AlphaZetta Academy

All Training Courses2019-02-22T01:12:11+00:00

All Training Courses

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Data Literacy for Everyone

With the advent of automation, humans’ role has become to do what computers cannot. Many more white-collar workers—perhaps all of them—will end up “working with data” to some extent. This course for managers and workers without a strong quantitative background introduces a range of skills and applications related to critical thinking in such areas as forecasting, population measurement, set theory and logic, causal impact and attribution, scientific reasoning and the danger of cognitive biases. There are no prerequisites beyond high-school mathematics; this course has been designed to be approachable for everyone.


Fundamentals of AI, Machine Learning, Data Science and Predictive Analytics

Our leading course has transformed the ai, machine-learning and data-science practice of the many managers, sponsors, key stakeholders, entrepreneurs and beginning data-science practitioners who have attended it. This course is an intuitive, hands-on introduction to ai, data science and machine learning. The training focuses on central concepts and key skills, leaving the trainee with a deep understanding of the foundations of ai and data science and even some of the more advanced tools used in the field. The course does not involve coding, or require any coding knowledge or experience.

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Best Practices in Enterprise Information Management

The effective management of enterprise information for analytics deployment requires best practices in the areas of people, processes, and technology. In this talk we will share both successful and unsuccessful practices in these areas. The scope of this workshop will involve five key areas of enterprise information management: (1) metadata management, (2) data quality management, (3) data security and privacy, (4) master data management, and (5) data integration.


Intro to R (+ data visualisation)

R is the world’s most popular data mining and statistics package. It’s also free, and easy to use, with a range of intuitive graphical interfaces. This two-day course will introduce you to the R programming language, teaching you to create functions and customise code so you can manipulate data and begin to use R self-sufficiently in your work.


Intro to Python for Data Analysis

Python is a high-level, general-purpose language used by a thriving community of millions. Data-science teams often use it in their production environments and analysis pipelines, and it’s the tool of choice for elite data-mining competition winners and deep-learning innovations. This course provides a foundation for using Python in exploratory data analysis and visualisation, and as a stepping stone to machine learning.


Overcoming Information Overload with Advanced Practices in Data Visualisation

In this workshop, we explore best practices in deriving insight from vast amounts of data using visualisation techniques. Examples from traditional data as well as an in-depth look at the underlying technologies for visualisation in support of geospatial analytics will be undertaken. We will examine visualisation for both strategic and operational BI.


Soft skills for analytics professionals and data scientists

This course is for specialists working in the data domain. It teaches some of the vital skills that are not part of the formal training of quantitative professionals, which are essential in the modern workplace and crucial to the success of analytics efforts as well as the careers of analytics professionals.

Issues covered include effective communication, including presentation and communication skills, “storytelling”, and effective listening and elicitation. Technical methods will be presented in the context of their communication value.

The course also shares key insights and “trade secrets” that have served Eugene Dubossarsky well over decades of consulting, enterprise and startup work. These include effective ways to structure teams, projects, and analytics functions and careers, as well as “managing up”, branding and work style.


Stars, Flakes, Vaults and the Sins of Denormalisation

Providing both performance and flexibility are often seen as contradictory goals in designing large scale data implementations. In this talk we will discuss techniques for denormalisation and provide a framework for understanding the performance and flexibility implications of various design options. We will examine a variety of logical and physical design approaches and evaluate the trade offs between them. Specific recommendations are made for guiding the translation from a normalised logical data model to an engineered-for-performance physical data model. The role of dimensional modeling and various physical design approaches are discussed in detail. Best practices in the use of surrogate keys is also discussed. The focus is on understanding the benefit (or not) of various denormalisation approaches commonly taken in analytic database designs.


Advanced Python 1

This class builds on the introductory Python class. Jupyter Notebook advanced use and customisation is covered as well as configuring multiple environments and kernels.

The Numpy package is introduced for working with arrays and matrices and a deeper coverage of Pandas data analysis and manipulation methods is provided including working with time series data.

Data exploration and advanced visualisations are taught using the Plotly and Seaborne libraries.


Advanced Python 2

This class builds on the introductory Python class. Jupyter Notebook advanced use and customisation is covered as well as configuring multiple environments and kernels.

The Numpy package is introduced for working with arrays and matrices and a deeper coverage of Pandas data analysis and manipulation methods is provided including working with time series data.

Data exploration and advanced visualisations are taught using the Plotly and Seaborne libraries.


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