Originally posted on towardsdatascience. Using YFinance and Plotly libraries for Stock Data Analysis In this article, I will explain to you how you can use YFinance a python library aimed to solve the problem of downloading stock data by offering a reliable, threaded, and Pythonic way to download historical market data from Yahoo! finance. In the later...
Originally posted on dev JavaScript is not the only language that can be used to do Web development. Some other languages built upon other programming paradigms like Elm or PureScript are available as well. They rely on functional programming and most of the time have similar concepts. And one of these concepts is the Maybe...
Sooner or later, every data science project faces an inevitable challenge: speed. Working with larger data sets leads to slower processing thereof, so you’ll eventually have to think about optimizing your algorithm’s run time. As most of you already know, parallelization is a necessary step of this optimization. Python offers two built-in libraries for parallelization:...
Reduce the incidental issues in your programming environment so you can focus on the important data science problems. Consider the following situation: you’re trying to practice your soccer skills, but each time you take to the field, you encounter some problems: your shoes are on the wrong feet, the laces aren’t tied correctly, your socks are...
Basic concepts about HTML, XPath, Scrapy, and spiders Euge Inzaugarat Jul 2 “I would be nice to have all the documents of the website” — One of her colleagues said “Yeah, that could give us a lot of information” — Said another colleague “Can you do the scraper?” — They both turn to look at her “Ehhhh… I could….” — She started mumbling “Perfect” — They...
In javascript we often need to do multiple asynchronous things. I’d like to use this post to show a few examples of doing things serially, and in parallel with promises. Example 1: “Wait a second” x 3 First example, lets define a function where we “wait a second”, three times in a row. This function...
Learning about Data Structures and important packages like Numpy and Pandas in Python. This article is the second piece in the Python For Data Science Series. In case you haven’t gone through the introduction of Python(part 1), go ahead and skim through that article here. After knowing about the basics, its time to indulge in more challenging...
Want to catch tax cheats? The government of Rwanda does — and it’s finding them by studying anomalies in revenue-collection data. Want to understand how American culture is changing? So does a budding sociologist in Indiana. He’s using data science to find patterns in the massive amounts of text people use each day to express...
The artificial intelligence (AI) industry has been leading the headlines consistently, and for good reason. It has already transformed industries across the globe, and companies are racing to understand how to integrate this emerging technology. Artificial intelligence is not a new concept. The technology has been with us for a long time, but what has...
The best way to learn data science is by doing it, and there’s no other alternative . From this post, I am going to reflect my learning on how I developed a machine learning model, which can classify movies reviews as positive or negative , and how I embed this model to a Python Flask web application. The...
React vs Angular vs Vue: Why it doesn’t matter. 2018 marks the end of JavaScript fatigue and the “framework wars” A typical frontend/JavaScript developer career usually involves some jQuery and associated plugins before moving on to React, Angular or Vue. Having experienced React, Vue and Angular, it seems they solve similar problems in a similar...
The most significant obstacle preventing organizations from realizing the full potential of their data assets today is the widespread data disorder. Companies have quickly accrued massive amounts of data, and adopted big data environments to store it. And while insights might be buried within all that raw data; if no one knows where it came...
Any average HR department is rich in data. Personal employee data, recruitment data, and performance KPIsare just a few examples of the kinds of data a typical HR team is sitting on. Now, as our world becomes increasingly ‘datafied’, HR teams have more opportunities than ever before to capture and analyze data, which has given rise...
