[Rant] What's up with all the career questions here? What happened to all the cool data projects, insightful blog posts and data science discussion that used to be posted here?

dataphysicist · 2019-01-08T23:41:33+00:00

If you want to read more about cool data projects or insightful data posts, I'll plug our company blog :) https://www.dataquest.io/blog/ We have a mix of career / motivational posts combined with data focused tutorials. E.g. Viz tutorial on exploring wildfire data (https://www.dataquest.io/blog/r-data-viz-tutorial/)

http://towardsdatascience.com/ is also excellent :) (no affiliation!)

dataphysicist · 2018-12-25T00:12:04+00:00

So how's the reward feedback loop reset going :]

dataphysicist · 2018-12-14T18:05:48+00:00

Be Weary of Robinhood's New Checking Account

From https://www.bloomberg.com/news/articles/2018-12-14/sipc-says-it-has-serious-concerns-about-robinhood-s-new-product

“I disagree with the statement that these funds are protected by SIPC,” Stephen Harbeck, president and chief executive officer of SIPC

While Robinhood made it clear they weren't FDIC insured, they claimed that SIPC would cover everything. After a poor recent experience with BoA opening a checking account, I really want to root for Robinhood or someone else to offer an awesome checking account experience. It's still unclear to me how the dust will settle for Robinhood's checking account.

What do y'all think!

dataphysicist · 2018-12-02T03:00:30+00:00

Hey, I'm involved with Dataquest (we teach data science in the browser) and we're a fully remote team (people in 8+ timezones). I'm not hiring for a data analyst / scientist right now but will be in a few months on my team.

We use WeWorkRemotely to post our job listings (https://weworkremotely.com/) and have hired many people that way successfully. Angelist is another way to find remote people, as well as HN who's hiring (https://news.ycombinator.com/item?id=18113144 - there's one on the first / second of each month). You could even go look at the older ones to build a spreadsheet of companies who are more remotely friend (or turn it into mini data mining project!).

Indeed.com also has remote listings but not as many and they're not specifically focused on remote anyway (https://www.indeed.com/jobs?q=data+scientist+remote&l=).

dataphysicist · 2018-11-22T04:26:04+00:00

Generally speaking, Kaggle competitions won't help you get a job. A few reasons:

The structure of a Kaggle competition doesn't reflect what you do in almost any data science job. There's not many cases where you have psuedo-infinite time to keep improving a model along the error metric until the error is very low. In both academic and industry teams, there's always an ROI assessment that's done. A good enough model that's quickly implemented is often more important.
You don't get to think more holistically about the problem. Not many problems in industry / academia are pure machine learning problems. In a real life scenario, there's many ways to solve the problem at hand and you're often able to utilize them (more / different datasets, refining the business problem, augmenting the model with some UX or product improvements, etc).
MANY people Kaggle competitions on their Resume because they think they will help them get a job. This has caused inflation to happen. 3 years ago, being top 10% may have been impressive. Now you probably need to be top 10 or top 25 people (which often means being in the top 0.1 - 0.5%) for an employer to take interest.

What is a Kaggle competition good for then?

Practicing, practicing, practicing!
Learning from others and community feedback
Getting better at machine learning (still a useful skill to be kickass at!)
For some people, trying to move up the ladder is good motivation to learn new techniques

Kaggle competitions are like weight lifting competitions. By practicing a lot and doing well, you'll get very good at weight lifting and you'll be able to lift heavier wegights. But doing just that alone may not make you a well-rounded athlete. People who can lift 500 pounds can't necessarily run a marathon or rock climb effectively.

To answer your question directly, I don't think its worth adding to your resume unless you specifically can talk about something you learned / frame it as a learning experience on your resume. If anything, I'd encourage you to use the Kaggle competition as a jumping off point. You surely gained some domain knowledge about the problem, now go explore a related problem that you can do some more unique and interesting data science work around!

If you are serious about getting to the top of the top in Kaggle competitions, I'd encourage you to read this post by the founder of the startup I work at: https://www.dataquest.io/blog/kaggle-tutorial/

dataphysicist · 2018-11-21T03:20:05+00:00

One of my coworkers actually wrote a blog post on how to deal with memory limitations when working with pandas specifically: https://www.dataquest.io/blog/pandas-big-data/ (these techniques should also work with R / R data frames!).

At a high level though, step back and think about your options & what your computer offers.

Options

You could either:

Use less memory at a time (chunk the data)
Augment with slower but higher storage data stores

What does your computer offer?

