Thinking Analytics run professional courses and workshops on Data Science and Advanced Analytics. Our trainers are ML and Big Data Analytics researchers engaged in theoretical and applied research, publications featured in numerous publications and cited in University textbooks.
The courses run both in-person and online. Online courses cover major time zones. Thinking Analytics also provide custom training for organisations.
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Modules
Business Analytics and Technology
This course is designed for budding Business Analyst.
In this course, you will learn some main concepts of Business Analytics. The objection is to provide students with the basic knowledge and skills required to be business analysts. The course will introduce the concepts, terminologies, tools and technologies of business analytics, and develop the skills necessary to analyse business situations and solve business problems using appropriate business analytics methods. This course will cover Predictive analytics (Linear Regression and Logistic Regression), Descriptive analysis (Clustering, Association Rules), Forecasting (Moving Averages, Time Series), and Visualisation using Excel.
Big Data Analytics
This course is designed to for Data Scientists who would like to advance their skills in Data Analytics and Machine Learning.
This course aims to provide students with the basic knowledge and skills required to be Big Data Analysts. Students will be exposed to some of the most widely used and successful supervised (Linear Regression, Logistic Regression, Support Vector Machines, Neural Networks) and unsupervised (K-Means, Clustering, PCA) learning techniques for Big Data Analytics. Students will be taught to apply best practice supervised and unsupervised learning models to solve data science problems, and to implement a range of supervised and unsupervised learning algorithms to explore and find structure in labelled and unlabelled data using MATLAB.
Deep Learning
This course is designed for the Data Scientist who would like to develop their theoretical and technical skills in Deep Learning.
This course will introduce the field of deep learning. In particular, students will cover a range of topics from basic neural networks, convolutional and recurrent network structures, deep unsupervised and reinforcement learning, and applications to problem domains like speech recognition and computer vision. Prerequisites: a strong mathematical background in calculus, linear algebra, and probability & statistics as well as programming in Python.
Machine Learning Applications
The course in intended for the Data Scientist with a keen interest in the field of Machine Learning.
This course explores the core principles and key applications of machine learning. During this course, students will gain an in-depth understanding of a variety of machine learning techniques that can be applied when analysing big data including regression, classification, tree-based methods, ensemble learning, support vector machines, clustering, and neural networks. In addition, students will learn how to match suitable machine learning techniques and solutions to particular problems.