively, so that I could get a sense of (1) how many hours of work I was capable of; and (2) what types of activity I completed.

To keep things as low cost and flexible as possible, I used a spreadsheet. This allowed me to add entries both on the go (which I preferred) and at the end of the day. This spreadsheet has been open on my computer ever since.

After just a few days, time tracking became a natural part of my workflow and took just a couple minutes a day to complete. As well as the name or description, category and subcategory of the activity, I also track the level at which it occurs (such as department, college, university or professional), the date, for how many hours I engage in the task and any other relevant details (for instance, what progress I make).

Crunching the numbers

I worked 1,835 hours between July 2022 and July 2023, averaging 32.8 hours of productive time a week over that 13-month period. Unsurprisingly, most of that time was spent on teaching and research, each of which accounted for about one-third of my productive hours. The trends reflect the workload of a new faculty member, with considerable time dedicated to teaching (for example, course preparation) in July and August of my first year, and a jump in research productivity between semesters and from May to July 2023 (see ‘Annual review’).

Annual review. A stacked percentage bar chart showing the breakdown of productive hours spent on areas such as teaching and research.

Collecting data is just one part of the picture, however. More important is identifying, critically examining, and responding to patterns in the data. This process not only made my annual review a piece of cake — I had all the information at my fingertips, after all — but also helped me to understand myself, troubleshoot sticking points and, ultimately, become a more productive researcher. Here are my five key takeaways:

• Working more than 45 productive hours a week is unsustainable. I exceeded 45 hours a week for several weeks in October 2022, and it started to negatively affect my health, relationships and more. Even when maintaining a 40-hour working week, I was so tired from the many cognitive demands of faculty life that I was rarely performing at my best. Tracking my time allowed me to seek balance whenever I tried to do too much. Now, I aim for no more than 40 productive hours a week.

• Tasks often take much longer than you expect. Even after years in academia, I still underestimate how long it will take me to write a paper, prepare slides or put together a grant proposal. By tracking my time, I gradually developed more-accurate estimates of the time requirements of certain tasks and thereby improved my ability to schedule them on a daily and weekly basis.

• It’s OK to have a life. I had a very productive first year: I published some two dozen papers, submitted or assisted on nine grants, did several conference presentations and chaired an international conference. I also received consistently positive teaching evaluations from students and peers — and yet I rarely worked more than 40 hours a week. The key here, again, is productive hours. When I’m working, I’m working; when I’m not, I’m not. And that’s fine. Being focused while at work allows more time for other rejuvenating and enjoyable activities.

• It is OK for focus to ebb and flow over time. Although I did reasonably well in maintaining my intended goal of a 40:40:20 split across research, teaching and service, this was not necessarily the case on a weekly or even monthly basis. My teaching-related hours were higher when I was preparing courses; my service hours sky-rocketed in April, when the conference I chaired was held; and I generally had much more time for research in May, June and July, when teaching and service loads were minimal. There is no need to be rigid about maintaining an ‘ideal’ split from week to week — if you’re diligent, these ratios will work out in the end.

• Be flexible. Before starting my position, I could only guess what tasks would be most relevant to track. With more than a year of experience behind me, I have started experimenting with extra subcategories (such as lab management) that better reflect my day-to-day work. I am also integrating ways to limit my working hours (for example, by planning activities outside work more consistently, doing a weekly review and scheduling the following week accordingly), so that my ‘eight hours a day’ do not stretch into the evenings and weekends. Furthermore, I am keeping an open mind as I refine my systems — what works for one person doesn’t necessarily help another, so I’m trying things out while being true to myself.

Odd as it might be to say, I’ve found that tracking my time can be rewarding and thought-provoking — fun, even. I have enjoyed this deep dive into my work life and plan to continue exploring trends and changes in my personal trajectories as I move forward in my career.

If you want to try out my time-tracking system, you can download my Microsoft Excel template. Good luck!

[ad_2]

Source Article Link

Categories
News

How to use ChatGPT to analyze spreadsheet data and more

How to use ChatGPT to analyze spreadsheet data and more

If you have lots of spreadsheets you would like to analyze, but unfortunately don’t have the time to invest in trawling through each one to any great depth, but known they contain a wealth of valuable insights. You might be interested to know that you can harness the power of artificial intelligence within ChatGPT to help you cross-reference and analyze different spreadsheets to provide research, business insights and more. This guide offers insight into how you can use ChatGPT to analyze spreadsheet data and more.

