Originally posted on dev. As files, datasets and configurations grow, it gets increasingly difficult to navigate them. There are however many tools out there, that can help you to be more productive when dealing with large JSON and YAML files, complicated regular expressions, confusing SQL database relationships, complex development environments and many others. JSON JSON...
Tag: data
Parsing data with strtok in C
Originally posted on opensource. The strtok function is a handy way to read and interpret data from strings. Use it in your next project to simplify how you read data into your program. Some programs can just process an entire file at once, and other programs need to examine the file line-by-line. In the latter...
Beyond SQL: 8 new languages for data querying
Originally posted on infoworld. SQL has dominated data querying for decades. Newer query languages offer more elegance, simplicity, and flexibility for modern use cases. For the last three decades, databases and Structured Query Language (SQL) were almost synonymous. Anyone who wanted to retrieve information from a database had to learn SQL. Anyone who wanted to...
Way to customize each item in the list using simple CSS
Originally posted on dev. Lists are the form of data representation commonly used by all types of documents. In HTML there are two types of lists namely ordered and unordered lists. In an ordered list, the items are indicated by serial numbers or letters that are in some order. In an unordered list, the list...
How different programming languages read and write data
Originally posted on opensource. In his article How different programming languages do the same thing, Jim Hall demonstrates how 13 different languages accomplish the same exact task with different syntax. The lesson is that programming languages tend to have many similarities, and once you know one programming language, you can learn another by figuring its syntax...
Python or R: Which to choose for your next data project
Originally posted on sdtimes. When it comes to picking a language for a new data science project, developers often have to go through the debate of whether Python or R would be the best suited for the task. R is a language specifically designed for data analysis so it has a lot of useful features built in, but Python...
How to build a data-driven DevOps culture
Originally posted on sdtimes. Data helps organizations make a number of important decisions, every day. It can be used to measure ROI of marketing campaigns and even aggregate user habits. It can also be used to better your DevOps team. In a Power Talk with SD Times, Steve Boone, DevOps head of product management at HCL Software, and...
Downloading Stock Data and Representing it Visually
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...
The Maybe data type in JavaScript
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...
Multiprocessing vs. Threading in Python: What Every Data Scientist Needs to Know
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:...
How to Avoid Common Difficulties in Your Data Science Programming Environment
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...
How to extract online data using Python
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...
Serial Promises vs Parallel Promises
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...
Python for Data Science: From Scratch
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...
The Democratization of Data Science
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...
Do You Know The Difference Between Data Analytics And AI Machine Learning?
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...
Embedding Machine Learning Models to Web Apps
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...
How components won the “framework wars”
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...
Why Data Governance is Crucial for Big Data Environments
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...
The 3 Vital Ways HR Teams Should Be Using Data
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...