- Do you understand what is at stake?
- Do you know what already exists?
- Can you take an informed decision?
- What did you consider during your decision?
- What are your unknowns (things you'd need to know before your can make a decision)?
Do you have experience with the problem which needs a decision?
Indicate who has the most expertise to make the decision
- You should be allowed to cast a vote if the decision will impact you. If it doesn't (i.e., you're not a stakeholder), your vote will not be counted towards any option
Let's start by saying I'm not suggesting you read a full book per day. What I'm suggesting is to read at least a few pages of a book per day, reading a variety of books over the course of a week.
For a while I used to start book and finish them before starting another one. I'd allow myself to read a fiction book and a technical book at the same time, but not more than that. The idea was that by reading more than one of each my brain would have trouble with context and information retention.
I've recently decided to switch this approach. The main reason was that I found myself spending too much time reading articles online that I thought didn't bring me much value over time. I always thought books were more valuable, but their biggest problem was that it required a good amount of time involvement for the value to kick in.
Just like there are two strategies in learning systems, exploration and exploitation, I decided that leaning more on the exploration side might provide useful. Instead of spending hours on the same book over a short period of time (1-3 months), I would instead read bits of many books at once.
Here are the benefits I've observed through this approach:
It's easier to identify similar sources. I would read a few books on a similar topic, and of course they would all cite the same sources. The difference between processing all those books in parallel instead of sequentially is that you notice the pattern of reuse more clearly. When reading the books sequentially, what happens is that this type of information decays over time. We start to forget what the last book was referring to, so that the next book appears to have new references.
Similar ideas can be identified and speed up reading. As you identify the same ideas in different books, instead of reading the arguments careful in each book, the best argument is read thoroughly and the others quickly scanned for additional information.
You are exposed to more variety. Some people get topic fatigue, which is that you get bored of reading on the same topic. Reading on different topics avoids this issue while also stimulating you to think about many topics. This is a great way to sometimes make connections between unrelated topics.
Overall I've been very satisfied with this experiment and I've been doing it for over 4 months now. I highly recommend it if you have a large list of books you haven't started yet. See my article How to prioritize which book to read to help you organize your reading.
Here is a summary description of each column, what they represent, and the range of valid values they can have.
- level: hierarchical layout (a word is in a line, which is in a paragraph, which is in a block, which is in a page), a value from 1 to 5
- 1: page
- 2: block
- 3: paragraph
- 4: line
- 5: word
- page_num: when provided with a list of images, indicates the number of the file, when provided with a multi-pages document, indicates the page number, starting from 1
- block_num: block number within the page, starting from 0
- par_num: paragraph number within the block, starting from 0
- line_num: line number within the paragraph, starting from 0
- word_num: word number within the line, starting from 0
- left: x coordinate in pixels of the text bounding box top left corner, starting from the left of the image
- top: y coordinate in pixels of the text bounding box top left corner, starting from the top of the image
- width: width of the text bounding box in pixels
- height: height of the text bounding box in pixels
- conf: confidence value, from 0 (no confidence) to 100 (maximum confidence), -1 for all level except 5
- text: detected text, empty for all levels except 5
Here is an example of the TSV format output, for reference.
What do I look for in a resume?
Let's start by the things I don't look at in a resume for a position in which experience is expected:
- Your university: I couldn't care less where you've studied. While having a university may sometimes tell me you've been serious enough to go through the pain of university, I also know it's possible to go through university without acquiring any knowledge.
- Your grades: It's great that you have A+ in so many classes. However grades do not always generalize to an effective worker. Furthermore, most of the applicants will also have high grades, which makes it a noisy/useless signal. Do understand that I also do not have the time to fact-check your grades, so you might as well have written you had a perfect score in every class. Be careful with this, as some people will see it as something to probe you on during the initial interviews, and this could backfire on you.
- Your extra-curricular activities: Unless you are doing extra-curricular activities that are relevant to the position you are applying for, I am not filtering for people with whom I could do things with outside of work.
