- Continuous learning projects to complete using K.A.T.E.®

Cover the most industry-relevant skills and cutting-edge techniques in Data Science that can be applied to:

a) Make better decisions faster

b) Lower costs with increased operational efficiency

c) Increase revenue by developing new product offerings

Python Courses include:

Pragmatic Python Programming (2 day course) -

Python fundamentals, interacting with APIs, data processing and code architecture

Python for Analysts (2 day course) -

Intensive conversion training courses for developers writing and maintaining Python projects in Production.

Python for Engineers (2 day course) -

Intensive Python conversion training course for developers familiar with C

Data Science courses include:

Core Data Science (3 day course) -

Exploratory Data Analysis and visualisation, feature engineering, unsupervised learning and supervised learning

Data Science in Production (3 day course) -

Preprocessing, Exploratory Data Analysis and visualisation, feature engineering, unsupervised learning and supervised learning, model evaluation and tuning, random forests and logistic regression.

Machine Learning and Deep Learning (2 day course) -

Model evaluation, decision trees, random forests, logistic regression, SVMs, neural networks and deep learning

Apache Spark in Practice (2 day course) -

Learn how to work with Spark (RDDs, Dataframes and SQL) and Parquet

Pragmatic Model Interpretation (1 day course) -

LIME, SHAP, ELI5 and best practices for interpreting and evaluating machine learning models

Regression and Time Series Analysis (2 day course) -

Linear regression, nonlinear regression, auto-regressive models, time series analysis and regularisation

Specialised Data Science courses include:

Cloud Computing and Databases (2 day course) -

Th course covers working SQL to query structured data and NoSQL. In particular, the second focuses on document-oriented storage and graph databases.

Neural Networks and Deep Learning (2 day course) -

This course is designed to ensure your team gains a strong understanding of how neural networks are constructed and how to apply them using Keras. Participants will learn about neural network architectures, ranging from simple single-layer architectures to deep learning architectures including CNNs and RNNs.

Natural Language Processing (2 day course) -

This course is designed to provide your team with hands-on training in text processing, semantic analysis and sophisticated Machine Learning approaches.

Recommender Systems, Interpretability and Evaluation (2 day course) -

This course is split into three core sections, building Recommender Systems, followed by model evaluation and interpretability. We cover different types of Recommender Systems such as Collaborative Recommender systems, Content-based recommender systems and Hybrid recommender systems.