Scikit-learn vs. StatsModels: Which, why, and how?
StatsModels & Scikit-learn are two popular packages for working with stats and machine learning in Python. Learn more about each from The Data Incubator.
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StatsModels & Scikit-learn are two popular packages for working with stats and machine learning in Python. Learn more about each from The Data Incubator.
Artificial intelligence (AI) isn’t going to steal your data scientist job! Instead, AI tools like ChatGPT can automate some of the more mundane tasks in your future career, saving you time and energy. To make life easier, here are some data science prompts to get you started.
You have a programming background. So do data scientists. So switching to a career in data science should be pretty easy, right? Yes, and no. While it’s not a seamless transition, it’s certainly doable, and we’ll tell you how.
You’ll hear the same things about data science time and time again—that you can get an entry-level position making six figures, you’ll never be out of work and it’s one of the most sought-after careers in the world. But one thing nobody mentions is how stressful this career can be at times. So, how stressful is it really?
TDI graduates talk about their experiences with the program, what the program prepared them for and share how TDI helped them bring their data science ambitions to life.
You might think data science is a young person’s game. After all, this is a relatively new discipline that might not have been around when you were in school. But research shows nearly half of all data scientists are 40 years and older. Whatever your age, it’s never too late to pursue your dreams of becoming a qualified data scientist. Learn how to succeed in this profession in this blog.
If you plan to become a data scientist, it’s critical to understand the storytelling techniques that will help you present data to marketers, directors, investors and stakeholders. You won’t just tell your audience about algorithms and hard numbers but explain the phenomena behind them. Learn more about data storytelling here.
Python and R are two of the most common languages for data science and machine learning. TDI can help you understand the strengths and weaknesses of each.
Data science lives at the intersection of a variety of different fields including programming, mathematics, statistics, machine learning, computer science, software development, traditional research and domain knowledge.