Python Cheat Sheet for Data Science

Pandas, Numpy, and Scikit-Learn are among the most popular libraries for data science and analysis with Python. Numpy is used for lower level scientific computation. Pandas is built on top of Numpy and designed for practical data analysis in Python. Scikit-Learn comes with many machine learning models that you can use out of the box.…

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How Data Science Can Nearly Double Your Income, with Zachary Washam

In data science, knowledge is not power… applied knowledge is power. Businesses want to know if you can deliver results that impact the bottom line. But how exactly can you do so? Are hard skills enough? What practical steps can you take to help your organization thrive? We explore these questions and much more in…

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Open Source vs Commercial Machine Learning Software

At the start of any machine learning project, you face an important choice: Which language or software should I use? Well, you have many options to choose from. Python, R, SAS, MATLAB… the list goes on. But first, you’ll actually need to make another choice: Should I go with open source or commercial software? Open…

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How to Write the Perfect Data Scientist Resume

As data scientists, we often obsess over numbers and conversion rates… and that’s a good thing! A job search is just a numbers game with plenty of conversion rates. In fact, you can optimize the conversion rate between each step of the application process: Applications ⇒ Interviews ⇒ Job Offers Today, we’ll look at how you can…

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21 Machine Learning Interview Questions and Answers

If you want to land a job in data science, you’ll need to pass a rigorous and competitive interview process. In fact, most top companies will have at least 3 rounds of interviews. During the process, you’ll be tested for a variety of skills, including: Your technical and programming skills Your ability to structure solutions…

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8 Fun Machine Learning Projects for Beginners

In this guide, we’ll be walking through 8 fun machine learning projects for beginners. Projects are some of the best investments of your time. You’ll enjoy learning, stay motivated, and make faster progress. You see, no amount of theory can replace hands-on practice. Textbooks and lessons can lull you into a false belief of mastery because…

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Overfitting in Machine Learning: What It Is and How to Prevent It

Did you know that there’s one mistake… …that thousands of data science beginners unknowingly commit? And that this mistake can single-handedly ruin your machine learning model? No, that’s not an exaggeration. We’re talking about one of the trickiest obstacles in applied machine learning: overfitting. But don’t worry: In this guide, we’ll walk you through exactly…

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Datasets for Data Science and Machine Learning

These days, we have the opposite problem we had 5-10 years ago… Back then, it was actually difficult to find datasets for data science and machine learning projects. Since then, we’ve been flooded with lists and lists of datasets. Today, the problem is not finding datasets, but rather sifting through them to keep the relevant…

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How to Learn Python for Data Science in 2017 (Updated)

In this guide, we’ll cover how to learn Python for data science, including our favorite curriculum for self-study. You see, data science is about problem solving, exploration, and extracting valuable information from data. To do so effectively, you’ll need to wrangle datasets, train machine learning models, visualize results, and much more. Enter Python. This is the…

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Best Practices for Feature Engineering

Feature engineering, the process creating new input features for machine learning, is one of the most effective ways to improve predictive models. Coming up with features is difficult, time-consuming, requires expert knowledge. “Applied machine learning” is basically feature engineering. ~ Andrew Ng Through feature engineering, you can isolate key information, highlight patterns, and bring in…

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