Learning Path: Your mentor to become a machine learning expertCurricula / References, Learning Guides, Etc.
From Analytics Vidhya: “Through this learning path, we hope to provide you an answer to this problem. We have deliberately loaded this learning path with a lot of practical projects. You can not master machine learning with the hard work! But once you do, you are one of the highly sought after people around. Since this is a complex topic, we recommend you to strictly follow the steps in sequential … Continue Reading
Comprehensive learning path – Data Science in PythonCurricula / References, Learning Guides, Etc.
From Analytics Vidhya: “So, you want to become a data scientist or may be you are already one and want to expand your tool repository. You have landed at the right place. The aim of this page is to provide a comprehensive learning path to people new to python for data analysis. This path provides a comprehensive overview of steps you need to learn to use Python for data analysis. … Continue Reading
DataQuest Data Science BlogBlogs / Blogs, Podcasts, Etc.
Written by the makers of Dataquest.io, a data science tutorial site, the blog covers topics across data science from those curious about the field to specific tips for advanced users. Posts include: tips and tutorials for using Python, R, SQL, Kaggle, cheat sheets, definitions of data science terms, links to resources, data science success stories, and general advice on entering the data science field.
DataQuest – Advanced Topics in Data ScienceCourses / Interactive tutorial style course
This is step 8 of 11 in the DataQuest Data Scientist Path. “Learn extra topics like NLP and how to participate in Kaggle competitions.” The curriculum for this step includes: Kaggle Competitions Getting Started with Kaggle Improving your Submission Exploring Topics in Data Science Naive Bayes for Sentiment Analysis An Introduction to K-Nearest Neighbors Natural Language Processing
Doing Data Science: Straight Talk from the FrontlineBooks / physical books or multiple formats
How can you get started working in a wide-ranging, interdisciplinary field that’s so clouded in hype? This insightful book, based on Columbia University’s Introduction to Data Science class, tells you what you need to know.