- The list of all your publications: I work in a scientific field, and while for some publications are badges of success, I see listing articles as filler into a resume. If I want to know all the articles you've published, I can look it up on Google Scholar. Instead, focus on listing the areas of research you're interested in and indicating how many papers in those areas you've published.
Here are the things I look for:
- 2-3 pages: If you cannot summarize your accomplishments in less than 3 pages, then you don't know how to summarize. I don't want to know everything you've done in your professional life. I don't want to know every single paper you've published, every conference/workshop you've attended, every grant you've received, every honors you have.
- Relevant work experience: If you've been working in the same position for a different company, that will generally be a good thing. It means you already have prior experience in the field, you have seen how another company has accomplished what you might still do at your new job. It means you'll be easier to ramp up and may require less supervision/support during that period.
- List what you did: If you only list the title/position you had and the company, I have no clue what you did there. You might as well not have worked there. Clearly list the big tasks/milestones you've worked on and what was your contribution.
- List clear and quantitative accomplishments: "Increased sales by 200%", "Largely reduced operation costs" may sound great, but without the ability to compare against something, those accomplishments do not mean much.
- List technologies you've used: When hiring it is often common that you want your new recruits to already have some prior experience in the tools that are used at the company, especially if you need them to ramp up quickly. This is even more important when the set of tools used in your industry is common enough as it will communicate how in touch with the field you are.
- How long you've worked at your previous jobs: Are you the kind of person that will keep a job for a year then move to the next one? That's great! I'm not looking to work with people that have a short time horizon for their positions. I want to work with people that will go through the marathon with me.
- Proper ordering of the sections of your resume: It's a little thing, but the ordering of the sections in your resume will communicate a lot to me. It will let me know whether you know how to prioritize, which is a critical skill. This point goes in hand with the "2-3 pages" item, as they both show that you are able to critically assess the content you produce.
- What you studied in university: I expect people that apply to the positions I filter for to belong into a certain set of domains. This is generally not a very important criterion, but it gives me a better idea of your professional career.
- When you finished your degree: This is used to determine how recent your education is. I consider professional experience once the degree is completed, not while it is being completed.
- Free of grammatical and syntactical errors: Make sure your resume doesn't have major grammatical or syntactical mistakes. Those communicate a lack of seriousness and professionalism that I would expect in your future communication with others in the company. If your resume was initially written in a different language, make sure it is thoroughly translated.
- Github account: If you list one, expect me to look at it. If you don't contribute much (less than 20 contributions per year), then it's simply better not to list it.
- Personal website: If you list one, I will look at it as well. I have a background in web development, so I will use it as an additional way to evaluate it. Make sure it is online. A personal website that is down or for which the domain expired will lose you points.
What is data profiling?
Data profiling is the process of extracting information about data. Given tabular data (think of an Excel spreadsheet), we commonly want to extract the following properties about each column:
- Number of rows
- Number of cells without data
- Number of cells with a value of zero
- Number of distinct/unique values
- Number of duplicate rows
- Minimum, mean, median, maximum, quantiles, range, standard deviation, variance, sum
- Values distribution
- Most common values
- Examples of values
The process of data profiling allows a data scientist or engineer to identify quickly potential sources of problems in the data such as:
- Negative numbers when numbers should all be positive
- Missing values which may need to be imputed or for which the row may have to be removed
- Issues with the distribution of values such as class imbalance if we plan to solve a classification problem
In an ideal situation, data profiling reports:
- No missing cells, this way you do not have to ask if data can be filled in or you don't need to impute the data using assumptions
- Proper normalization of the data (e.g., value separate from their unit), this way the data can be used as-is, otherwise you need to transform the column to extract the numeric value from the unit
- All the data in a column using the same unit, unless otherwise specified (e.g., you do not want data in meters, centimeters, feet or inches in the same column), this way your data is consistent, otherwise you need to identify the scales/units used and transform the data to use a common unit
- Little to no row duplication, this way you know that your data was collected without creating duplicate entries, which sometimes happen when databases are merged manually to create a data file, otherwise you may have to drop the duplicate rows or identify how many of the duplicates should be kept