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Cambridge Spark is a leader in personalised Data Science education. We offer intensive Bootcamps, Apprenticeships, Graduate Schemes and Bespoke Corporate Training that are delivered by industry and academic experts, complimented by our industry-leading proprietary education platform, K.A.T.E.®.
In-person training delivered at your office by an industry expert. Highly practical course materials focused on the workflow of a Data Scientist including: - Interactive Python notebooks with exercises and solutions - 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.
We offer an Apprenticeship Levy approved 13-month flexible programme, delivered through a mix of in-person and online training. We offer two tracks: 1. Data Analyst 2. Data Science Students are given the chance to gain Data Science Associate certification from EMC and develop Portfolio of projects throughout. Some of the projects include: - Kickstarter campaign success predictor - Bitcoin time series forecasting - Customer segmentation - EDA using PySpark Learn more here now: https://cambridgespark.com/apprenticeship/
K.A.T.E.®, Cambridge Sparks' proprietary AI-powered adaptive learning platform, assesses its students code submissions to provide instant feedback on the quality of the code, as well as suggest learning resources to help improve any areas that require developing - enabling them to iteratively make improvements to their skills and create production-ready code. K.A.T.E.® also adapts the level of exercise difficulty, dependant on the individuals performance on previous exercises undertaken, creating a more personalised learning experience. The platform enables organisations, from SME's to large corporations, to scale their Data Science training - enabling them to professionally develop new and existing staff to create an immediate impact to their teams as they work through training modules. Visit the Edukate site now to learn more: https://edukate.ai
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