- A Layman’s Guide to Data Science. Part 2: How to Build a Data Project - Apr 2, 2020
As Part 2 in a Guide to Data Science, we outline the steps to build your first Data Science project, including how to ask good questions to understand the data first, how to prepare the data, how to develop an MVP, reiterate to build a good product, and, finally, present your project.
- Why you should NOT use MS MARCO to evaluate semantic search - Apr 2, 2020
If we want to investigate the power and limitations of semantic vectors (pre-trained or not), we should ideally prioritize datasets that are less biased towards term-matching signals. This piece shows that the MS MARCO dataset is more biased towards those signals than we expected and that the same issues are likely present in many other datasets due to similar data collection designs.
- Advice for a Successful Data Science Career - Mar 30, 2020
This blog is meant to show that most everyone has had to expend quite a bit of effort to get where they are. They have to work hard, sometimes experience failure, show discipline, be persistent, be dedicated to their goals, and sometimes sacrifice or take risks.
- SIGKDD Community Impact Program (Deadline Jun. 15) - Mar 27, 2020
SIGKDD is announcing a funding opportunity through its Community Impact Program.The goal of the program is to support projects that promote data science and help the data science community to grow, broaden, and diversify. Read more here.
- Making sense of ensemble learning techniques - Mar 26, 2020
This article breaks down ensemble learning and how it can be used for problem solving.
- Covid-19, your community, and you — a data science perspective [Gold Blog]
Let's talk about covid-19; the reality, the numbers, and the data science.
- 50 Must-Read Free Books For Every Data Scientist in 2020 [Silver Blog]
In this article, we are listing down some excellent data science books which cover the wide variety of topics under Data Science.
- 20 AI, Data Science, Machine Learning Terms You Need to Know in 2020 (Part 2) [Gold Blog]
We explain important AI, ML, Data Science terms you should know in 2020, including Double Descent, Ethics in AI, Explainability (Explainable AI), Full Stack Data Science, Geospatial, GPT-2, NLG (Natural Language Generation), PyTorch, Reinforcement Learning, and Transformer Architecture.
- Python and R Courses for Data Science [Silver Blog]
Since Python and R are a must for today's data scientists, continuous learning is paramount. Online courses are arguably the best and most flexible way to upskill throughout ones career.
- Probability Distributions in Data Science [Silver Blog]
Some machine learning models are designed to work best under some distribution assumptions. Therefore, knowing with which distributions we are working with can help us to identify which models are best to use.
- Learning from 3 big Data Science career mistakes [Gold Blog]
Thinking of data science as merely a technical profession, like programming, may take you away from your goals. We explain big mistakes to avoid, including not understanding the 2 cultures of statistics, and not understanding the shift to industrial focus.
- Free Mathematics Courses for Data Science & Machine Learning [Gold Blog]
It's no secret that mathematics is the foundation of data science. Here are a selection of courses to help increase your maths skills to excel in data science, machine learning, and beyond.
- 20 AI, Data Science, Machine Learning Terms You Need to Know in 2020 (Part 1) [Gold Blog]
2020 is well underway, and we bring you 20 AI, data science, and machine learning terms we should all be familiar with as the year marches onward.
- Fourier Transformation for a Data Scientist [Gold Blog]
The article contains a brief intro into Fourier transformation mathematically and its applications in AI.
- The Data Science Puzzle — 2020 Edition [Silver Blog]
The data science puzzle is once again re-examined through the relationship between several key concepts of the landscape, incorporating updates and observations since last time. Check out the results here.
- Data Validation for Machine Learning [Silver Blog]
While the validation process cannot directly find what is wrong, the process can show us sometimes that there is a problem with the stability of the model.
- Top 9 Mobile Apps for Learning and Practicing Data Science [Silver Blog]
This article will tell you about the top 9 mobile apps that help the user in learning and practicing data science and hence is improving their productivity.
- 7 Resources to Becoming a Data Engineer [Gold Blog]
An estimated 8,650% growth of the volume of Data to 175 zetabytes from 2010 to 2025 has created an enormous need for Data Engineers to build an organization's big data platform to be fast, efficient and scalable.
- How To “Ultralearn” Data Science: summary, for those in a hurry [Gold Blog]
For those of you in a hurry and interested in ultralearning (which should be all of you), this recap reviews the approach and summarizes its key elements -- focus, optimization, and deep understanding with experimentation -- geared toward learning Data Science.
- What is a Data Scientist Worth? [Platinum Blog]
What is the Salary of a Data Scientist in 2019? Let's have a look at some data to see how we can answer that question.
- 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.
- AI, Analytics, Machine Learning, Data Science, Deep Learning Technology Main Developments in 2019 and Key Trends for 2020 [Silver Blog]
We asked leading experts - what are the most important developments of 2019 and 2020 key trends in AI, Analytics, Machine Learning, Data Science, and Deep Learning? This blog focuses mainly on technology and deployment.
- The 4 Hottest Trends in Data Science for 2020 [Silver Blog]
The field of Data Science is growing with new capabilities and reach into every industry. With digital transformations occurring in organizations around the world, 2019 included trends of more companies leveraging more data to make better decisions. Check out these next trends in Data Science expected to take off in 2020.
- AI, Analytics, Machine Learning, Data Science, Deep Learning Research Main Developments in 2019 and Key Trends for 2020 [Gold Blog]
As we say goodbye to one year and look forward to another, KDnuggets has once again solicited opinions from numerous research & technology experts as to the most important developments of 2019 and their 2020 key trend predictions.
- Data Science Curriculum Roadmap [Silver Blog]
What follows is a set of broad recommendations, and it will inevitably require a lot of adjustments in each implementation. Given that caveat, here are our curriculum recommendations.
- A Non-Technical Reading List for Data Science [Silver Blog]
The world still cannot be reduced to numbers on a page because human beings are still the ones making all the decisions. So, the best data scientists understand the numbers and the people. Check out these great data science books that will make you a better data scientist without delving into the technical details.