New features recently released by OpenAI and added to its ChatGPT AI model. Enabling users to upload and analyze various file types, significantly enhancing the AI tools capabilities. Previously called Code Interpreter the feature is now known as Advanced Data Analysis. In this quick guide we will take you through how you can use this powerful artificial intelligence to analyze spreadsheets of data, providing feedback and insights in just a few minutes—a task that would have previously taken hours of analysis.

Analyzing data in spreadsheets, financial data, product data, and sales data has never been easier using the right ChatGPT prompts. But do remember that uploading documents including personal data may not be the best thing to do. In such scenarios, it is advisable to run a large language model locally, such as Llama 2 or similar depending on the power of your PC, Mac or Linux machine.

How to analyze spreadsheet data using ChatGPT

The ChatGPT spreadsheet analysis feature is built into the ChatGPT Plus subscription, as well as the new Enterprise package and does not require any plugins. It is designed to handle large data sets and provide accurate answers to complex questions based on the data. This feature is particularly beneficial for analyzing data in spreadsheets, and is even capable of generating reports that you can download as PDFs or in a file format of your preference.

Other articles you may find of interest on the subject of using artificial intelligence for data analysis:

ChatGPT spreadsheet analysis

Financials, sales data, and research data are just some areas where the analysis of large and complex datasets is crucial for driving business strategy and operations. Let’s break down how a language model can enhance these areas, considering the integration with automation tools such as Zapier and Make to add another layer of no-code automation.

Financial Data Analysis: Financial data is typically quantitative and requires high precision in analysis. A language model could be employed to interpret financial statements, extract key performance indicators, and evaluate financial ratios. By processing historical data, it could identify trends in revenues, expenses, and profitability. For forecasting, the model could use historical trends to project future performance under various scenarios. However, it’s critical to remember that financial markets are influenced by a multitude of factors, some of which may not be present in historical data, and thus, the language model’s predictive capabilities could be limited without incorporating these exogenous variables.

Employee Data Analysis: In the realm of HR, ChatGPT spreadsheet analysis encompasses a range of metrics from employee performance data to satisfaction surveys. Here, a language model could analyze text responses to identify common themes in employee feedback, gauge sentiment, and track changes over time. For performance metrics, it can help correlate various factors with employee performance outcomes. This could inform decisions on training needs, promotions, or other HR interventions. The nuance lies in ensuring that the data is not used in isolation from the qualitative context that human judgment provides. Also be extra careful not to upload personal identifiable data to third party AI model servers such as ChatGPT and others. As explained earlier run a large language model locally, using something like LM Studio.

Sales Data Analysis: Sales data can be voluminous and vary significantly over different time periods and regions. A language model can assist in parsing through this data to identify patterns in customer purchasing behavior, seasonal trends, or the impact of marketing campaigns. It could also help in comparing performance across different sales teams or territories. Forecasting sales is complex, as it often involves understanding the nuances of market conditions, consumer behavior, and competitive dynamics, which may not be entirely captured by historical data alone.

Automation with Plugins: The integration with automation tools like Zapier, Bubble and Make opens up possibilities for real-time data processing and application. For example, a language model could be set up to receive financial data as it’s updated, analyze it, and provide a report that could be automatically sent to stakeholders. In employee data analysis, triggers could be set for when certain metrics hit a threshold that warrants attention, prompting immediate analysis and reporting. Similarly, for sales data, an automated workflow could analyze daily sales figures and provide a dashboard of insights to sales managers.

It is important to note that the effectiveness of a language model in these tasks depends on the quality of the input data and the design of the analysis framework. The model can identify patterns and provide insights based on the data it processes, but the interpretation and decision-making should be informed by domain expertise and an understanding of the broader context. Additionally, while automation can increase efficiency, it’s essential to monitor for errors or biases that could arise in automated workflows, especially when decisions have significant financial or personal implications.

The future prospects of ChatGPT look promising, with continuous improvements and developments expected. The new feature for analyzing different file types is just the beginning. As ChatGPT continues to evolve, users can look forward to more advanced features and capabilities that will further enhance their data analysis processes.

ChatGPT’s Advanced Data Analysis feature enabling users to upload different file types is a powerful tool that can significantly enhance data analysis processes. Whether it’s analyzing spreadsheets, financial data, employee data, or sales data, ChatGPT can handle it all with precision and efficiency. With the potential for automation with plugins like Zapier and promising future prospects, ChatGPT is set to become an even more valuable tool for data analysis.

Filed Under: Guides, Top News





Latest timeswonderful Deals

Disclosure: Some of our articles include affiliate links. If you buy something through one of these links, timeswonderful may earn an affiliate commission. Learn about our Disclosure Policy.