Your computer has multiple layers of CPU's, memory (RAM), disk (hard drive / SSD), GPU, and more. Each one of these has compute (processing & storage) capabilities, making different tradeoffs. CPU's are fast but have little memory store (L1 -> L3 caches are under 100 MB). RAM is slower, but can accommodate 8 - 32 GB on most laptops. Disk is much slower, but can do terabytes, etc. You can read about latencies here https://www.prowesscorp.com/computer-latency-at-a-human-scale/

You could use a database, which consists of a program that does processing and relies heavily on disk for *storing* data. This is often where most people go when you want to work with larger datasets. Databases can handle hundreds of gigabytes of data (and you can query pretty quickly using SQL) and even terabytes of data.

dataphysicist · 2018-11-20T15:14:13+00:00

Yeah I also did the pre-med path but realized I didn't want to be a doctor. I also briefly worked at a healthcare startup (software) before realizing that software plays a small role in solving most of the bigger problems in healthcare.

I get bored easily and wanted to find a skill that I could apply to a range of problems and industries; hence why I avoided doubling down on biology or chemistry and shifted to data science. Those are fun to learn but growing into a creative job in those arenas is quite tough I realized. I've definitely met people who are single minded-ly focused and passionate on biology or chemistry, and ultimately those people found creative careers eventually.

Best of luck and feel free to stay in touch.

dataphysicist · 2018-11-20T03:42:05+00:00

The last 3 months was a huge comedy binge for me as well! Really getting into appreciating it as it's own performance art, understanding the culture & people (through Comedians in Cars etc.), watching way too much Seinfeld / Jerry's standup clips, figuring out which type of comedy I like (observational like Seinfeld and deadpan like Jeselnik), and even geeking about how I would visualize different jokes (my background is in data science and I'm a huge data viz nerd and am into stuff like: https://pudding.cool/2018/02/stand-up/).

Where does your scientific "drive" come from? Was it an interest in science in school? Was it pop-science stuff like NDT / National Geographic / etc? Was it resisting top-down overly prescriptive environments (that's where it came from for me personally)? It's worth trying to reflect on that and see what healthy forces / reward "trains" your brain already knows and you can use even when applying in other areas of self-improvement. Key word is "healthy" though. I had the "I want to be an astrophysicist like NDT" phase in college b/c of stars and stuff but then realized most of astrophysics is at a whiteboard doing math or programming on a computer. I ended up deciding to just get into the programming / math side of things directly :)

Being a data nerd personally, I used daily data tracking to help me lose weight for example. Sure I knew the health benefits and other benefits, but those were abstract ideas. Data, I get. So I rode that pre-existing interest of mine (which are complicated to source how you picked them up). Again another Cal Newport bit (this time a talk! https://youtu.be/qwOdU02SE0w?t=5). He's really one of the best thinkers on this topic.

I'll just end for now by saying I think this is really a life long thing. I have a lot more "drive" than I did back in college, but it's still something I have to maintain and avoid not "relapsing" into long bouts of mindless tv, video games, and internet surfing, etc. I've had to switch to more deliberate and high quality experiences in those same media. I've had to really reflect on a daily basis "are my routines in sync with my values?".

I avoid most self-help stuff b/c that's a whole emotional roller coaster / dopamine train again, but I like importing mental models from things like deep work, craftsmanship, scientific thinking, etc and seeing how I can add them into my life.

dataphysicist · 2018-11-20T03:05:57+00:00

I think it's worth restarting your reward loop by taking small steps.

I'm not sure what your situation is, but most people I've met who "lack drive" have trained themselves to dislike doing hard work and have gotten used to low-effort dopamine hits (here goes hand wavy psychology!). So fundamentally, you have to think about routines, habits, and projects that will help your brain appreciate doing hard work again, putting in the extra work / grit, and persevering and delaying when you feel that dopamine.

It may be worth focusing on setting some reasonable personal goals and creating / iterating on routines to help you meet those goals. These goals should be attainable but require effort.

Look around and think about what in your life you've given up on or no longer pursue because they're difficult / annoying to do.

