A skill that is useful for many types of jobs is web scraping. It is useful especially if the data you want to collect is available online but is spread across different pages. Then, instead of just copy and pasting the information one by one, you can then just use a script to automate the task. Based on this pooled data, you can then get some insight into whatever industry or domain that you are interested in studying.
Please note though that before you engage in this, make sure that the website permits this. You probably would not want to have yourself banned if their system detects your activities as abusive. If you want to learn how to do it, I suggest to just search for the term Beautiful Soup on Google (need basic knowhow of Python). There are many quick tutorials on how to use it online. The basic workflow is to retrieve the page, explore the page to find the tags that are associated with the data that you are trying to capture, save the relevant data and then organize the data into some dataframe. Throughout the process, it is just a matter of experimentation to ensure that you are actually downloading the things that you are interested in.
The applications of web scraping are endless. In the past, I have used it to collect the job postings in the specific field I was studying in the pharmaceutical industry every week. The idea was that the job postings would somehow reflect the research priorities and trends in the industry. With the textual data such as the job description, the type of company and the specific instruments used in the job requirement, you can derive many insights about how hot the field is and what directions it is taking. Too bad though that I did not end up pursuing more this area as I had other promising areas that I pursued.
Another application of web scraping (which I have a paper under review) is in order to get a quick overview of the landscape of companies in your niche. You can then scrape the product and service pages of companies. By doing some standard natural language processing techniques (you can google TF-IDF or LDA), you can then get an overview of the words used within the niche and how companies relate to one another. Through this mapping, you can then get a big-picture overview of the types of offerings within the industry.