Autistic person talks about job interviews

Hi everyone!

Earlier this month on Twitter, I asked my fellow Autistics people there what part of job hunting they found difficult.  The majority voted for "Interviews", so for this month, I'd like to share my experiences in interviews.  Hopefully you can find some solace or enlightenment from this post 😊

Content:

1. An overview of the job application process
2. Why do we need to have interviews anyway?
3. Types of interviews
4. When did I feel like the interview went well?
5. When did I feel like the interview went... not-so-well?
6. Did my feeling and the results match?
7. My overall thoughts on interviews
8. Dear fellow autistic people 
9. Final thoughts

An overview of the job application process

Interviewing is a common method of trying to find "the one" potential employee but it's still usually just one part of the job application process.  In most of the companies that I applied for, the application usually consisted of the following stages.

1) Preparing/Submitting application forms (CV, resume, portfolio, other documents)

2) Aptitute test (usually testing basic literacy, numeracy,  and "logical thinking")

3) Interviews (usually 2-3 rounds but it is possible to have more or less)

4) Offer (Hopefully 🤞)

There can also be take-home assignments, assessment centres, casual meetings with human resources (HR) or other employees depending on how competitive the role is and how much the company wants to assess "cultural fit." 

What does cultural fit mean?  Essentially it means that the person's presentation and core values align with the vision and values of the company.  The point of finding people who are a good cultural fit seems to stem from the idea that the company would have the manpower to realize their business plans and create their version of the ideal workplace, and keep their employees happy and keep working for the company.  (Sources:  Business News Daily, BBC) 

Due to the pandemic, most of the job application process (or at least for me) was online in the comfort of my own room, but I did have to go to the office in-person for a few occassions.

Why do we need to have interviews anyway?

From the company's perspective, I believe that interviews specifically would be considered a viable option to assess the following:

• Cultural fit
• Whether the applicant can present themselves as "professional" according to maintream corporate standards
• Whether the applicant can show that they have the skillset that the company wants (including communication skills)

It may seem like there's a power imbalance with the company having more power but I can see how the job seeker would have some use for interviews as well.  As much as the company wants to find someone that fits, so would the job seeker.  I understand that when you're seeking for a job, you can feel like any company would do but if you want to look for long-term employment, it would be desirable to be able to blend in with the working environment and build rapport with the people.  It's also a good opportunity to check whether the company... actually knows what kind of people they want to hire.... (Sometimes the job advertisement may present itself as something and the job itself being something else...)  

Types of interviews

Generally speaking, I experienced three types of interviews.

• HR 
• Usually the first interview
• Could also be a casual meeting
• Usually would look for basic back-and-forth verbal communication skills and whether you can dress and act "properly"
• Technical 
• Usually the mid-level interview
• The interviewer is an employee/manager that has a good understanding of the skills and experience required in the position
• May involve technical tests (e.g. coding tests for programmer roles)
• Would involve an in-depth assessment of the applicant's skillset
• Executive 
• Usually the final interview
• Most likely to assess cultural fit
• May ask the applicant what their career goals are

Not all companies involved all three types of interviews and in this exact order.  Some companies had more interviews and some had less, but broadly speaking these are the sort of interviews you would likely encounter.

When did I feel like the interview went well?

I would get a good feeling after an interview if I:

1) Managed to answer all the questions without getting stuck

2) If the interviewer was friendly 

3) If the interviewer wanted to present the company in a positive light when I asked questions at the end (e.g. good pay, good benefits, positive environment etc.)

When did I feel like the interview went... not-so-well?

I didn't feel so confident when:

1) I got stuck in the middle of the interview

2) If the interviewer seemed to either try to encourage (or rather console) me to "keep trying hard" or if the interviewer seemed less friendly as time went on

3) If the interviewer tried to present the company in a negative light when I asked questions at the end (e.g. too much overtime etc.)

Did my feeling and the results match?

For the most part I felt like the interview went as well as I thought it did (probably 8/10).  That being said, there were times when I was positively surprised and there were times when I thought I did well but ended up getting rejected.  There were also places that didn't even bother to send out a result (which mostly likely means a rejection but how annoying that they don't at least send out an email... lol...).  

My overall thoughts on interviews

Overall, although I felt like it took a while to land that job, I think interviews are here to stay.  It's such a mainstream method in the job application process that a lot of people have had a history of developing.  It seems to serve some purpose from both the interviewer and the interviewee, even if it feels like there is a power imbalance since one party would probably be more desperate than the other.  

