Data Science Tutorials & Resources for Beginners
If you want to know more about Data Science but don’t know where to start this list is for you! 📈
No previous knowledge required but Python and statistics basics will definitely come in handy. These ressources have been used successfully for many beginners at my local Data Science student group ML-KA.
What is Data Science?
- ‘What is Data Science?’ on Quora
- Explanation of important vocabulary – Differentiation of Big Data, Machine Learning, Data Science.
- Data Science for Business (Book) – An introduction to Data Science and its use as a business asset.
Common Algorithms and Procedures
- Supervised vs unsupervised learning – The two most common types of Machine Learning algorithms.
- 9 important Data Science algorithms and their implementation
- Cross validation – Evaluate the performance of your algorithm / model.
- Feature engineering – Modifying the data to better model predictions.
- Scientific introduction to 10 important Data Science algorithms
- Model ensemble: Explanation – Combine multiple models into one for better performance.
Data Science using Python
This list covers only Python, as many are already familiar with this language. Data Science tutorials using R.
- O’Reilly Data Science from Scratch (Book) – Data processing, implementation, and visualization with example code.
- Coursera Applied Data Science – Online Course using Python that covers most of the relevant toolkits.
numpy is a Python library which provides large multidimensional arrays and fast mathematical operations on them.
pandas provides efficient data structures and analysis tools for Python. It is build on top of numpy.
- Introduction to pandas
- DataCamp pandas foundations – Paid course, but 30 free days upon account creation (enough to complete course).
- Pandas cheatsheet – Quick overview over the most important functions.
scikit-learn is the most common library for Machine Learning and Data Science in Python.
- Introduction and first model application
- Rough guide for choosing estimators
- Scikit-learn complete user guide
- Model ensemble: Implementation in Python
Jupyter Notebook is a web application for easy data visualisation and code presentation.
- Downloading and running first Jupyter notebook
- Example notebook for data exploration
- Seaborn data visualization tutorial – Plot library that works great with Jupyter.
Various other helpful tools and resources
- Template folder structure for organizing Data Science projects
- Anaconda Python distribution – Contains most of the important Python packages for Data Science.
- Spacy – Open source toolkit for working with text-based data.
- LightGBM gradient boosting framework – Successfully used in many Kaggle challenges.
- Amazon AWS – Rent cloud servers for more timeconsuming calculations (r4.xlarge server is a good place to start).
Data Science Challenges for Beginners
Sorted by increasing complexity.
- Walkthrough: House prices challenge – Walkthrough through a simple challenge on house prices.
- Blood Donation Challenge – Predict if a donor will donate again.
- Titanic Challenge – Predict survival on the Titanic.
- Water Pump Challenge – Predict the operating condition of water pumps in Africa.