Description

Semi-Private Python Data Science Course Online

Introduction

• Why data science?
• Uses
• 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

Processing data

• 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

Visualising data

• Different plots (line, bar, box, scatter) in Matplotlib
• Editing graphs (styles, colours, labels etc.)

Statistics

• Calculating averages, variance etc.
• Probability distributions (normal, binomial, poisson)
• Hypothesis testing

Machine Learning

• 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

Summary

• Combining processing, visualising, statistics and machine learning lessons
• Practice tasks
• Answering any questions and identify real business needs