My overall engagement and enjoyment of the interview largely depended on whether I could see myself getting along with the interviewer, and also whether the atmosphere of the interview made me more comfortable than nervous.  I tried not to think too much of the outcome when I was at the interview, because I thought it might make it harder to do my best.  The main parts of interviews that I disliked was that as much as I would prepare, it's almost impossible to know what kind of interviewer I would have, what kind of questions would carry more weight, or when I would ponder if I said or did the "wrong thing".  It also sucks if I can't answer all their questions regardless of whether I get the offer or not.

Each company gave me different reasons for their rejections, but it seemed like I generally struggled with showing my skillset in tech roles.  I wasn't that confident in my tech skills (programming, machine learning etc.) since I didn't have a strong computational background (No CS degree for example).  I think that being autistic meant that for me, it was even harder to make confident statements since I have a habit of spacing out mid-conversations and misinterpreting what people are saying. 

In the end though, I'd like to think that I'm going to be at a company that's right for me at present time, since I tried to be as honest as I can  (masking only the bare minimum like wearing a suit).  I got to ask my own questions for the company for over an hour after all haha!

Dear fellow autistic people

I understand that autism is a spectrum and thus there are all sorts of autistic people.  Some of you may struggle with interviews, some of you may thrive, and some may never have been interviewed yet.  Interviews can be tough for anyone, but since autistic people tend to be the minority in thought processes and communication patterns, it can be especially difficult to find that one company that will give you a chance, and one that seems like the right fit.  

From my own experience, I found that I got burnt out quite a bit since I felt like I had to mask more than usual.  I was fairly worn down towards the last couple of months.  I'm glad that I got an offer from a place where I masked the least because that gives me more confidence that I'll be okay there.  Although I suppose time will tell XD

There's no right answer I can give to "how to get a job" or "how to have the perfect interview" but my advice would be to handle job hunting as training/running a marathon.  Just keep thinking about how to present yourself, practice, do the interview, and try again.  When you've finished one interview, move on to the next one.  Take regular breaks, and maybe even longer breaks when feeling worn out.

Wherever you are, good luck!

Final thoughts

I hope you enjoyed this month's article.  There's still more that I could talk about, but I think I've written what's important.  Check out my previous posts!  My past autism related posts are:

• AUTISM AWARENESS (or ACCEPTANCE) MONTH 2021
• Is autism a disability?

What are your thoughts about job interviews?  What did you like or dislike?  Do you think being autistic has its advantages or disadvantages?  Let us know in the comments!

If you want to have a say in what I share next, look out for my Twitter!

See you next month!

Job hunting in 2022 for tech be like...

 Hi all!

I've FINALLY gotten around to writing again!!!  Missed you all loads!

A while ago, I started a poll about what topic to cover next and the majority of you chose "job hunting."

Therefore, I'm going to share my experiences finding a job in tech.  

What kind of jobs did you apply for?

In short...

• Data scientist or analyst 
• Software developer/engineer/programmer etc.
• Consultant 

After doing a research project and a temp job in academia related to big data, I realized that I really enjoy data science.  Initially when I first started job hunting, I wasn't too sure what aspect of data science I would be suited for the most.  The coding?  The statistics?  Being able to make pretty presentations?  And so on and so forth.  

That's why I applied for any tech job related to data science or big data projects.

What aspect of data science were you most equipped for job-wise and why do you think that is?

In the end, I feel like I did better when it came to applying for jobs with a heavier emphasis on "eagerness to learn", as well as having good enough presentation and communication skills to talk about myself and my academic or real-life data science projects in a logical manner.  I got a job offer for a broad tech position at a data science company and their main criteria seemed to be just that.  There was another position where I managed to get to the final interview, and that was the kind of people they claimed to have wanted.  Maybe it's because I was applying for entry-level new graduate positions, but I was honestly surprised with how important it was to show that I am eager to improve myself.

Was it important to look for specific job titles when applying?  For example, if I wanted to become a data scientist then should I only look for advertisements specifically titled "data scientist wanted?"

