I remember graduating from school, putting together a resume, and constantly wondering if my resume was “good.” It can be so hard to know what makes a resume good, but after reading hundreds of data science resumes, I wanted to point out 5 mistakes I see all the time. If you can avoid these mistakes, you will have made great progress in getting your resume to a good spot.
So — here are 5 common resume mistakes:
I’ve found that it is increasingly common for people to try to make their resume different. Things like skill charts or personal touches, while they look cool, are often distracting. Business Insider did a comparison of 2 resume formats and tracked recruiters’ eye movements. …
Now, don’t get me wrong, online courses are great! You can learn so much online and that is truly amazing.
But — I find often there is a disconnect between the expected outcome of taking an online course and what actually happens. Let me first preface with the fact that I am specifically speaking to data science courses as those are the courses I am more familiar with. Other types of courses might lead to different outcomes.
Let’s start with a story of a young data scientist looking to break into the field.
In 2017, it was estimated that 750 million people worldwide used Excel. The population of the world in 2017 was about 7.6 billion. That means roughly 10% of the population was using Excel and I would guess mostly for data analytics. That is insane.
There is no doubt that Excel has been an incredibly important tool for companies and still has a place in the toolkit of every data analyst and scientist, but for most of your work, you need to stop using Excel and upgrade to Python. I’m going to show you why.
So, if you still have not taken the leap to learn Python and take your data analytics and visualization skills to the next level, I present you with 5 reasons why you need to learn Python right now. By the end, I’m confident you’ll be looking forward to replacing most of your Excel work with Python. …
You probably have photos, right?
You probably want those photos tagged automatically for you, right?
But you also don’t want to write a ton of code to do so.
Read on to learn how to use deep learning and Pytorch to tag any photo with less than 60 lines of code. The best part is, you’ll only have to change about 3 lines of code to get it to work for your own images!
An extremely common machine learning problem is to classify or tag an image. …
In this post, we are going to discuss the linear regression model used in machine learning. Modeling for this post will mean using a machine learning technique to learn — from data — the relationship between a set of features and what we hope to predict. Let’s bring in some data to make this idea more concrete.
from sklearn.datasets import load_boston
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import numpy as np from sklearn.model_selection
import learning_curve from sklearn.metrics
%matplotlib inlinenp.random.seed(42)boston_data = load_boston() boston_df = pd.DataFrame(boston_data.data, columns=boston_data.feature_names)
target = boston_data.target
Here is some description of our…
I think the title is pretty clear, so let’s get straight to it.
Before even thinking about hiring a data scientist, you should step back and consider your data.
A data scientist’s job is to create value from data. If you are unsure whether you even have data, that is a very good sign that you’re not ready for a data scientist.
If you know you have data, but really have no idea how to access it, it’s reliability or any of the specifics, then you should first answer those questions.
You will get significantly more value from a data scientist hire if your company has a strong grasp on its data assets. Your understanding doesn’t have to be perfect, but you should be able to point a data scientist to some data with documentation. …
My son is really into Pokemon. I don’t get it, but I guess that’s besides the point. I did start to wonder, though, if I could create new Pokemon cards for him automatically using deep learning.
I ended up having a bit of success generating Pokemon-like images using Generative Adversarial Networks (GANs) and I thought others might enjoy seeing the process.
I recently sent out a poll to people who subscribe to my email list and asked what they were most interested in learning.
About 86 percent said deep learning.
That blew my mind. I knew deep learning was a hot topic, but I had no idea just how interested people were in learning more.
So — I thought I would write up how I would start learning deep learning if I were to start today.
Unsurprisingly, in my opinion, the best place to start is with Andrew Ng’s deep learning specialization.
Andrew Ng has an incredible gift for teaching and does a great job starting from the basics and working up to image and text processing using deep learning. …
In 1973, at the height of the OPEC oil crisis and skyrocketing fuel prices, NASA scientist and USC professor Jack Nilles began thinking about ways work could be done without the need for commuting. Nilles’ thought experiments evolved into case studies, numerous books, including The Telecommunications-Transportation Tradeoff, the original book on telecommuting, as well as dozens of papers, articles and keynote speeches. To this day, Nilles remains one of the principal evangelists for remote work as a viable alternative to a traditional office.
Today, it would not be entirely hyperbolic to consider Nilles’ early work clairvoyant. The last decade has seen a dramatic rise in companies adopting a remote workforce. A 2019 report compiled by Flexjobs and Global Workplace Analytics found that the number of people telecommuting in the United States increased by 159% from 2005 to 2017. With the rapidly evolving global crisis surrounding COVID-19, forcing more and more companies to adopt strict work-from-home policies, remote work is further cementing itself as a workplace paradigm to be taken seriously. …
Earlier this month, Kaggle released a new dataset challenge: the COVID-19 Open Research Dataset Challenge. This challenge is a call to action to AI experts to develop text processing tools to help medical professionals find answers to high priority questions.
To that end, and in partnership with AI2, CZI, MSR, Georgetown, NIH & The White House, Kaggle assembled a dataset “of over 29,000 scholarly articles, including over 13,000 with full text, about COVID-19, SARS-CoV-2, and related coronaviruses.”
I believe that a lot of data scientists are looking for opportunities to help combat the COVID-19 pandemic and this is a great place to start. Kaggle provides the data and even a set of tasks for you to tackle. …