Here’s what helped me succeed in my immersive program.
While I was completing an immersive data science program, I often found myself turning to additional resources to solidify my understanding of key concepts and practices. Below is a list of resources that I found useful and complimentary to my studies. This list is a solid starting place for anyone interested in the field of data science or those looking to dive in deeper to a specific area.
Kaggle is a platform for participating in machine learning competitions. Participants are tasked with creating their best model from a given dataset and prompt, and the winners can earn cash prizes and recognition.
But Kaggle is much more than a competition website — it also hosts Kernels/Notebooks with thousands of user examples of data cleaning, exploratory data analysis (EDA) and training machine learning (ML) algorithms.
Kaggle also contains thousands of readily available datasets on a variety of topics that can be easily downloaded to practice data cleaning, EDA and/or building ML models. Kaggle was an essential tool for me to get hands-on practice and often a great resource for finding data when I had a project in mind!
StatQuest — YouTube channel
Josh Starner created the StatQuest YouTube channel to explain essential topics in Statistics and Machine Learning in a fun* format that is entertaining and easy to digest. His videos cover a range of subjects from P-values to Gradient Descent to Support Vector Machines. (*catch phrases, music and original songs included).
HackerRank / LeetCode / CodeWars
HackerRank, LeetCode, and CodeWars are incredibly useful resources for practicing data science programming skills (Python, SQL) and improving your problem-solving abilities. All three websites host a variety of code challenges and brain teasers to test and hone your skills. These websites are also very helpful in preparing for interview code challenges.
Towards Data Science — Medium Publication
Towards Data Science (TDS) is Medium’s biggest publication covering all things data science. I’ve found TDS to be a great resource for:
- Tutorials with code (in languages such as Python, R, SQL, and more) that cover all steps in the data science process — data acquisition, cleaning, analysis, engineering and modeling as well as tutorials for using different frameworks and libraries.
- Walk-throughs of ML projects, ML algorithms and techniques.
- Latest news and product developments in the industry.
- Data Science career advice.
3Blue1Brown- Youtube channel
The 3Blue1Brown channel explains complex subjects within linear algebra, calculus and neural networks with an intuitive visual approach.
“The goal is for explanations to be driven by animations and for difficult problems to be made simple with changes in perspective.”
Python Data Science Handbook
Available free online.
Python for Data Analysis
Another great book also available here!
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow.
Unfortunately this one doesn’t appear to be hosted for free online, but it is available for purchase through Amazon.
This book requires some prior knowledge of Python and explains some of the most-common ML libraries like Scikit-Learn, Keras, and TensorFlow. It covers regression, classification, neural nets, time-series handling, unsupervised machine learning algorithms and much more. There are also multiple hands-on project walk-throughs, start to finish, that are really helpful in learning the Data Science process.
These are just a few of the resources that I found to be really helpful in learning Data Science and resources that I still continue to use daily. I will update this list as more come to mind. Please leave a comment if there are any resources that you would recommend!