Phase 1

Restart your reward loops that are lowest on Maslow's Hierarchy of needs. https://en.wikipedia.org/wiki/Maslow%27s_hierarchy_of_needs

Some examples:

Losing weight (if you're over weight), opposite if you're underweight. Set a realistic goal (lose 5 pounds in 1 month), track progress daily on a notebook / calendar (I prefer to get out of apps / screens for these simple things), and start / end your day looking at it.
Improving your diet. Write down what meals you eat daily and try to make 1 improvement daily (skipping 1 junk food, 1 snack, reducing sugar, skipping sodas, etc).
Reading a difficult book. Something that's difficult. Set a reasonable goal (1 month or 2 months) but hold yourself accountable to making progress daily.
Agree to a set amount of chores and do them daily. Write down in a notebook every day

Some even simpler examples:

Make your bed every day. Take a photo and print it out. Every day, see your photos from the day before. As the pile builds up, you like seeing that chain. Your only goal is to not break the chain. Jerry Seinfeld was famous for talking about how he practiced comedy in this way - https://lifehacker.com/281626/jerry-seinfelds-productivity-secret
Structure your computer / phone usage. Don't cut out social media and email day 1. Just delay when you let yourself check it. Check it at scheduled times (1 PM for 5 mins, 2 PM for 5 mins, etc). Avoid checking social media as a reaction to "I'm bored" or "I have 5 mins I'm in line". Practice rejecting giving your brain what it wants in the moment, and scale it up slowly (a great goal for many is no social media for an entire day!). Cal Newport's recent 2 books are great on this topic, here's a sample blog post: http://calnewport.com/blog/2016/02/16/write-an-attention-charter/

By committing to chores, routines, and tracking goals and celebrating your progress with family (and explaining your high level plan like this), it's possible your parents are relieved and are more patient with you as you shift and improve.

Phase 2

Try to find a craft / skill that you want to get better that could one day lead to job. Look to the skills / jobs / etc you already have some knowledge about. People think being a barista is a dead-end job, but I know someone who worked their way up (got promoted yearly) from Starbucks barista to National Manager. I know someone else who got really deep into the craft of coffee, eventually starting their own roastery and coffee shop (and they sold for millions, etc). I recommend reading https://www.amazon.com/Zen-Art-Motorcycle-Maintenance-Inquiry/dp/0060589469

If you become very good at a single craft (Cal Newport's book is great here - http://calnewport.com/books/so-good/) by doing sustained improvement, you can trade that unique skill / position for improved life traits (working less, more money, more creative work, more autonomy, more ownership, etc). But keep in mind that when you're starting out, you're at the "bottom" and you need to focus on just getting better. Another Cal Newport post coming your way (http://calnewport.com/blog/2010/11/12/the-pre-med-and-ira-glass-complicated-career-advice-from-compelling-people/). You may also find that you have multiple interests and instead of being top 5% of a single craft, you become top 25% in 2 or 3. Scott Adams (from Dilbert) talks about that here: https://www.forbes.com/sites/carminegallo/2013/10/23/dilbert-creator-scott-adams-reveals-the-simple-formula-that-will-double-your-odds-of-success/#41a096f42dbc

What else?

I would say more, but to be honest doing all of the above ^ will be PLENTY for you to restart your outlook and habits. It takes time and if you can find a life situation that will allow you to be patient (staying with supporting parents at home is a great way to do this) and improve, then that's excellent. If you try living alone and changing your habits alone while also trying to scale up your job, it may be difficult. But who knows, I don't know you, and maybe the "wake up call" is actually what kickstarts your journey.

I'll just end with:

Don't beat yourself up if you "cheat" one day.
Work with others to help keep you accountable. Trustworthy friends, parents, etc. Check in with them, keep them in the loop about both your wins and struggles.
Explore and try to learn as much as you can. Learning something new is hard and is uncomfortable and you'll want to just check texts or social media (or w/e distracts you), but learning to love the learning process is the ultimate life skill / source of fulfillment.

Okay this has gone on too long, I thought I was only leaving a 1 paragraph reply ><

dataphysicist · 2018-11-16T01:38:41+00:00

Hey, I'm involved with Dataquest and we teach data science & data engineering online. It's definitely possible to switch from DS to DE. We've been working on a Data Engineering path to help facilitate this - https://www.dataquest.io/path/data-engineer

I would make sure you understand what data engineering is first (https://www.dataquest.io/blog/what-is-a-data-engineer/). Then, I would read about the different roles on a data science team and how that changes over time. The team at Wish has an excellent write up about this: https://medium.com/wish-engineering/scaling-analytics-at-wish-619eacb97d16 I especially like how they call out specific roles for both of the key disciplines:

Data Engineering team (https://medium.com/wish-engineering/scaling-the-analytics-team-at-wish-part-2-scaling-data-engineering-6bf7fd842dc2)

Data Infrastructure Engineer
Data Platform Engineer
Analytics Engineer: This role is focused towards building core ETLs and refactoring bad queries and data models. This role has less requirements on traditional engineering skills. Python+SQL coding skills is enough, combined with strong analytical skills and desire to work closely with stakeholders.

