Tag: statistics

Don’t Just Hope: Binge-Learn Data Science This New Year

Posted on by Billy

Welcome, fellow data enthusiasts, to the precipice of a new year! As 2023 gracefully exits stage left, we stand poised on the threshold of 2024, a blank canvas brimming with possibilities. For many, this translates to resolutions, aspirations, and perhaps the ever-present yearning to conquer the enigmatic realm of data science.

This blog post is your armor against the inevitable doldrums, your compass through the labyrinthine world of data, and your ultimate guide to sticking with data science throughout 2024.

Charting Your Course: A Roadmap to Success

First things first, you need a roadmap. Think of it as your personal GPS, guiding you through the dense forest of algorithms and statistical models. There are plenty of excellent resources available online, but I recommend checking out these gems:

  • DataCamp: Structured learning paths with bite-sized, interactive lessons.
  • Kaggle: Learn by doing with real-world datasets and a vibrant community of data scientists.
  • Coursera: Specializations from top universities and industry leaders.
  • My content: If you are just starting out with programming, consider looking into my intro to programming textbook using R.  If you prefer a video format, I also have a video series on the topic.

Remember, the perfect roadmap is the one that works for you. Don’t be afraid to customize it, experiment with different resources, and find what ignites your inner data scientist.

Fueling the Fire: Staying Motivated

Data science is a marathon, not a sprint. There will be days when the code doesn’t compile, the models refuse to cooperate, and you feel like you’re banging your head against a statistical wall. But fear not, for even the mightiest data wranglers face these hurdles. Here’s how to stay motivated:

  • Set achievable goals: Break down your learning into smaller, manageable chunks. Completing these mini-quests will give you a sense of accomplishment and keep you moving forward. 
  • Find your community: Join online communities, forums, or local meetups to connect with other data enthusiasts. Sharing your struggles and successes can be incredibly motivating.
  • Celebrate the wins: Take the time to appreciate your progress, no matter how small. Did you finally understand the concept of p-values? High five yourself! Baked a machine learning-themed cake? Share it with your fellow data warriors!
  • Remember your “why”: Remind yourself why you embarked on this data-driven odyssey in the first place. Is it to solve real-world problems? Make a difference in the world? Fuel your passion for data and let it guide you through the tough times.

Sharpening Your Tools: Practice Makes Perfect

Data science is not a spectator sport. To truly master this craft, you need to get your hands dirty. Here are some ways to put your theoretical knowledge into practice:

  • Work on personal projects: Find a dataset that sparks your curiosity and build something cool with it. Analyze your favorite movie ratings, predict the next stock market trend, or create a tool to solve a problem you face in your daily life.
  • Participate in hackathons: These timed coding competitions are a great way to test your skills under pressure and learn from other data scientists.
  • Contribute to open-source projects: Lend your expertise to existing projects and gain valuable experience while giving back to the community.

Remember, the more you practice, the more confident and skilled you’ll become. So, don’t be afraid to experiment, make mistakes, and learn from them. Every line of code, every failed model, is a stepping stone on your path to data science mastery.

Remember, the journey of a data scientist is not a solitary one

We are a community of curious minds, united by our passion for extracting insights from the ever-growing ocean of data. So, let’s embark on this exciting adventure together, armed with our roadmaps, fueled by motivation, and ever-honing our skills through practice. Together, we can conquer the dataverse in 2024 and beyond!

Note: Bard was used to help write this article.  Midjourney was used to help create the images presented in this article.

Tips and Tools for Applying to Graduate School

Posted on by Billy

If you want to apply to graduate school for statistics, there are certain tools you will need to more easily apply to graduate school. (Note: While I am writing this for individuals applying for statistics, many of these tips can also be applied to other types of programs. It is also assuming that you are going directly from undergrad to grad school. However, this again could be applied to many people going from the work force to grad school.)

But before you begin, ask yourself the following question; what is your reason for applying to graduate school? You need to understand why you are pursuing this path. Use this reason to guide you in throughout this process so that you remain motivated. If your reason does not motivate you, find another reason.

Find a Graduate Advisor

Throughout this process, you will need someone to help you. Find someone with experience in applying to graduate schools to help you. For me, that was a professor I had as an undergrad. You will need this person to get advice from but also to bounce ideas off of. They will help you to edit your essays as their experience will help you to write your essays in a particular way.

What schools do you apply to?

While you must make the final decision on what schools you apply to, you should ask yourself these questions regarding each program:

  1. Does the program look like a place that you can be at for the next 5 years?
  2. Would you be excited to apply to the program?
  3. Does the program have professors actively doing research in areas that you are interested in?

You need to be able to answer these questions and understand why you are applying to each program. If you need to, write down each answer for each program.

How many schools do you apply to?

You should apply to at least:

  • 1 reach programs
  • 3 or 2 competitive programs
  • 1 safety school

This is a general guideline and should be always reconsidered if you feel like you should apply to more programs. Be forewarned: it can easily get overwhelming if you apply to too many programs.

Recommenders

You should have 3 or 4 individuals in mind for writing you a letter of recommendation. They should be people that you know fairly well. They could be a favorite professor, department chair, or, if warranted, a boss from work. For myself, I tried to find a balance between professors who could speak on my theoretical and/or applied statistics skills. However, I tried to at least have all of them covered.

When you ask them, ask them in person. This is the best way. If you cannot, formulate a brief but polite email for them to write you a strong letter. However, you might have to send the email multiple times, at least a week apart, because the individual might have missed the email. This might be because we get a lot of email, and they can be easy to miss.

If they agree, write them a thank you note with a list of school you are thinking of applying to with the following information:

  • When the due date is for the program’s reference writers
  • How they will submit the program (electronically, via email, etc.)

You should also give them this list with this information on a piece of paper. Also, remind them at appropriate intervals about submitting the letter. Offer to give them a resume or CV of your work. Also, keep them updated with your current place of applying to graduate school. It will enforce it in their minds that you are serious about this and will also remind them to get the letter in!

Contents for Personal Statement/Statement of Purpose (SOP)

Your SOP should incorporate the following:

  • Your reason for applying to graduate school
  • Activities that you have participated in that will showcase yourself as a strong candidate (undergraduate research opportunities)
  • What field of statistics you are particularly interested in (computational statistics, nonparametrics, etc.)
  • A personal touch for the program you are applying to (ie. mentioning a professor in the department that is engaging in research you find exciting)
Other Tools

Other useful tools that I used often was:

 

Summary
Essays

– Personal Statement

– SOP

Graduate Advisor

-find 1 and meet with him/her often
*could be department chair
*could be professor you get along with
-meet with them and have them edit essays

Recomenders

-have 3 to 4
-first ask if they can write you a strong letter
– second give them an excel sheet of what they need to know
*dates
*where
*strong letter
– remind them 2 weeks before letter due
– remind them 1 week before letter due
– remind them every day afterwards

Where to Apply

-pick at least 1 reach
-pick at least 3 medium
-pick at least 1 safety

Timeframe

-start looking during the summer before you apply (your senior year)

Personal Decisions

– how many places do you apply to
– do you apply to Applied Stat/Applied Math/Math/ Computer Science/Biostat?
– do you go for programs where you need to get master’s first?
*do you get master’s first?
– do you go for master’s at