How to Overcome Impostor Syndrome For Good

I was working at a job I loved when I started to wonder whether I was a bit of a fraud. Digital strategy and client service was my forte. But when I was offered extra responsibilities that suddenly had me leading meetings with top affiliate global marketers (while I had minimal experience in that field), I […]

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 […]

Generating Climate Temperature Spirals in Python

Ed Hawkins, a climate scientist, tweeted the following animated visualization in 2017 and captivated the world: This visualization shows the deviations from the average temperature between 1850 and 1900. It was reshared millions of times over Twitter and Facebook and a version of it was even shown at the opening ceremony for the Rio Olympics. […]

Using Linear Regression for Predictive Modeling in R

Predictive models are extremely useful, when learning r language, 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 […]

This Artist Turns the Forest Floor into Data Visualizations

Editor’s note: This post was written as part of a collaboration with iDataLabs, a marketing intelligence company. Author Julia Cook works in marketing at iDataLabs. We’re all familiar with data visualizations — word clouds, pie charts, pivot tables — but how does one put enquiries in paint? Patty Haller, a landscape artist from Seattle WA, may have figured that […]

Eric: “I wanted something practical”

Eric Sales De Andrade came to Dataquest via Quora. “I read a response from Vik and he seemed to know what he was writing about.” At the time, he worked in data mining— “But it was just putting stuff in a database. I wanted to get real with data.” He had originally tried DataCamp and a machine […]

Mike: “I wanted to grow my skills”

Mike Roberts didn’t plan on becoming a data scientist. After getting a degree in physics, he gave art management and professional poker playing a shot before becoming a BI analyst. He joined Dataquest to beef up those BI skills, but quickly learned he could switch to a much more interesting role within data science. For […]

Christian: “Consistency is Key”

After working in Business Intelligence, Christian L’Heureux took a break from data science. When he returned, as part of his MBA program, he found a changed world. He knew he needed to get up to speed quickly on the new landscape — especially Python. After trying DataCamp and CodeAcademy, he found Dataquest. “I liked the way […]

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 […]

Five Reasons for Historians to Learn R

In which I do some cheerleading for the R Project for Statistical Computing. 1. You’re almost certain to find it worth the effort Often, in the endless “should academics learn to code” debate, it’s not clear to newcomers what you can actually use this code for once you’ve invested a lot of time in learning […]

Learning From Bank Data: Women Across the World

This post looks at the World Bank World Development Indicators (WDI). This massive collection has data in several categories: demographic, education, work, poverty, health. It includes both country-level data and various aggregates by different criteria: geographical regions, income levels, etc. The UK Data Service has a useful guide as well as access to the data. […]

jupyter-notebooks-tutorial

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 […]

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