Topic: Data Science
This page features most recent and most popular posts on Data Science.
Latest posts on Data Science
- Python Data Structures Compared - Jul 27, 2021Let's take a look at 5 different Python data structures and see how they could be used to store data we might be processing in our everyday tasks, as well as the relative memory they use for storage and time they take to create and access.
- Not Only for Deep Learning: How GPUs Accelerate Data Science & Data Analytics - Jul 26, 2021Modern AI/ML systems’ success has been critically dependent on their ability to process massive amounts of raw data in a parallel fashion using task-optimized hardware. Can we leverage the power of GPU and distributed computing for regular data processing jobs too?
- 5 Mistakes I Wish I Had Avoided in My Data Science Career - Jul 26, 2021Everyone makes mistakes, which can be a good thing when they lead to learning and improvements over time. But, we can also try to first learn from others to expedite our personal growth. To get started, consider these lessons learned the hard way, so you don’t have to.
- Why and how should you learn “Productive Data Science”? - Jul 26, 2021What is Productive Data Science and what are some of its components?
- Top Python Data Science Interview Questions - Jul 23, 2021Six must-know technical concepts and two types of questions to test them.
Most popular (badge-winning) recent posts on Data Science
- Advice for Learning Data Science from Google’s Director of Research [Silver Blog]Surfing the professional career wave in data science is a hot prospect for many looking to get their start in the world. The digital revolution continues to create many exciting new opportunities. But, jumping in too fast without fully establishing your foundational skills can be detrimental to your success, as is suggested by this advice for data science newbies from Peter Norvig, the Director of Research at Google.
- 5 Lessons McKinsey Taught Me That Will Make You a Better Data Scientist [Gold Blog]How to stand out from your peers in the data world.
- Managing Your Reusable Python Code as a Data Scientist [Silver Blog]Here are a few approaches that I have settled on for managing my own reusable Python code as a data scientist, presented from most to least general code use, and aimed at beginners.
- What will the demand for Data Scientists be in 10 years? Will Data Scientists be extinct? [Gold Blog]Participate in the latest KDnuggets survey and share your opinion: what does the next decade have in store for data scientist demand?
- Data Scientists Will be Extinct in 10 Years [Platinum Blog]And why it’s not a bad thing.
- Top 10 Data Science Projects for Beginners [Gold Blog]Check out these projects for ideas to strengthen your skills and build a portfolio that stands out.
- Will There Be a Shortage of Data Science Jobs in the Next 5 Years? [Gold Blog]The data science workflow is getting automated day by day.
- How I Doubled My Income with Data Science and Machine Learning [Gold Blog]Many career opportunities exist in the ever-expanding domain of data. Finding your place -- and finding your salary -- is largely up to your dedication, focus, and drive to learn. If you are an aspiring Data Scientist or have already started your professional journey, there are multiple strategies for maximizing your earning potential.
- A checklist to track your Data Science progress [Silver Blog]Whether you are just starting out in data science or already a gainfully-employed professional, always learning more to advance through state-of-the-art techniques is part of the adventure. But, it can be challenging to track of your progress and keep an eye on what's next. Follow this checklist to help you scale your expertise from entry-level to advanced.
- Data Scientist, Data Engineer & Other Data Careers, Explained [Platinum Blog]In this article, we will have a look at five distinct data careers, and hopefully provide some advice on how to get one's feet wet in this convoluted field.
- Data Preparation in SQL, with Cheat Sheet! [Gold Blog]If your raw data is in a SQL-based data lake, why spend the time and money to export the data into a new platform for data prep?
- Rebuilding My 7 Python Projects [Silver Blog]This is how I rebuilt My Python Projects: Data Science, Web Development & Android Apps.
- Why You Should Consider Being a Data Engineer Instead of a Data Scientist [Silver Blog]A new king of the jungle has emerged.
- Data Science Books You Should Start Reading in 2021 [Gold Blog]Check out this curated list of the best data science books for any level.
- How to ace A/B Testing Data Science Interviews [Silver Blog]Understanding the process of A/B testing and knowing how to discuss this approach during data science job interviews can give you a leg up over other candidates. This mock interview provides a step-by-step guide through how to demonstrate your mastery of the key concepts and logical considerations.
- How to organize your data science project in 2021 [Gold Blog]Maintaining proper organization of all your data science projects will increase your productivity, minimize errors, and increase your development efficiency. This tutorial will guide you through a framework on how to keep everything in order on your local machine and in the cloud.
- Top 3 Statistical Paradoxes in Data Science [Silver Blog]Observation bias and sub-group differences generate statistical paradoxes.
- Awesome Tricks And Best Practices From Kaggle [Gold Blog]Easily learn what is only learned by hours of search and exploration.
- What’s ETL? [Silver Blog]Discover what ETL is, and see in what ways it’s critical for data science.
- Top 10 Python Libraries Data Scientists should know in 2021 [Platinum Blog]So many Python libraries exist that offer powerful and efficient foundations for supporting your data science work and machine learning model development. While the list may seem overwhelming, there are certain libraries you should focus your time on, as they are some of the most commonly used today.
- How to Succeed in Becoming a Freelance Data Scientist [Platinum Blog]With recent growth in data science, now is the best time to get into freelancing. The following steps will help you get started with finding clients or help you improve your current strategy.