Actually I found that in the world of data science, it was important to have a look at the job description rather than the job title itself.  When I was applying, I specifically looked for job titles labelled data scientist because the term itself was booming online through articles and YouTube videos titled "How to become a data scientst" etc.  Eventually I realized that different companies used that title to recruit people in part because of the hype surrounding the term but in reality wanted something else like programmers for app designs.  Then I started expanding my search for any broad tech position that uses big data in some way shape or form.  It still wasn't a perfect filter for the "ideal" job but the job hunting process became more effective. 

How did you hand in your applications?

For some companies I applied through the company recruitment website or web page, and for others I applied through a recruitment agency.  This agency specifically helps students or graduates from graduate/postgraduate school find a job at a company that wants such people.  After browsing through online reviews, I felt that as someone who has a Master's degree in a STEM field looking a tech position, it would be a good fit for me.  My agent was a lovely person who helped me with every aspect of the application process (writing resumes and preparing for interviews tailored to the company) that I felt supported for the most part.  I ended up taking a job through the agency anyway.

What was the application process like?

Any combination of resume screening, interviews (online or in-person), screening tests, coding tests or take-home projects.  It really depends on the company but in my experience I usually had to hand in my resume, then take a screening test, then have 2 or 3 rounds of 30-minute to 1-hour interviews then wait for the job offer.  It might be best to share what each individual step was like otherwise this is going to be a loooooong post haha.

How many jobs did you apply for before getting a job offer?

About 30.  I managed to get to the interview stage for about 20 of them, then got to the final interview stage for 2 then got 1 job offer.

What was the toughest part of job hunting?

The rejections.  Sometimes it was because I really wanted the job, but for the most part it was because of the accumulating thoughts of "maybe I'll never find a job" and "I put so much effort... sigh..." that built up a lot of stress over time.  Towards the final couple of months, I had trouble sleeping and was gettting tired throughout the day.  Once I burst out crying in the middle of the night randomly.  If there's one thing I took from the negatives, it's TAKE CARE OF YOUR HEALTH.  The basic advice of getting regular exercise, taking care of other aspects of life as well, and taking regular breaks apparently applies to job hunting as well.  As someone who reached this state after 30 job applications, I honestly think those of you that went through hundreds of applications.... are true soldiers in the modern world!!!!

On a lighter note, did you find anything enjoyable about job hunting?

Being able to meet so many people in various businesses, companies, positions and different personalities.  It's such a wide world out there, and it was enlightening to know that there's a lot more that the universe has to offer than what I've been used to.  Even though the rejections were hard, it was also one step closer to learning about what kind of job and environment would be suitable for me and what kind of people I can get along with.  In the end, companies and industries are made up of people.  Try and find the kind of people you can see yourself working with (and hopefully enjoy working with) because it would probably make your job hunting more enjoyable.

Any final words?

The biggest piece of advice I can give, is if possible, try to find someone to job hunt with.  It can be useful for practical reasons like being able to view yourself more objectively (finding strengths or weaknesses), practicing interviewing, and for emotional reasons like being able to share your ups and downs during your job hunting experience.  Might not be nice to hear if you consider yourself to be a loner, but I honestly felt relieved sometimes knowing that I can talk to my agent or my friends and family during this time.

Is there anything else you'd like me to share?  Comment down below!

Also if you want to have a say, I'm usually most active on Twitter.

I hope you come back here for the next post!!!

Merry Christmas and a Happy New Year!!!

 Hello, my readers

It's been a while since my last post on this blog.  Honestly I wasn't sure what sort of topic I should write about and I've been pretty busy, but in the spirit of Christmas I felt to talk about a fairly big transition in my life:  Venturing into the world of finance from medicine.  If you're about to transition into a new chapter of your life, or you think you might, maybe you can find some enjoyment in this post.

If you read my About Me page, then you'll know that I completed my secondary and tertiary education in medical research (especially in neurology) with a final Master's dissertation about using big data for dementia research.  Since then I've been working in a university lab studying topics that exist on the intersection of medicine and business.  Without giving away too much due to privacy concerns, my daily tasks consist of collecting and processing data related to healthcare.  Initially I was considering starting a PhD about utilizing data science for neuroscience research, so this job was the perfect opportunity for me to develop my academic skills while I was applying for PhD places.  In the end, I did get that PhD opportunity but by that time I started feeling like I was getting complacent in academia.  I wanted to try and start a new chapter of my life into the corporate world.  Is academia really where I want to be?  Would I actually prefer working in a company if I tried it?  Is medicine what I really want to pursue a career in?  I just couldn't shake those feelings.  

