- 20 Core Data Science Concepts for Beginners [Platinum Blog]
With so much to learn and so many advancements to follow in the field of data science, there are a core set of foundational concepts that remain essential. Twenty of these ideas are highlighted here that are key to review when preparing for a job interview or just to refresh your appreciation of the basics.
- TabPy: Combining Python and Tableau [Platinum Blog]
This article demonstrates how to get started using Python in Tableau.
- Do’s and Don’ts of Analyzing Time Series [Silver Blog]
When handling time series data in your Data Science analysis work, a variety of common mistakes are made that are basic, but very important, to the processing of this type of data. Here, we review these issues and recommend the best practices.
- Top Python Libraries for Data Science, Data Visualization & Machine Learning [Platinum Blog]
This article compiles the 38 top Python libraries for data science, data visualization & machine learning, as best determined by KDnuggets staff.
- Data Science Minimum: 10 Essential Skills You Need to Know to Start Doing Data Science [Platinum Blog]
Data science is ever-evolving, so mastering its foundational technical and soft skills will help you be successful in a career as a Data Scientist, as well as pursue advance concepts, such as deep learning and artificial intelligence.
- Geographical Plots with Python [Silver Blog]
When your data includes geographical information, rich map visualizations can offer significant value for you to understand your data and for the end user when interpreting analytical results.
- Modern Data Science Skills: 8 Categories, Core Skills, and Hot Skills [Gold Blog]
We analyze the results of the Data Science Skills poll, including 8 categories of skills, 13 core skills that over 50% of respondents have, the emerging/hot skills that data scientists want to learn, and what is the top skill that Data Scientists want to learn.
- Creating Powerful Animated Visualizations in Tableau [Silver Blog]
In this post we explore animated data visualization in Tableau,one of the tool's powerful features for making visualizations appealing and interactive.
- These Data Science Skills will be your Superpower [Gold Blog]
Learning data science means learning the hard skills of statistics, programming, and machine learning. To complete your training, a broader set of soft skills will round out your capabilities as an effective and successful professional Data Scientist.
- Top 10 Data Visualization Tools for Every Data Scientist [Silver Blog]
At present, the data scientist is one of the most sought after professions. That’s one of the main reasons why we decided to cover the latest data visualization tools that every data scientist can use to make their work more effective.
- COVID-19 Visualized: The power of effective visualizations for pandemic storytelling [Platinum Blog]
Clear, succinct data visualizations can be powerful tools for telling stories and explaining phenomena. This article demonstrates this concept as relates to the COVID-19 pandemic.
- Coronavirus Data and Poll Analysis – yes, there is hope, if we act now [Silver Blog]
We examine the growth of coronavirus daily cases in most affected countries, and show evidence that social distancing works in reducing the rate of spread. We also analyze KDnuggets Poll results - the scale of change to online and how Data Science work is likely to increase or drop in different regions. Stay Healthy and practice social distancing!
- Plotnine: Python Alternative to ggplot2 [Silver Blog]
Python's plotting libraries such as matplotlib and seaborn does allow the user to create elegant graphics as well, but lack of a standardized syntax for implementing the grammar of graphics compared to the simple, readable and layering approach of ggplot2 in R makes it more difficult to implement in Python.
- Open Source Projects by Google, Uber and Facebook for Data Science and AI [Gold Blog]
Open source is becoming the standard for sharing and improving technology. Some of the largest organizations in the world namely: Google, Facebook and Uber are open sourcing their own technologies that they use in their workflow to the public.
- Understanding Boxplots [Silver Blog]
A boxplot. It can tell you about your outliers and what their values are. It can also tell you if your data is symmetrical, how tightly your data is grouped, and if and how your data is skewed.
- The 4 Quadrants of Data Science Skills and 7 Principles for Creating a Viral Data Visualization [Silver Blog]
As a data scientist, your most important skill is creating meaningful visualizations to disseminate knowledge and impact your organization or client. These seven principals will guide you toward developing charts with clarity, as exemplified with data from a recent KDnuggets poll.
- Which Data Science Skills are core and which are hot/emerging ones? [Gold Blog]
We identify two main groups of Data Science skills: A: 13 core, stable skills that most respondents have and B: a group of hot, emerging skills that most do not have (yet) but want to add. See our detailed analysis.
- Why Data Visualization Is The Most Important Skill in a Data Analyst Arsenal [Gold Blog]
Visually-displayed data is much more accessible, and it’s critical to promptly identify the weaknesses of an organization, accurately forecast trading volumes and sale prices, or make the right business choices.
- The Easy Way to Do Advanced Data Visualisation for Data Scientists [Silver Blog]
Creating effective data visualisations is a core skill for data scientists. This tutorial will guide you through how to easily develop interactive visualisations using the Python library plotly.
- PyViz: Simplifying the Data Visualisation Process in Python [Silver Blog]
There are python libraries suitable for basic data visualizations but not for complicated ones, and there are libraries suitable only for complex visualizations. Is there a single library that handles both these tasks efficiently? The answer is yes. It's PyViz
- How to choose a visualization [Gold Blog]
Visualizations based on the structure of data are needed during analysis, which might be different than for the end user. A new guide for choosing the right visualization helps you flexibly understand the data first.