## Tutorial: Why Functions Modify Lists and Dictionaries in Python

In this beginner Python tutorial, we’ll take a look at mutable and immutable data types, and learn how to keep dictionaries and lists from being modified by our functions.

## Tutorial: Poisson Regression in R

Poisson Regression can be a really useful tool if you know how and when to use it. In this tutorial we’re going to take a long look at Poisson Regression, what it is, and how R programmers can use it in the real world. Specifically, we’re going to cover: What Poisson Regression actually is and […]

## Tutorial: Find Dominant Colors in an Image through Clustering

Take the first step into image analysis in Python by using k-means clustering to analyze the dominant colors in an image in this free data science tutorial.

## Tutorial: Time Series Analysis with Pandas

In this tutorial, we will learn about the powerful time series tools in the pandas library. Originally developed for financial time series such as daily stock market prices, the robust and flexible data structures in pandas can be applied to time series data in any domain, including business, science, engineering, public health, and many others. […]

## Advanced Jupyter Notebooks: A Tutorial

Lying at the heart of modern data science and analysis, Jupyte project lifecycle. Whether you’re rapidly prototyping ideas, demonstrating your work, or producing fully fledged reports, notebooks can provide an efficient edge over IDEs or traditional desktop applications. Following on from “Jupyter Notebook for Beginners: A Tutorial“, this guide will take you on a journey […]

## Historic Wildfire Data: Exploratory Visualization in R

In recent weeks, news of the devastating wildfires sweeping parts of the US state of California have featured prominently in the news. While most wildfires are started accidentally by humans, weather conditions like wind and drought can exacerbate fires’ spread and intensity. Improved understanding of historical wildfire trends and causes can inform fire management and […]

## Math in Data Science

Math is like an octopus: it has tentacles that can reach out and touch just about every subject. And while some subjects only get a light brush, others get wrapped up like a clam in the tentacles’ vice-like grip. Data science falls into the latter category. If you want to do data science, you’re going […]

## Scikit-learn Tutorial: Machine Learning in Python

Scikit-learn is a free machine learning library for Python. It features various algorithms like support vector machine, random forests, and k-neighbours, and it also supports Python numerical and scientific libraries like NumPy and SciPy. In this tutorial we will learn how to easily apply Machine Learning with the help of the scikit-learn library, which was […]

## Linear Regression in Real Life

This post was written by Carolina Bento. She leads Data Analytics teams that empower companies to make data-driven decisions, and currently manages Product Analytics team at eero. This article was originally posted on Medium, and has been reposted with permission. We learn a lot of interesting and useful concepts in school but sometimes it’s not […]

## Python Dictionary Tutorial

Python offers a variety of data structures to hold our information — the dictionary being one of the most useful. Python dictionaries quick, easy to use, and flexible. As a beginning programmer, you can use this Python tutorial to become familiar with dictionaries and their common uses so that you can start incorporating them immediately into […]

## Understanding Regression Error Metrics

Human brains are built to recognize patterns in the world around us. For example, we observe that if we practice our programming everyday, our related skills grow. But how do we precisely describe this relationship to other people? How can we describe how strong this relationship is? Luckily, we can describe relationships between phenomena, such […]

## Basic Statistics in Python: Probability

When studying statistics, you will inevitably have to learn about probability. It is easy lose yourself in the formulas and theory behind probability, but it has essential uses in both working and daily life. We’ve previously discussed some basic concepts in descriptive statistics; now we’ll explore how statistics relates to probability. Prerequisites: Similar to the […]

## Basic Statistics in Python: Descriptive Statistics

The field of statistics is often misunderstood, but it plays an essential role in our everyday lives. Statistics, done correctly, allows us to extract knowledge from the vague, complex, and difficult real world. Wielded incorrectly, statistics can be used to harm and mislead. A clear understanding of statistics and the meanings of various statistical measures […]

## 12 Essential Command Line Tools for Data Scientists

This post is a short overview of a dozen Unix-like operating system command line tools which can be useful for data science tasks. The list does not include any general file management commands (pwd, ls, mkdir, rm, …) or remote session management tools (rsh, ssh, …), but is instead made up of utilities which would […]

## Python Generators

Python generators are a powerful, but misunderstood tool. They’re often treated as too difficult a concept for beginning programmers to learn — creating the illusion that beginners should hold off on learning generators until they are ready. I think this assessment is unfair, and that you can use generators sooner than you think. In this […]

## 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 […]

## 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 […]

## Using Linear Regression for Predictive Modeling in R

Predictive models are extremely useful for forecasting future outcomes and estimating metrics that are impractical to measure. For example, data scientists could use predictive models to forecast crop yields based on rainfall and temperature, or to determine whether patients with certain traits are more likely to react badly to a new medication. Before we talk […]

## Top 10 Machine Learning Algorithms for Beginners

Introduction The study of ML algorithms has gained immense traction post the Harvard Business Review article terming a ‘Data Scientist’ as the ‘Sexiest job of the 21st century’. So, for those starting out in the field of ML, we decided to do a reboot of our immensely popular Gold blog The 10 Algorithms Machine Learning […]

## Using Box Plots to Explore Women’s Height Data

I’ve recently been working on the Digital Panopticon, a digital history project that has brought together (and created) massive amounts of data about British prisoners and convicts in the long 19th century, including several datasets which include heights for women. Adult height is strongly influenced by environmental factors in childhood, one of the most important […]

## Jupyter Notebook for Beginners: A Tutorial

The Jupyter Notebook is an incredibly powerful tool for interactively developing and presenting data science projects. A notebook integrates code and its output into a single document that combines visualisations, narrative text, mathematical equations, and other rich media. The intuitive workflow promotes iterative and rapid development, making notebooks an increasingly popular choice at the heart […]