After 4-5 months of job hunting, I ended up accepting a tech position at a FinTech company.  To be perfectly honest with you, when I first saw this job advertisement, I didn't think I would get the job simply because of my educational background.  One of the topics under research at the lab where I work can cover healthcare finances, but I didn't have any formal education in finance nor a very strong academic foundation of engineering.  Even my family members felt it would be difficult for me.  I still applied for the position because I was really interested in it, and I'm so glad that I did!  Apparently the company thought that I was a strong logical thinker and felt my interests and observations regarding digital monetary transactions were genuine and I would be self-motivated enough to grow myself into the role.  Due to the job requirements, I've been studying for a securities broker qualification (which I passed yay!) and I learned A LOT about money, trust funds, stocks, bonds etc.  In the past I would not have ever dreamed of being able to understand why Wall Street crashed, but now I know that it's because of messing around with the derivative market!!  That's so cool to me that I can say that now!  I still like medicine and will continue to learn and read about topics I find interesting (most likely neurodiversity), but I just love that I can still learn so much about the world and start my new journey in the fabulous world of FinTech.  

If you're at a point in your life where you feel like you want to make a change, maybe go ahead and try!  Figure out what you can do and what you need to be able to do to make those changes.  It might take a long time, it might be hard work, but it will never happen if you don't try.  Finally, rejoice the year with a Merry Christmas and may you all have a wonderful New Year!!!   

Kind regards,

Lukas Fleur

P.S.  I started advertising a magazine called North Wing Magazine (the logo in the top right hand corner).  It's a medical magazine primarily run by students hoping to spread awareness regarding medical and healthcare related topics, especially in the UK.  There's essays, articles, or simple pieces written by some amazing people!  If that's your cup of tea, check it out!

UPDATE: I HAVE RETURNED!!!

 HELLO WORLD!

For those of you who are new here, welcome!  I'm glad that you decided to have a look at my blog 💕 If you've been following my blog, thank you so much for returning!  I've been away for quite a while now (so long for my New Year's Resolution to post every month LMAO) so I decided to give you an update on what I've been up to and what I plan to do with the blog from now on.

In my last post, I talked about getting a PhD offer.  Due to... circumstances (The years 2021 and 2022 have continued to be quite a wild ride 👀) and some considerable thought about my life, I decided to apply for a jobs.

Specifically, I wanted to see if I can start my career as a data scientist in industry.  I've always wanted to start a career in industry but I decided to apply for a PhD because: 1) I wasn't sure if I could get a data scientist job right away after my Master's considering that I have a degree in Clinical Neurology and not Data Science/Computer Science etc. AND 2) I thought that having a PhD would make me more employable.  I eventually received an offer for a PhD, but the course was going to start a lot later than I expected, so I thought, "Why not try apply for data scientist positions and see what happens?"  If I can start my career now, I won't need to go through a PhD.

During the time I've been away from the blog, I've been going through the motions of preparing resumes, attending company events, networking, preparing for interviews etc.  I'm happy to let you know that I accepted a job offer at a company that offers a training program so that new employees can confidently grow their skills needed for the job.  I'm so excited to start my new career!!!  

For the forseeable future of this blog, I'm thinking of sharing my experiences related to 1) applying for jobs; 2) differences between PhD and job applications; 3) challenges and tips for applying for tech positions when not having a tech or heavily quantitative educational background AND MORE!!!  Hopefully focusing on writing story times and articles can help with producing content consistently.

Thanks for reading!

Story time: That time an international student applied for a PhD for UK/settled status students (PhD in UK)

 Welcome back!

And... I know that in last month's post I announced that I would make a Part 2 for Project 8: Logistic Regression but due to life being really busy for me at the moment, I've decided to make a chill story time about what happened when I, an international student, applied for a PhD in the UK even though the position was advertised exclusively for people from the UK.  Hopefully y'all will like this, since my last story time was one of the most popular posts in this blog. 😌

Alright, cut to the chase.  Did you get in?

Unsurprisingly, YES I DID 😆

What was the PhD about?

The PhD was a fully-funded (Well... sort of.  More about this later) bioinformatics project that includes a placement overseas.  

What made you decide to apply for a PhD for locals when you'd be an international student?

I already had a good working relationship with the supervisor and they encouraged me to apply.  I didn't have the most traditional educational background when applying for data science-related PhD projects which put me at a disadvantage when applying for larger programmes.  Since they already knew that I had prior research experience in bioinformatics, it was easier to convince them to take me and provide advice regarding PhD applications specifically tailored to me.