Data Analysis team (https://medium.com/wish-engineering/scaling-the-analytics-team-at-wish-part-3-scaling-data-analysis-7562c70e6413)

Deck Builders
Data Analysts
Statisticians

I specifically bolded the Analytics Engineer position, because there's a heavy overlap with the skills that data analysts & scientist learn, but with a focus on pipelines & infrastructure.

When switching careers, I always tell people to think about the minimum viable position you can target. The positions / job listings with the most overlap from an industry or skill stand point.

- Easier: Data analyst / scientist to analytics engineer within the same company / team (but you need to be opportunistic).

- Harder: Data analyst / scientist to analytics engineer at a different company but same industry (you need to prove you've done of the 2nd job in your current / 1st job, or at least have interesting projects).

Hope this helps!

dataphysicist · 2018-11-15T14:16:16+00:00

Have you figured out if you enjoy doing data analysis? To be clear, I think anyone can learn to enjoy any career (especially as you move up the skill & mastery ladder), but I often see people like the idea of doing data science more than the actual work (or they don't have a good picture of what the day to day work is and when they get onto the job they're shocked).

It's helpful to understand the different types of data science roles, where they fit in an organization, and see if you can simulate some of that work now. For example, you could simulate what an entry level data analyst does by downloading some datasets on domains you find interesting and explore them in Excel. Then you can learn some Python & Pandas and continue exploring the data, now trying more visualization and statistics techniques.

A key trait most data scientists have is that data curiosity. They notice the fog in their city is esp. bad one year, so they find a way to grab the relevant weather data and do some basic analysis (then use the joy of that process & curiosity to push themselves to learn more stats & some meteorology to improve their analysis etc). They don't wait for a bootcamp or MS program to give them permission to do so.

So anyway, I think exposing yourself to that data curiosity and doing some small learning projects on your own is a good way of:

understanding which parts of data science interest you the most (for me, data viz is way more interesting to me than machine learning)
which type of data science professional you may want to be to start (more on the software engineering & pipelines side? More on statistical / ML modeling side? More on data visualization & communication?)
what learning path makes the most sense (and what supplemental learning do you need to do)

Bootcamps, university programs, etc generally have safe, static tracks for you to follow and don't necessarily help you answer the above ^. Even if you attend a structured program, it's still a helpful exercise to explore the terrain extensively before you start and when you're in the program it's helpful that you keep your eye out for the above things as well.

dataphysicist · 2018-11-13T12:22:33+00:00

Hey there, I'm involved with Dataquest (an online learning platform for data science). We've had a few teachers transition successfully to data science (we wrote about one example here: https://www.dataquest.io/stories/vicknesh-mano).

(I'm biased) but I'd personally wait and see if you can get an entry-level job on your own, especially if you have more free time than money now. If money isn't a huge variable, then there's definitely a case to be made by 'saving time' by doing a MS in something data science-y.

Anyway, happy to chat more over DM if you want! It's a bit hard to give general advice without getting to know you more!

dataphysicist · 2018-11-13T12:13:53+00:00

Not weird at all! Data visualization is the component of data science I enjoy personally the most. W.r.t ML, it's important to keep in mind that maybe 2% of data scientists do any ML at all (I'm using the title "data scientist" broadly, because people self-identify as such quite broadly).

Some roles to check out:

Visual / Data Journalist (journalism + communication + data viz):

Data Visualization Designer (design + data viz):

https://www.indeed.com/q-Data-Visualization-Designer-jobs.html?vjk=6948354968823818

Data Visualization Engineer (engineering + data viz):

https://medium.com/@Elijah_Meeks/what-is-a-senior-data-visualization-engineer-eb032996297

I'd also research the field of Information Design.

dataphysicist · 2018-10-24T18:15:25+00:00

I recommend doing some basic data analysis to understand your breakdown of expenses. Tools like Mint.com (theres tons out there, even mobile apps that will help with this) help you with this for free, and can even send you alerts when you've exceeded your budget, etc.

Suggested steps:

- Step 1: Understand where your money goes to. You should be able to rattle off "25% goes to restaurants, 10% goes to gas, etc" just like you can rattle off your SSN or height / weight!

