An Oracle-based approach to the “taxi fare” prediction problem – episode 2

This is the second part of the series on the Taxi Fare prediction problem from an Oracle perspective. You can read Episode 1 here. In this Episode, I will put our model to work. That is, I will show several…

Read More >

Analyzing BigQuery via Excel and Google Sheets

Both MS Excel and Google Sheets offer ways to connect directly to BQ data, to run queries, to pull data back to Excel/Sheets and allow further analysis via options such as pivot tables, charts and drilling up/down. MS Excel The…

Read More >

Azure Data Lake basics for the SQL Server DBA / developer and… for everyone!

The basics If you’re a Microsoft SQL Server DBA or developer and have not been introduced to the Microsoft Azure Data Lake and would like to understand what it’s all about and how to get started, this article is for YOU….

Read More >

Why column stores?

Introduction I’ve been working with data in many forms for my entire career. During this time, I have occasionally needed to build or query existing databases to get statistical data. Traditional databases are usually designed to query specific data from…

Read More >

Five ways to build a data-driven culture within your organization

In this post guest blogger Jim Donnelly introduces some of the concepts in our latest eBook, Analytics for Everyone, discussing what it takes to drive a data-first culture across your organization. Imagine that it’s been about a year since you…

Read More >

A comparative analysis of Amazon SageMaker and Google Datalab

Amazon SageMaker and Google Datalab have fully managed cloud Jupyter notebooks for designing and developing machine learning and deep learning models by leveraging serverless cloud engines. However, as much as they have in common, there are key differences between the…

Read More >

Exploring Amazon SageMaker

Amazon SageMaker is another cloud-based fully managed data analytics/ machine learning modeling platform for designing, building and deploying data models. The key selling point of Amazon SageMaker is “zero-setup”. The concept of “zero-setup” means data science teams can entirely focus…

Read More >

Dipping your toes into building an Analytics Platform on Google Cloud Platform

“We have many disparate data sources and we’re having a hard time getting a global view of all our data across our organization.” “Our data is currently all in <enter data warehouse name here> and we want to migrate it…

Read More >

Datascape podcast episode 15 – machine learning primer for enterprise IT with Paul Spiegelhalter

Joining us today we have my esteemed colleague Paul Spiegelhalter. Paul is a data scientist and machine learning specialist with expertise in predictive analytics and algorithmic modeling across a number of industries, including computer vision, online advertising and user analysis,…

Read More >

Supervised machine learning: a conversational guide for executives and practitioners

This post gives a systematic overview of the vital points to consider when building supervised learning models. We address in Q&A style some of the key decisions/issues to go over when building a machine learning/ deep learning model. Whether you…

Read More >
Page 1 of 212