Part 1 - Introduction to Python for Data Science
Days 1-3- Introduction to Python Programming for Data Science
- Installing and Working with Anaconda.
- Variables, Lists, Arrays, Dictionaries, etc.
- Control and Looping Structures.
- Methods.
- Reading and Saving Data.
- Data handling.
Part 2 - Data Science with Python
Day 4 – Data Visualisation and Statistics in Python
- Concepts of Statistics.
- Basis of Experimentation, normalisation, and random sampling.
- Hypothesis testing, confidence intervals, interpretation of p-values.
- Data Visualisation.
Day 5 – 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 6 – Model Training and Validation
- Classification as a supervised Learning Example.
- Logistic Regression.
- Formalisation of Training and Model Validation Approaches.
Day 7 – Clustering and Model Selection
- Clustering and Data Annotation.
- Data Quality Verification - Inter-rater and Intra-Rater Comparisons.
- Measures for Model Selection (t-tests, ANOVA, Kohen Kappa, etc.)
Day 8 – Classification Methods
- Further Methods for Classification of Tabular Data.
- Decision Trees and Random Forests as Ensemble Models.
Day 9 - Modelling of Temporal Data
- Continuous and Discrete State-Space Temporal Data.
- Continuous State-Space Data: AR, MA, ARMA and ARIMA Models.
- Discrete State-Space Data: Markov Chains Models.
Day 10 – Neural Network and Deep Learning Models
- Feedforward Neural Networks and the Back Propagation Algorithm.
- Various Deep Neural Network such as CNNs and RNNs with Examples.