Would you say that knowing the right person that can give you an "in" is important when applying for PhDs?

Absolutely yes.  If you're interested in applying for PhDs, then the first step is to look for a supervisor that 1) is capable of supervising PhD students; 2) currently conduct research related to what you're interested in; 3) someone who you like personally (or at least can work with professionally).  My advice is to treat PhD applications much like job applications.  Now that I think about it, the general flow of a PhD application probably deserves it's own full article... 

How come you didn't apply for a PhD in your home country?

I already did my Bachelor's and my Master's in the UK.  I felt it was more straightfoward to apply for a PhD in the UK since I was quite out of touch for applying for grad school in my home country.

Would you recommend international students to apply for PhDs not intended for international students?

Generally speaking, I'd say no.  I mentioned before that the PhD was fully-funded, but only for local students.  I was quite lucky that I would still receive partial funding but I was told that I had to cover the rest of the fees (mostly tuition fees because that's hella pricey 😧) This is something that I think most people outside of grad school don't know, but funding means A LOT to university researchers.  I'm not just talking about PhD students, I'm talking about post-grads and any academic without tenure.  If you're applying for PhDs, there is IMMENSE pressure on you to get full funding that covers all of your tuition fees, living costs, travel, and anything else related to your research.  Keeping that in mind, international students are at a great disadvantage because there's larger fees to cover and fewer opportunities to get funding.  If you can get a PhD position that offers full funding at international student rates then TAKE IT!!!  (That is if you want to do a PhD as an international student in the UK, of course)

Wow!  I guess there's quite a lot of ground to cover when it comes to PhD applications.  I thought that a PhD is like an extension of school.

😆😆😆 

Final thoughts

I hope you all enjoyed reading about my experience applying for a PhD.  If there's anything you'd like to know more about, please comment down below and I'll consider your requests!  Next time, I (hope to) will write about Project 8 Part 2.  Check out my past posts in the archive section to see more of my works.  I'm semi-active on Twitter so if you're interested in my daily tweets, please follow me! 

Project 8 Part 1: Logistic Regression - Python

 Welcome

Hi again, hi again!  If you've been catching up with my blog, thanks for your continuous support 💓 If you're new here, thank you for giving my blog a chance 💕 Since I started learning R, I've thought about making code comparisons between Python and R.  Concidentally, I've also started learning machine learning so I thought... why not try and compare machine learning codes between Python and R!  So far, I've learned how to build logistic regression models using Python and R.  Project 8 is divided into parts 1 and 2 where the codes using Python and R will be described respectively.

I will be using the Iris dataset to demonstrate how the codes work👍 If you're someone who requires assistive software to read, I suggest downloading the PDF documents to read the codes.

Python - Jupyter Notebook

For this project, I built a logistic regression model using sklearn.  For starters, the packages I used were Pandas, Numpy, Scipy, Sklearn, and matplotlib.

(Click here for the PDF version of code:  import packages)

Sklearn allows us to import some of the most famous datasets when learning data science.  For this project, I imported the Iris dataset and included data under the columns sepal_len, sepal_wid, petal_len, petal_wid, and class.  The NAs were dropped and empty lines were removed.  (Note:  This section of the code is based on the work of Srishti Saha from GitHub)

(Click here for the PDF version of code: import Iris dataset)

Just type in iris_df to have a look at the dataset!

In order for the model to work, we have to make sure that the variable that we want to predict, in this case "class", is an integer.

Just type in iris_df["class"].dtype to confirm!

In order to make a model that predicts Y ("class") from X ("sepal_len", "sepal_wid", "petal_len", "petal_wid"), then both X and Y have to be turned into arrays.

(Click here for the PDF version of code: convert to arrays)

X had to be rescaled so that the maximum value becomes 1 so that we can produce Y which will be returned from a value within the range of 0 to 1.

(Click here for the PDF version of code:  rescaling X)

In order to test the model, the data was split into a training set and a test set.  The training set provides information for the model to "learn" how to make classifications.  The test set makes sure that the model can actually make classifications and is useful for finding out how accurate the model is.  In this project, I split the data so that the training set contains 80% of the data and the test set the remaining 20%.

