Skip to the part you’re most interested in: What are Python Programming Libraries? What to Use Python Programming Libraries For 7 Core Python Programming Libraries for Data Science & How to Use Them NumPy Requests Beautiful Soup Selenium Pandas Matplotlib SQLite 8 Core Python Programming Libraries for Machine Learning SciKit Learn XGBoost TensorFlow Keras PyTorch PySpark MLLib NLTK Eli5 The world as we know it today would not exist without programming libraries. I don’t want
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Data science is becoming a very hotter and hotter topic by the minute, and data scientists are becoming more and more demanded by all sorts of companies. I personally like to think of data scientists as the watermelon of the fruit aisle in the summer. Everyone wants one – but there is a limit to how many there are. (Side note: I love watermelon.)
The term “Data Scientists” is very vague though – what exactly does it mean, how can you do it, and what can you do with it?
In this article, we’re going to discuss API’s and address some of the biggest questions surrounding this mysterious acronym:
What are APIs? & What are they used for?
Who has an API?
What are the advantages and disadvantages of API’s?
What is an API Economy?
How to Apply & Monetize an API
Curious? Awesome. Take a read through the article!
Being able to analyze data properly has always been important, even before we went into the digital and big data era.
Data analysis is a very important skillset for scientists, because models are built on the results that we see in experiments, and if we are able to properly analyze our experimental data, we are able to formulate models that better represent reality.
But data analysis is only half the story.
The other half, the one that is most often forgotten, is that you also need to be able to:
- Communicate your findings to others
- Convince others that what you’ve found is indeed correct
This is also known as….. Storytelling.
This article is all about how learning programming completely changed my life aka. How I went from the insufferable 9-5 to freedom thanks to programming.
I take you through a little bit of foundation as to my priorities in life, and why the 9-5 did not fit any of my priorities. Then, I continue along to describe my transition through learning programming and data science and what my current situation looks like as well as how I am able to work like this.
Today, I want to talk about something that honestly blew my programming mind. I was taking a course on Neural Networks and Deep Learning – the one by Andrew Ng, former head of Baidu AI Group and Google Brain.
And I stumbled upon a method I’d never encountered before to shortening your code and making it run 150x times faster.
Yes, 150 times – sometimes even 200 times faster. (I have screenshots in this article.)
You: “Get outta here, Max! That’s crazy talk.”
Don’t worry, I thrive off of disbelief. Let’s pack that skepticism into our carry-on, and get directly into the article.
P.S. There’s a free treat for all that make it to the end of this article (come on, it’s not that long).
Today’s article is about the two main traits a data scientist must have to become a to-die-for data scientist.
What’s a to-die-for data scientist? It’s basically a Data Scientist that every company wants/NEEDS to have on their team. You are the ultimate data science package – you understand difficult concepts, you know the basic techniques, you can analyze the data in a fresh perspective, and you use your brain in a systematic manner.
You get recruitment emails everyday and Linkedin requests from recruiters every week asking if you’re ‘available to chat’. Everyone seems to want some of your cool analysis action.
So – what are the two traits that you need to become a to-die-for data scientist?
Why Knowing How to Program Makes You a Rock Star (In the Job Market, At University, in Your Social Circle)
Have you ever thought “Hmm…. It would be so great if I was able to:
* Sort through my emails faster
* Analyze millions of pieces of data
* Build an app
* Help my daughter learn math through an interactive online soccer game
* Develop a new software
* Search the internet faster
* Develop a machine learning algorithm
* Build a website for my business…
I could go on and on, but I’ll save us both our precious time and say that programming is the answer to all of these questions.
Today, I want to lay out exactly how I learned Python programming and became a successful data scientist. I like to think of it as my Cinderella Story because I feel like through hardship and lots of confusion came an evolution of sorts, and I feel like a princess frolicking in the field of data science now.
Read on to find out all about my not-so-tragic backstory, how I started with programming and how I ultimately found my way to data science.
What is web-scraping? Web-scraping is the extraction of data from websites. (Vague, right? Don’t worry – we’ll get into details in a bit.) The internet is your oyster when it comes to web scraping. Literally every website that you can find online is offering up its data to you to scrape.
What can you scrape? More like, what can’t you scrape? IMDB movie rankings, search engine results for SEO, events, email addresses, social media trends, Netflix movie titles, government statistics, stock market data, job listings, dating profiles, apartment listings, Reddit posts, luxury vacation deals, etc.. The possibilities are endless.
I’m just here to teach you the simple steps – you’re the one that’s going to get crazy creative and gather all the data you can with it.