Tag: Data Science

Simple Python Math Module Guide (22 Examples and 18 Functions)

Using The math Module in Python math is a built-in module in the Python 3 standard library that provides standard mathematical constants and functions. You can use the math module to perform various mathematical calculations, such as numeric, trigonometric, logarithmic, and exponential calculations. This tutorial will explore the common constants and functions implemented in the […]

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Data Engineer, Data Analyst, Data Scientist — What’s the Difference?

In the fast-growing field of data, the “big three” job roles are data engineer, data analyst, and data scientist. Figure out which is the best fit for you.

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Want a job in data? Learn SQL.

Learning SQL might not be as “sexy” as learning Python or R, but it’s a fundamental skill for almost every data scientist and data analyst job. Here’s why.

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R vs Python for Data Analysis — An Objective Comparison

Python vs. R — which is better for data science? We compare the two languages side by side and see how Python and R perform on the same analysis steps.

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How to Become a Data Scientist (Step-By-Step) in 2020

Data science is one of the most buzzed about fields right now, and data scientists are in extreme demand. And with good reason — data scientists are doing everything from creating self-driving cars to automatically captioning images. Given all the interesting applications, it makes sense that data science is a very sought-after career. Data science is applied in many […]

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Six Reasons Why You Should Learn R for Data Science

Why should you learn R programming when you’re aiming to learn data science? Here are six reasons why R is the right language for you.

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Tutorial: Python Regex (Regular Expressions) for Data Scientists

In this tutorial, learn how to use regular expressions and the pandas library to manage large data sets during data analysis.

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Dataquest’s Philosophy: Building the Perfect Data Science Learning Tool

Learn how Dataquest’s philosophy sets our platform apart from other data science learning tools, and what we’ve learned from years of teaching data science.

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Want a Job in Data Science? Here’s Why You Should Specialize

Being a jack-of-all-trades may not be the best approach when it comes to getting a data science job and building your data science career.

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How to Use Dataquest

Dataquest’s learning platform is user-friendly enough that if you’d like to, you can simply dive right in. But if you’re the type who likes to flip through the user manual first, this article is for you! In it, we’re going to cover the basic features of the Dataquest platform, and pass along some helpful tips […]

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An Intro to Deep Learning in Python

Deep learning is a type of machine learning that’s growing at an almost frightening pace. Nearly every projection has the deep learning industry expanding massively over the next decade. This market research report, for example, expects deep learning to grow 71x in the US and more than that globally over the next ten years. There’s […]

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Learn to do Data Viz in R

One of the reasons that R is a top language for data science is that it’s great for data visualization. R users can take advantage of the wildly popular ggplot2 package to turn massive data sets into easily-readable charts in just a few lines of code. That can be incredibly valuable for presenting your data, […]

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12 Essential Command Line Tools for Data Scientists

Learn 12 useful command line/terminal commands for data science work.

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Programming Best Practices For Data Science

The data science life cycle is generally comprised of the following components: data retrieval data cleaning data exploration and visualization statistical or predictive modeling While these components are helpful for understanding the different phases, they don’t help us think about our programming workflow. Often, the entire data science life cycle ends up as an arbitrary […]

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Data Retrieval and Cleaning: Tracking Migratory Patterns

Advancing your skills is an important part of being a data scientist. When starting out, you mostly focus on learning a programming language, proper use of third party tools, displaying visualizations, and the theoretical understanding of statistical algorithms. The next step is to test your skills on more difficult data sets. Sometimes these data sets […]

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