O'Reilly on Data

OCTOBER 19, 2021

In this article, we want to dig deeper into the fundamentals of machine learning as an engineering discipline and outline answers to key questions: Why does ML need special treatment in the first place? If you peek under the hood of an ML-powered application, these days you will often find a repository of Python code.

IT 346

IT Testing Experimentation Software 346

AWS Big Data

JULY 28, 2023

Examples cover code snippets in Python and SQL for both frameworks across three major themes: data preparation, data processing, and data enrichment. The DataStream API supports Java, Scala, and Python and offers primitives for many common stream processing operations, as well as a balance between code verbosity or expressiveness and control.

Data Processing 79

Data Processing Big Data Data Quality Technology 79

DataKitchen

MAY 10, 2021

Before we talk about winning the war against data and analytics errors, let’s review some fundamental DataOps principles: Avoid manual tests. Some people prefer R over Python. Figure 6: The DataKitchen Platform provides process observability reports like the Tornado Report which tracks errors and resolution time per data source.

Testing 214

Testing Manufacturing Data Quality Statistics 214

datapine

APRIL 28, 2023

Some popular programming languages that are considered to be “strongly typed” include SQL, C, Java, Pascal, and Python, among others. This is because in most, not to say all, programming languages, they are fundamental to ensure the data is functioning correctly with no major errors.

Reporting 200

Reporting Data-driven Visualization Interactive 200

Smart Data Collective

SEPTEMBER 9, 2019

The majority of machine learning and deep learning solutions have focused on fundamental analysis of securities. The skepticism of many fundamental analysts is not proof that technical analysis is invalid. However, it is a very different skill from fundamental investment analysis.

Deep Learning 66

Deep Learning Forecasting Machine Learning Strategy 66

FineReport

JANUARY 5, 2022

They have mastered the fundamentals of the report and system and are unsure of how much further progress they can make. Therefore, using R, Python, and other data analysis languages, referred to as the chart function package, present visual data and analysis. Understand data and operation. In addition, E-charts, HighCharts, D3.js,

Reporting 52

Reporting Analytics Visualization Dashboards 52

The Unofficial Google Data Science Blog

AUGUST 31, 2016

While MapReduce remains a fundamental tool, many interesting analyses require more than it can offer. Thus it is easy to use Spark for interactive analysis (from a python or scala shell) and to prototype ML algorithms but harder to do so in Dataflow. Thus the ability to manipulate big data is essential to our notion of data science.

Data Science 66

Data Science Sales Optimization Cost-Benefit 66

Domino Data Lab

FEBRUARY 4, 2019

We’ve been putting together the keynotes and tracks for Rev 2 and I’m super excited about what’s in store. We’ll have a Leadership track, a Practitioner track, plus AMAs (“ask me anything” with notable experts) and industry poster sessions. Strata SF , Mar 25–29 in SF – including a new Jupyter track.

Data Science 66

Data Science Experimentation Data-driven Machine Learning 66

Domino Data Lab

MAY 8, 2019

Keep in mind that data science is fundamentally interdisciplinary. There’s a joke going around the interwebs, attributed to Mat Velloso: If it is written in Python, it’s probably machine learning. OSCON , Jul 15-18 in Portland: CFP is open for the “ AI Ops: Development meets Data Science ” track which I’ll be hosting on Jul 16.

Machine Learning 105

Machine Learning Data Science Modeling Visualization 105