Over the last few years, endjin has helped numerous organizations benefit from applying data science and machine learning tools and processes to difficult business questions.
Through running Microsoft's Advanced Analytics Laboratories, executing data science experiments, enabling a team through hands-on workshops, and productionizing existing data science solutions into the Cloud, endjin has a wealth of experience and expertise in the full spectrum of data science.
We believe that effective data scientists aren't created in intense 6-week training courses, but through a combination of technical theory, domain knowledge, process understanding, and people skills that can only be obtained over time through continued application and experience.
So, we won't claim to make you a 'Data Scientist' just through completion of this course. Rather, the course will provide a detailed, interactive overview of all these facets of Data Science, from endjin’s unique perspective, offering a syllabus whose interactive content has been specifically designed to be highly compatible with the problems businesses face today.
In today’s data driven world, it is more important than ever to utilize data at hand in order to aid business understanding, draw meaningful business insights, and, most importantly, keep up to speed with your industry and competitors. Data Science defines the process behind identifying, understanding and solving data-related enterprise problems.
There is a myriad of courses available that focus on the technical and theoretical side of Data Science, but whilst this course also includes comprehensive technical information, it also pays particular attention to the business drivers behind Data Science – understanding different domain problems, identifying and investigating potential problem sources, following a structured process and communicating and presenting findings to stakeholders.
This course is targetted at anyone looking for a comprehensive overview of the different aspects of working as a professional data scientist; it is deliberately focused around practical business applications of data science, rather than academic research. It would also be useful to someone who's looking to build out a new data science capability within a team. Some modules are technical in nature, but the course is designed to explain complex topics as they are introduced, so whilst experience in statistics, data manipulation, and programming are beneficial, they are not a pre-requisite for taking this course.
1. The Data Science Process
From knowing where to start and defining a testable hypothesis through to developing, training and evaluating predictive models - this module will guide you through a structured approach to experimentation and demonstrate why the application of this process is the real science in Data Science.
2. Data Mining Theory
From understanding the problem type and the models that can be applied, to the techniques that can be used in preparing data, this module covers the theory behind all aspects of data preparation, processing and modelling to discover patterns and extract meaningful insight.
3. Tools and Technology
From getting the most out of well-known tools like Excel, to data science programming languages like R and Python, documentation and experimentation with Juptyer Noteooks, data visualisations with Power BI, and cloud hosted software-as-a-service platforms like Azure Machine Learning Studio, this module looks at the Data Scientist workbench and how to use the right tool for each job.
4. Productionisation and Operations
How do you turn an effective model into a production quality data science solution? This module looks at how, why and when non-functional aspects like automation, scale, testing and support should be considered, and using tools like Microsoft R Server Operationalization, Azure Data Factory and Azure Machine Learning, demonstrates options and approaches for architecting and deploying end to end data science solutions so that your business can depend on the insights that you're surfacing.
5. Real World Business Problems
What are highest priority questions that businesses are looking to answer in today's data driven world? And how does a Data Scientist go about tackling them? This module explains some of the most common data science business scenarios - including customer propensity, predictive maintenance, anomaly detection, customer churn, recommendations and sentiment analysis - and provides end to end walkthroughs of how they can be approached.
6. Assets and Extras
As well as the modules described above, included with this course is a set of data science assets that can be used to kick start your journey - lab notebook templates to document your experiments, sample experiments with end to end walkthrough videos, and scripts and templates to help with productionizing existing R models. In addition, you'll have access to a dedicated, private Slack channel to ask questions along the way and discuss the content with other course attendees.
— Marko Trninic, Solutions Africa: Innovation & Analytics Director, AB InBev