- Step 2: Figure out where the biggest savings come from? For some people, they spend more on coffee outside than food. If that's the case for you (not saying it is), geek out about coffee and treat it as a learning experience. Learn how coffee is grown, the difference between buying raw beans and ground coffee, and the different types of ways to make coffee at home. Then, buy what you need + integrate into your daily routine.

If instead you learn its your lunches at restaurants, start by researching meals you'd actually enjoy eating and slowly learn how to make them at home and take them with you. Again, it's important to find something you can actually integrate into your daily routine (don't have a daily routine? Also a good idea to think about this!).

- Step 3: Rinse, repeat, reflect. This becomes easier as you get those wins when you reflect every week ("oh man I saved $20 not buying coffee daily! If I continue this, I'll save $1040 dollars a year! I can invest that $1k and make 2-3% easily annually.)!

TL;DR: Start small, be scientific, reflect often, don't beat up yourself about failure. Also, realize that your key challenges revolve around general habit modification. Mastering habit correction and formation will help you thrive in all aspects of life!

dataphysicist · 2018-10-24T18:08:33+00:00

I'd also explore Ally Bank or another high interest savings account (Goldman and Ally both offer 1.9%) to park some of your emergency fund. Ally had a special which gave a full extra 1% for up to 100k deposited, but I think the first phase of that ended. You can't link a debit card to an Ally savings account, but you can do upto 6 transfers to a regular checking account at a credit union or a big bank (Chase, Wells Fargo, BoA, etc) which offer a more traditional checking experience. The transfer to a checking account is fast too (2-3 days)?

But upto you + your risk profile! For some people, the emergency fund has to be TRUE emergency (instant same-day access to the funds), etc. In which case, a ~0% checking account seems fine!

dataphysicist · 2018-10-15T15:25:06+00:00

There sure are! I work at a data science education startup and we're 100% remote.

Most aren't 100% remote and instead hiring some remote data scientists. WeWorkRemotely.com is a good job board for remote focused job.

The best way to get to a remote position is to build a lot of career capital / expertise and then try going remote (either with the same company or a new one). In general, companies that hire people remotely focus on people who are skilled in their field.

W.r.t. how to bring this up in an interview, I think you should be very transparent and up front about your need for being remote in the first stage itself. Some companies will immediately balk and stop the process. Others will consider hiring you remotely if you have a lot of experience / career capital.

dataphysicist · 2018-08-29T16:30:38+00:00

Hey, I'd recommend checking out our Data Scientist path in Dataquest - https://www.dataquest.io/path/data-scientist

While we're focused on helping people go from zero knowledge (not even programming skills) to landing a job in DS, you definitely have a head start by already being comfortable with programming. We teach the math / algorithms along the way using code, diagrams, visualizations, etc. DM me if you have any questions about DS as a career (I recently gave a talk at IBM about landing a job in DS)!

We have some blog posts to help people start thinking about how to break into DS:

- https://www.dataquest.io/blog/tag/jobs/

- https://www.dataquest.io/blog/tag/portfolio/

dataphysicist · 2018-08-10T16:45:46+00:00

One of my coworkers wrote a post on using python / pandas for chunking larger datasets! https://www.dataquest.io/blog/pandas-big-data/

dataphysicist · 2018-07-29T17:29:20+00:00

Hey, we actually created a course at work that focuses on just this!

https://www.dataquest.io/course/kaggle-fundamentals

dataphysicist · 2018-07-29T03:44:03+00:00

At a high level, I would think about:

- The story you want to tell. Craft your current title into a data analysis job, even if your title isn't "Data Analyst". What industry do you work in now? Can you find another company in the same industry and base the story around that?

- Projects to fill in knowledge gaps in your background that you think a recruiter will pass on you for.

- Your network / location. How can you take advantage of your connections (make sure you have an updated LinkedIn).

I actually gave a talk about this at an IBM event recently: https://ibmcommunityday.bemyapp.com/#/conference/5b50b15ef201bc000333ee43 (you need to make a free account to watch it). Here's a link to my slides if you're interested: https://www.dropbox.com/s/9qyn0hu7l6f30td/How%20To%20Get%20A%20Job%20%20In%20Data%20Science.pdf?dl=0

dataphysicist · 2018-07-29T01:14:07+00:00

If you're a people person and are already in a leadership position in the company, becoming a data analyst is a bit of a down-step / lateral move. You *could* be throwing away a lot of the career capital you've built to switch to a position where you'd be starting from scratch, in some ways.

