Day 1 – Python Programming for Data Science - First steps
- Installing and Working with Anaconda
- Variables and operations
- User Input and console output
- Working with Strings and lists
- Working with Numbers
- Functions
Day 2 – Python Programming for Data Science - Loops and Control
- Tuples and sets
- Comparison operators
- Control structures
- Loop Structures
- Random number generation
Day 3 – Python Programming for Data Science - Arrays and files
- Reading and saving data
- Dictionaries and number arrays
- Plotting data
Day 4 – Python Programming for Data Science - Pandas data handling
- Pandas DataFrame
- Pandas functions
- Plotting data in Pandas
Day 5 – Data Statistics and Visualisation in Python
- Concepts of Statistics
- Basis of Experimentation, normalisation, and random sampling
- Hypothesis testing, confidence intervals, interpretation of p-values
- Data Visualisation
Day 6 – Classes and Libraries in Python
- Defining classes and objects in python
- Memory usage in python
Day 7 – Introduction to Data Science
- Introduction to Data Science
- Applications of Data Science
- Types of Data
- The Five Steps of Data Science
- Descriptive, Predictive, and Prescriptive Analytics
- Linear Regression as a First Model
- Mean Squared Error (MSE) as a Validation Metric
Day 8 – Models for Classification of data
- Classification as a supervised Learning Example
- The Logistic Regression
- Gradient descent learning
- Accuracy metric
Day 9 – Model Training and Validation
- Formalisation of Training and Model Validation Approaches
- Choosing the right model and validation metric
- Hyper-parameter optimisation
- Learning with small datasets
Day 10 – Capstone Project
- Synoptic exercise: Students will apply their knowledge gained in previous sessions to a case study
- Use Python programming, apply statistics, build a predictive model, interpret and visualise results