## Visualizing Women’s Marches: Part 2

This post is the second in a series on visualizing the Women’s Marches from January 2017. In the first post, we explored the intensive data collection and data cleaning process necessary to produce clean pandas dataframes. Data Enrichment Because we eventually want to be able to build maps visualizing the marches, we need latitude and […]

## Exploring Women’s Army Auxiliary Corps Data

Today I want to go on an excursion in “catalogues as data“. The UK National Archives’ Discovery catalogue is an excellent resource for this activity, because a) it has a lot of records that have document descriptions at ‘item’ or ‘piece’ level in the catalogue, containing quite structured information (like dates, places, occupations) that can […]

## Visualizing Women’s Marches: Part 1

In celebration of Women’s History Month, I wanted to better understand the scale of the Women’s Marches that occurred in January 2017. Shortly after the marches, Vox published a map visualizing the estimated turnout across the entire country. This map is excellent at displaying: locations with the highest relative turnouts hubs and clusters of where […]

## R Fundamentals: Building a Simple Grade Calculator

R is one of the most popular languages for statistical analysis, data science, and reporting. At Dataquest, we have been adding R courses (you can learn more in our recent update). For a comparison of R and Python, check out our analysis here. In this tutorial, we’ll teach you the basics of R by building […]

## Data Science Terms and Jargon: A Glossary

Getting started in data science can be overwhelming, especially when you consider the variety of concepts and techniques a data scienctist needs to master in order to do her job effectively. Even the term “data science” can be somewhat nebulous, and as the field gains popularity it seems to lose definition. To help those new […]

## Control Structures in R: Using If-Else Statements and Loops

Control structures allow you to specify the execution of your code. They are extremely useful if you want to run a piece of code multiple times, or if you want to run a piece a code if a certain condition is met. This tutorial is based on part of our newly released Intermediate R course. […]

## Introduction to AWS for Data Scientists

These days, many businesses use cloud based services; as a result various companies have started building and providing such services. Amazon began the trend, with Amazon Web Services (AWS). While AWS began in 2006 as a side business, it now makes \$14.5 billion in revenue each year. Other leaders in this area include: Google—Google Cloud […]

## Introduction to Functional Programming in Python

Most of us have been introduced to Python as an object-oriented language; a language exclusively using classes to build our programs. While classes, and objects, are easy to start working with, there are other ways to write your Python code. Languages like Java can make it hard to move away from object-oriented thinking, but Python […]

## Introduction to Python Ensembles

Stacking models in Python efficiently Ensembles have rapidly become one of the hottest and most popular methods in applied machine learning. Virtually every winning Kaggle solution features them, and many data science pipelines have ensembles in them. Put simply, ensembles combine predictions from different models to generate a final prediction, and the more models we […]

## How to Set Up a Free Data Science Environment on Google Cloud

Whether you’re running out of memory on your local machine or simply want your code to run faster on a more powerful machine, there are many benefits to doing data science on a cloud server. A cloud server is really just a computer, like the one you’re using now, that’s located elsewhere. In this post, […]

## Learning Curves for Machine Learning

Diagnose Bias and Variance to Reduce Error When building machine learning models, we want to keep error as low as possible. Two major sources of error are bias and variance. If we managed to reduce these two, then we could build more accurate models. But how do we diagnose bias and variance in the first […]

## Adding Axis Labels to Plots With pandas

Pandas plotting methods provide an easy way to plot pandas objects. Often though, you’d like to add axis labels, which involves understanding the intricacies of Matplotlib syntax. Thankfully, there’s a way to do this entirely using pandas. Let’s start by importing the required libraries: import pandas as pd import numpy as np import matplotlib.pyplot as […]

## Pandas Concatenation Tutorial

You’d be hard pressed to find a data science project which doesn’t require multiple data sources to be combined together. Often times, data analysis calls for appending new rows to a table, pulling additional columns in, or in more complex cases, merging distinct tables on a common key. All of these tricks are handy to […]

## Using Excel with pandas

Excel is one of the most popular and widely-used data tools; it’s hard to find an organization that doesn’t work with it in some way. From analysts, to sales VPs, to CEOs, various professionals use Excel for both quick stats and serious data crunching. With Excel being so pervasive, data professionals must be familiar with […]

## Regular Expressions for Data Scientists

As data scientists, diving headlong into huge heaps of data is part of the mission. Sometimes, this includes massive corpuses of text. For instance, suppose we were asked to figure out who’s been emailing whom in the scandal of the Panama Papers — we’d be sifting through 11.5 million documents! We could do that manually […]

## Setting Up the PyData Stack on Windows

The speed of modern electronic devices allows us to crunch large amounts of data at home. However, these devices require the right software in order to reach peak performance. Luckily, it’s now easier than ever to set up your own data science environment. One of the most popular stacks for data science is PyData, a […]

## Kaggle Fundamentals: The Titanic Competition

Kaggle is a site where people create algorithms and compete against machine learning practitioners around the world. Your algorithm wins the competition if it’s the most accurate on a particular data set. Kaggle is a fun way to practice your machine learning skills. This tutorial is based on part of our free, four-part course: Kaggle […]

## SQL Fundamentals

The pandas workflow is a common favorite among data analysts and data scientists. The workflow looks something like this: The pandas workflow works well when: the data fits in memory (a few gigabytes but not terabytes) the data is relatively static (doesn’t need to be loaded into memory every minute because the data has changed) […]