Semi-Private Python Data Science Course Online
- Why data science?
- Why Python for data science?
- Introduce different packages – Numpy, Pandas, Matplotlib, Scikit-learn
- Setup environment (install Anaconda and toy example datasets for practice tasks)
- Identify what tutees will need data science for
- Inputting/storing data (reading CSVs and other file types)
- Cleaning data (what happens if there are missing values?)
- Unstructured data (Pandas Dataframes)
- Introduction to Python with SQL Databases
- Different plots (line, bar, box, scatter) in Matplotlib
- Editing graphs (styles, colours, labels etc.)
- Calculating averages, variance etc.
- Probability distributions (normal, binomial, poisson)
- Hypothesis testing
- Supervised (linear regression, neural networks, decision trees, naïve bayes, k nearest neighbours)
- Introduction to Tensorflow and Pytorch
- Unsupervised (clustering, outlier detection)
- Testing to improve accuracy (using statistics)
- Visualising machine learning models
- How to explain the workings of machine learning models
- Combining processing, visualising, statistics and machine learning lessons
- Practice tasks
- Answering any questions and identify real business needs