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Learning With Data
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My Potent First Day Routine for Phenomenal Success on Your Next Data Science Project
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I remember my first data science project. It was fun, I learned a ton, but in the end, it was an absolute disaster. Looking back, I realized that how I spent my time during the first day of that project had an enormous impact on its failure. In my excitement, I had skipped any serious amount of exploratory data analysis and went straight for the most complex and insane model possible.
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Answers to the 3 Most Common Data Science Questions
Ask Us Anything Photo by Bryan Minear on Unsplash
Earlier this year, I was super excited to join Towards Data Science as an editor. It has been a lot of fun helping the community grow and in this article, I will be contributing to our “Ask Us Anything” series by answer 3 common data science questions. I hope you enjoy!
Should I go to Graduate School? This is probably the question I receive the most and is probably the hardest to answer.
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The Top 3 Books to Get Started with Data Science Right Now
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I remember when I was first learning data science. There were almost too many resources and too much to learn that it was easy to get lost. I explored many avenues that while interesting, in retrospect, were not the most efficient way to get started. If you are just starting your journey and want the 3 best books to help you focus your studies, this is the article for you.
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Want to Accomplish Your Goals? Then Stop Getting Distracted
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My alarm goes off — time to wake up. I sleepily reach towards my phone to turn it off and lay back down in bed, phone in hand. Before I know it distraction has struck. I’m 3 swipes deep into my LinkedIn feed and will soon be late for work if I don’t start getting ready.
Sound familiar?
A new study from IDC Research found that
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How To Add Confidence Intervals to Any Model
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“Can I trust your model?”
It is the first thing your manager asks as you present your latest work. How do you answer? Do you refer to the mean squared error? the R² coefficient? How about some example results? These are all great, but I would like to add another technique to your toolkit —** confidence intervals**.
Trust At the end of the day, one of the most important jobs any data scientist has is to help people trust an algorithm that they most likely don’t completely understand.
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How To Use Deep Learning Even with Small Data
You’ve heard the news — deep learning is the hottest thing since sliced bread. It promises to solve your most complicated problems for the small price of an enormous amount of data. The only problem is you are not working at Google nor Facebook and data are scarce. So what are you to do? Can you still leverage the power of deep learning or are you out of luck? Let’s take a look at how you might be able to leverage deep learning even with limited data and why I think this might be one of the most exciting areas of future research.
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One Word of Code to Stop Using Pandas So Slowly
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You have your data all loaded into a Panda’s dataframe, ready to do some exploratory analysis, but first, you need to create a few additional features. Naturally, you turn to the apply function. Apply is great because it makes it easy to use a function on all the rows of your data. You get it all set up, run your code and…
Wait
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The Essential Python Libraries for Data Science
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You’ve been learning about data science and want to get rocking immediately on solving some problems. So, of course, you turned to Python
Source: https://xkcd.com/353/
This article will introduce you to the essential data science libraries so you can start flying today.
The Core Python has three core data science libraries upon which many others have been built.
Numpy Scipy Matplotlib For simplicity, you can think of Numpy as your go-to for arrays.
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The Most Undervalued Standard Python Library
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Python has a lot of great libraries included out of the box. One of which is collections. The collections module provides “high-performance container datatypes” which provide alternatives to the general-purpose containers dict, list, set, and tuple. I’d love to introduce you to three of these datatypes and in the end, you’ll be wondering how you ever lived without them.
NamedTuple I can’t overstate how useful namedtuples can be for data scientists.
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Why you will regret not hiring undergraduates
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You are on the verge of graduating from your undergraduate degree, excited about getting your first job as a data scientist, and start looking to apply. You pursue listings on Indeed and LinkedIn and soon become quite troubled — almost all of the jobs either want prior experience or an advanced degree. You have neither. So — what do you do? Well, you have a few options, some of which I discussed in a previous article:
