The potential to leverage advancements in data analytics has exploded in recent years. However, the discord between this potential and the ability of individuals to leverage it has never been greater. Discover Data is focused on bridging this gap, and on making the world of data analytics more accessible to all. We believe that everyone can benefit from a broad understanding of the world of data analytics and its practical applications.
Picking up data science can be tricky at first – navigating through systems, tools, libraries and packages can pose challenges and roadblocks before even getting started. We provide training in small, highly personalised groups, and focus on the topics that matter most from a commercial perspective. This focus is a product of years of industry experience working with Google. We cut through the noise and excess complexity, focusing on what matters most to advance your learning path & business goals.
Once you have completed your training, we offer office hours on a weekly basis for up to 4 weeks afterwards. We understand that individuals are at different stages in their learning paths, and as such, training can be tailored accordingly.
Big Data is a catch-all phrase that encompasses all different aspects of data science. As a beginner, its important to understand how the different pieces fit together, from data pipelines to data mining, to data visualization, to predictive analytics. It is also important to understand the context in which big data has evolved. In this module, we look at the entire big data eco system, its evolution, and practical applications in today’s world.
Read more about our specially tailored program in the link above.
In this module we dive into the theory behind predictive machine learning models, exploring some of the most frequently used concepts with real world business applications, such as regression, classification, decision trees, random forest, and training/test split. We also analyze the different stages of predictive model creation – from defining business objectives, gathering & preparing data (categorical values, missing values & outliers), feature selection & scaling, model creation and validation.
A fundamental grasp of the theory behind these methods is essential for any practical application in a business setting.
Increasingly, companies such as Microsoft are seeking to incorporate & simplify machine learning in their cloud architecture. This has several key benefits – 1) a simplified drag & drop functionality (no coding required), 2) the scale of the cloud and 3) the ability to deploy machine learning models into web apps. In this module, you will learn how to upload, analyze, visualize, clean data, and build machine learning models using Microsoft Azure’s sleek and intuitive interface.
Read more in the link above.
R is the mother of all data science statistical packages, and offers countless opportunities to understand and interpret data. However, this tool can sometimes seem impenetrable upon first glance. Loading data, setting up your environment, downloading the relevant packages (there are 8000 of these…) can put people off before analysis has even begun. We provide training in a highly structured & tailored fashion to take the pain out of this, eliminating unnecessary complexity where it exists. We focus on real world, practical applications of the tool, in order to fully expose its capabilities.
A/B Testing
SQL Bootcamp
SQL is the most in-demand skill among tech firms, and is the building blocks behind analysis. Without reliable data, the most powerful algorithm in the world is useless. In this bootcamp, we look at basic & advance SQL, and also look at the many applications/ platforms that run off SQL. You may have heard of platforms such as Postgre, Redshift, BigQuery, Hive and MySQL – all of these use SQL, albeit with minor nuances. By learning the core features of SQL, you can in time learn to master these.