(Click here for the PDF version of code:  train test split)

I made sure whether the data was actually split into 80:20.  The full dataset has 150 data points.  The train set has 120 data points and the test set has 30 data points.  Since 150 x 0.8 = 120 and 150 x 0.2 = 30, the splitting was performed accordingly.  

(Click here for the PDF version of code:  Confirm split)

The logistic regression model was made using the train set.

(Click here for the PDF version of code:  train the model)

This is what happened when I tried to test the model on the test set.

(Click here for the PDF version of code:  test the model)

You'll see a big array of decimal numbers ranging from 0 to 1.  Logistic regression provides an outcome of the variable class as either "Yes" or "No"... kind of.  What this model really does is provide the probability that class would be "Yes".  The closer the Y value is to 1, the higher chance that class is "Yes."

How useful is the model?

So now that I have the predictions for class based on the other variables in the Iris dataset.  How would I know how accurate the predictions are?  One way is to use the Jaccard index to produce an average percentage of how similar the actually Y values were vs the predicted Y values (called Yhat). 

(Click here for the PDF version of code:  how good is the model?)

The Jaccard index was 0.825.  That means that the model produces results correctly 82.5% of the time.  You could interpret that as "1/5 of all cases could be wrong" or you could say that "it's a whole lot better than a 50:50 chance!"  Personally, I think that 82.5% is a pretty solid number considered that it's a pretty small dataset!

Final thoughts

Thank you so much for reading!  Making this post was actually a lot of fun and I hope you all enjoyed it ❤ I feel like knowing that you are out there reading this blog keeps me motivated to keep on coding 😀 Next time, I will be showing that making the same logistic regression model would look like when using R.  Until then, please feel free to read my other posts in this blog.  If there's anything you want to say about this post, comment down below!

Happy New Year!!! (How I got into R storytime...)

Welcome back to my blog as we enter 2022!

Happy New Year!  I hope you all had a lovely winter 💗 I know I've been a bit lazy with my blog in 2021, so for my New Year's Resolution, I will write one blog post per month and deliver consistent content for you to enjoy.  As promised in my previous post "Is autism a disability?", I'm going to talk about how I ended up learning R and why some of you might find it useful (hint: data scientists and data analysts).  There will be some R-related content from now on as well as Python and neurodiversity as before!

What is R?

R, like Python, is a programming language.  The main difference is that instead of being able to do a bit of everything, R is mostly used for statistical analysis.  It is a language developed by statisticians for statisticians.  Much like most Pythonistas use Jupyter notebook as an editor, R programmers use RStudio to write code and import packages.

How I ended up learning R?

I got new job!  Yup, that's right.  After I finished my Master's I ended up working at a research lab involved in data science where I have to use R.  Necessity is a good motivator for... everything I suppose haha.  

Was it easy to learn?  How hard is it?

Personally I found it fairly straightforward to learn.  Already knowing Python, I was fairly comfortable with programming concepts and as someone who has been in the STEM field throughout my education the statistics wasn't too hard to grasp.  It also helps that I'm a massive math nerd and did a computational project for my dissertation 😅 This was the first time that I've used a book to learn how to code.  I'd say pick a book that's essentially a "Book for Dummies" that describes all the steps starting from installation of R.  Of course, I tested out the codes from the book to see if it actually workers on my computer and not just read it.  The thing that I've found with any kind of programming is that you just have to start and make new programs and you'll get from A to B at some point.  Once I was done with the basics, I started making new codes.  StackOverFlow has been particularly useful whenever I got stuck.  There's always someone more experienced than you!  Especially if you're just getting started.

Who would find R useful?

Most likely if you're in the data science field, R is a useful programming language to learn.  R is designed for statistical calculations.

Which do I prefer:  Python or R?

Long story short:  it depends.  If I want to do some heavy statistical analysis, calculations, or data visualizations, then I prefer R.  Generally though, I prefer Python because Python codes are easier to read (especially for machine learning related codes) and it's like the "jack of all trades" kind of programming language.  R is useful for reading Excel or CSV (UTF-8) files but Python can import other more "minor" types of files as well.

Final thoughts

Thanks for reading until the end of my post!  Since the majority of you wanted to read a story time about me starting to learn R in a twitter poll, I decided to make a post about it.  (A lot of you told me to take a break in December as well so I took your advice and resumed writing in January LOL)  I haven't yet decided on next month's topic but I'll let you know via social media!  In the meantime, as always, please check out my other blog posts!!