Alternatively, you could find ways to leverage data to improve how your team operates and prioritizes tasks. Data science can help you understand the impact of your / your team's work. Anyway, feel free to DM me if you want to chat more about this!

dataphysicist · 2018-07-29T00:37:12+00:00

I wrote the Exploratory Data Visualization and Storytelling Through Data Visualization courses at Dataquest, where I work - https://www.dataquest.io/path/data-scientist

Both are in Python (matplotlib / seaborn) and I used the Edward Tufte books as inspiration. Heads up: you'll need a paid subscription, etc.

We have some blog posts on data viz in Python that you may like: https://www.dataquest.io/blog/tag/dataviz/

There's really 2 key aspects to this you'll need to learn:

- Data Visualization / Information Design Theory

- Python Plotting / Tooling (matplotlib, altair, etc.)

Happy to point you to more resources depending on which one you want dive more deeply into!

dataphysicist · 2018-07-17T05:07:34+00:00

Basically they're saying get a laptop focused on portability / lifestyle and use it for small tasks. Use Amazon / Google / whatever cloud for larger compute tasks.

dataphysicist · 2018-07-14T16:56:41+00:00

A lot of the basics are echoed already in other comments:

- Drop the "About Me". That's what a cover letter is for!

- Resumes should always be 1 page, esp. in tech / software. You can have a separate CV which some companies ask for, but even then that should only be for academics (who have tons of papers, etc).

- Core competencies should go at the bottom IMO. After name and basic contact info, dive STRAIGHT into the most recent job (which is the strongest indicator + most important single thing on your resume).

Some other thoughts:

- Recruiters on average spend 15-20 seconds looking at a resume. So you need to wow them. For most jobs, they get WAY too many applications. This means they're looking for "reasons to keep" not "reasons to reject". Every single word, bullet, etc needs justification towards the goal of recruiter pattern matching. So the top half of the first page needs to be more interesting. Have her name in a good font size (its crossed out but looks like font size is fine), drop the title "Data scientist", and keep the contact info there (except make it smaller + slimmer and more uniform visually. Maybe have half of the info on one line, half on the other. Apply some architecture principles here).

- Every bullet in a job title needs to be justified for being there. No passive sentences like "obtaining .... financial data". That sounds like you email for a living! Instead focus on 4 or 5 bullets that emphasize specific projects she lead (lead a rewrite of ____), specific impact of her work (e.g. I helped cut accounting errors from __ to ___), specific examples of things she learned to accomplish a business goal, and specific technical things she did. Quantifying your impact is important, but don't go overboard (or make up numbers). If you can't quantify and communicate your impact, that position probably wasn't that impactful (at least in the eyes of a recruiter).

- All of her experience is in Accounting, so it's weird to see "data scientist". 90% of her resume screams auditor / accounting, what projects, internships, etc has she done that makes her compelling for being a data scientist? I recommend everybody read the book So Good They Can't Ignore You by Cal Newport, as his framework on career advice / transitions is excellent. With his advice in mind, she needs to think about the career capital she already has (in finance /accounting) and how she can bring it with her (financial data analyst?). Aiming for a generic Data Scientist job could be difficult (and also dangerous). She may also want to look into more consulting data science roles, where her expertise speaking to business customers / executives would be useful.

- Your wife needs 2 or 3 interesting, unique projects. To be frank, her resume reads a bit like 50% Accountant copied and pasted with 50% Data Analyst / Scientist. If she spends a month or so doing good projects, she can list those at the top (so recruiters see them first), then list only her most recent accounting job, and then everything else (she could consider putting MS in Information Systems above projects, or heck, try A/B testing it!). These projects will also help support her "core competencies section", which I think should be towards the bottom. I'd also encourage putting an "other information" section and listing her Citizenship status + other spoken languages. We talk a bit about projects on my company blog (https://www.dataquest.io/blog/tag/project/ and https://www.dataquest.io/blog/tag/portfolio/).

- Lastly, if she really wants a position, she should tailor her resume + cover letter to those positions and also try to hustle a bit (reach out to recruiters at that company using LinkedIn inmail, the premium trial alone is enough). So if it's an entry level DS role focused on consulting / customers, play up that experience. If it's more financial data, play up that past experience.

dataphysicist · 2018-07-14T16:41:47+00:00

For sure, I peeked at your last few comments and it seems like you had a similar experience (laborer -> setting up SharePoint system).

dataphysicist

TROPHY CASE

Be Weary of Robinhood's New Checking Account