Duration

Advanced Data Analysis and Machine Learning with Python

5 days

Overview

This advanced-level course is designed for individuals with a solid foundation in Python for data analysis and who want to delve deeper into advanced techniques, machine learning, and statistical modeling. Participants will explore advanced libraries such as Scikit-learn and Statsmodels for machine learning and statistical analysis, gaining hands-on experience in solving complex data problems.

Prerequisites

Participants should have completed an intermediate-level Python for data analysis course or have equivalent experience. Familiarity with basic statistics and machine learning concepts is recommended.

Lesson 1: Advanced NumPy Operations

Broadcasting, advanced array manipulation
Vectorized functions and universal functions

Lesson 2: Advanced Pandas Features

MultiIndex and advanced indexing
Time series analysis with Pandas

Lesson 1: Introduction to Time Series Analysis

Stationarity, autocorrelation, and seasonality
Decomposition and trend analysis

Lesson 2: Advanced Time Series Models

ARIMA and SARIMA models
Exponential smoothing techniques

Lesson 1: Introduction to Statsmodels

Building and interpreting statistical models
Linear and logistic regression with Statsmodels

Lesson 2: Advanced Statistical Models

Generalized Linear Models (GLM)
Time series modeling with Statsmodels

Lesson 1: Introduction to Machine Learning

Supervised vs. unsupervised learning
Model evaluation and selection

Lesson 2: Feature Engineering and Selection

Extracting and selecting relevant features
Dimensionality reduction techniques

Lesson 1: Regression Models

Linear regression, ridge regression, and LASSO
Evaluation metrics for regression

Lesson 2: Classification Models

Logistic regression, decision trees, and ensemble methods
Evaluation metrics for classification

Lesson 1: Clustering Algorithms

K-means clustering, hierarchical clustering
Evaluation metrics for clustering

Lesson 2: Dimensionality Reduction

Principal Component Analysis (PCA)
t-Distributed Stochastic Neighbor Embedding (t-SNE)

Lesson 1: Support Vector Machines (SVM)

Understanding SVM and kernel tricks
Applications in classification and regression

Lesson 2: Neural Networks and Deep Learning

Introduction to neural networks
Building and training deep learning models with TensorFlow and Keras

Lesson 1: Model Deployment

Exporting and deploying machine learning models
Integrating models into web applications

Lesson 2: Scaling Data Analysis Workflows

Parallel computing with Dask
Cloud-based data analysis with Google Colab or AWS

Lesson 1: Interactive Data Visualization with Plotly

Creating interactive dashboards
Visualizing geospatial data

Lesson 2: Customizing Seaborn Plots

Styling and theming Seaborn visualizations
Advanced plot types and customization

Lesson 1: Case Studies in Advanced Data Analysis

Solving complex data problems with advanced techniques
Applications in various industries

Lesson 2: Group Projects and Collaboration

Collaborative projects to solve real-world challenges
Presenting solutions and receiving feedback

Programme scope, delivery arrangements and suitability are confirmed during enquiry.