- Statistics with Julia: The Free eBook [Silver Blog]
This free eBook is a draft copy of the upcoming Statistics with Julia: Fundamentals for Data Science, Machine Learning and Artificial Intelligence. Interested in learning Julia for data science? This might be the best intro out there.
- Top Online Masters in Analytics, Business Analytics, Data Science – Updated [Gold Blog]
We provide an updated list of best online Masters in AI, Analytics, and Data Science, including rankings, tuition, and duration of the education program.
- If I had to start learning Data Science again, how would I do it? [Platinum Blog]
While different ways to learn Data Science for the first time exist, the approach that works for you should be based on how you learn best. One powerful method is to evolve your learning from simple practice into complex foundations, as outlined in this learning path recommended by a physicist who turned into a Data Scientist.
- The List of Top 10 Lists in Data Science [Gold Blog]
The list of Top 10 lists that Data Scientists -- from enthusiasts to those who want to jump start a career -- must know to smoothly navigate a path through this field.
- Going Beyond Superficial: Data Science MOOCs with Substance [Silver Blog]
Data science MOOCs are superficial. At least, a lot of them are. What are your options when looking for something more substantive?
- Unit Test Your Data Pipeline, You Will Thank Yourself Later [Silver Blog]
While you cannot test model output, at least you should test that inputs are correct. Compared to the time you invest in writing unit tests, good pieces of simple tests will save you much more time later, especially when working on large projects or big data.
- New Poll: Which Data Science Skills You Have and Which Ones You Want? Vote Now [Silver Blog]
Take part in the latest KDnuggets poll, and share your insights with the community. Which Data Science skills do you currently possess, and which are you looking forward to add or improve upon? Vote now!
- Setting Up Your Data Science & Machine Learning Capability in Python [Silver Blog]
With the rich and dynamic ecosystem of Python continuing to be a leading programming language for data science and machine learning, establishing and maintaining a cost-effective development environment is crucial to your business impact. So, do you rent or buy? This overview considers the hidden and obvious factors involved in selecting and implementing your Python platform.
- Know What Employers are Expecting for a Data Scientist Role in 2020 [Platinum Blog]
The analysis is done from 1000+ recent Data scientist jobs, extracted from job portals using web scraping.
- First Steps of a Data Science Project [Silver Blog]
Many data science projects are launched with good intentions, but fail to deliver because the correct process is not understood. To achieve good performance and results in this work, the first steps must include clearly defining goals and outcomes, collecting data, and preparing and exploring the data. This is all about solving problems, which requires a systematic process.
- Data Science MOOCs are too Superficial [Platinum Blog]
Most massive open online courses are too superficial because they offer introductory-level courses. For in-depth knowledge, more is needed to increase your knowledge and expertise after establishing a foundation.
- A Layman’s Guide to Data Science. Part 3: Data Science Workflow [Gold Blog]
Learn and appreciate the typical workflow for a data science project, including data preparation (extraction, cleaning, and understanding), analysis (modeling), reflection (finding new paths), and communication of the results to others.
- How Much Math do you need in Data Science? [Platinum Blog]
There exist so many great computational tools available for Data Scientists to perform their work. However, mathematical skills are still essential in data science and machine learning because these tools will only be black-boxes for which you will not be able to ask core analytical questions without a theoretical foundation.
- Five Cognitive Biases In Data Science (And how to avoid them) [Silver Blog]
Everyone is prey to cognitive biases that skew thinking, but data scientists must prevent them from spoiling their work. Learn more about five biases that can all too easily make your seemingly objective work become surprisingly subjective.
- Don’t Democratize Data Science [Gold Blog]
A plethora of online courses and tools promise to democratize the field, but just learning a few basic skills does not a true data scientist make.
- 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.
- Five Cool Python Libraries for Data Science [Gold Blog]
Check out these 5 cool Python libraries that the author has come across during an NLP project, and which have made their life easier.
- Should Data Scientists Model COVID19 and other Biological Events [Silver Blog]
Biostatisticians use statistical techniques that your current everyday data scientists have probably never heard of. This is a great example where lack of domain knowledge exposes you as someone that does not know what they are doing and are merely hopping on a trend.
- Free High-Quality Machine Learning & Data Science Books & Courses: Quarantine Edition [Gold Blog]
If you find yourself quarantined and looking for free learning materials in the way of books and courses to sharpen your data science and machine learning skills, this collection of articles I have previously written curating such things is for you.
- Can Java Be Used for Machine Learning and Data Science? [Gold Blog]
While Python and R have become favorites for building these programs, many organizations are turning to Java application development to meet their needs. Read on to see how, and why.
- Peer Reviewing Data Science Projects [Silver Blog]
In any technical development field, having other practitioners review your work before shipping code off to production is a valuable support tool to make sure your work is error-proof. Even through your preparation for the review, improvements might be discovered and then other issues that escaped your awareness can be spotted by outsiders. This peer scrutiny can also be applied to Data Science, and this article outlines a process that you can experiment with in your team.