Smarten

DECEMBER 11, 2017

Advanced Data Discovery allows business users to perform early prototyping and to test hypothesis without the skills of a data scientist. Advanced Data Discovery ensures data democratization and can drastically reduce the time and cost of analysis and experimentation.

Experimentation 65

Experimentation Statistics Visualization Dashboards 65

FineReport

DECEMBER 11, 2019

. – Head First Data Analysis: A learner’s guide to big numbers, statistics, and good decisions. The big news is that we no longer need to be proficient in math or statistics, or even rely on expensive modeling software to analyze customers. By Michael Milton. – Data Divination: Big Data Strategies.

Big Data 98

Big Data Statistics Experimentation Data Strategy 98

AWS Big Data

JULY 3, 2023

Look – ahead bias – This is a common challenge in backtesting, which occurs when future information is inadvertently included in historical data used to test a trading strategy, leading to overly optimistic results. Solution overview To set up and test this experiment, we complete the following high-level steps: Create an S3 bucket.

Snapshot 82

Snapshot Data Lake Testing Strategy 82

IBM Big Data Hub

APRIL 18, 2024

LLMs like ChatGPT are trained on massive amounts of text data, allowing them to recognize patterns and statistical relationships within language. Fine motor skills: It’s conceivable for AGI software to pair with robotics hardware. NLP techniques help them parse the nuances of human language, including grammar, syntax and context.

Cost-Benefit 96

Cost-Benefit Modeling Manufacturing Interactive 96

Smarten

MAY 11, 2017

Advanced Data Discovery allows business users to perform early prototyping and to test hypothesis without the skills of a data scientist, ETL or developer. Advanced Data Discovery ensures data democratization by enabling users to drastically reduce the time and cost of analysis and experimentation.

Experimentation 49

Experimentation Visualization Business Intelligence Predictive Analytics 49

The Unofficial Google Data Science Blog

APRIL 16, 2019

by MICHAEL FORTE Large-scale live experimentation is a big part of online product development. This means a small and growing product has to use experimentation differently and very carefully. This blog post is about experimentation in this regime. Such decisions involve an actual hypothesis test on specific metrics (e.g.

Experimentation 68

Experimentation Sales Metrics Measurement 68

Domino Data Lab

JUNE 23, 2019

Skomoroch proposes that managing ML projects are challenging for organizations because shipping ML projects requires an experimental culture that fundamentally changes how many companies approach building and shipping software. You’re changing things fundamentally in how you build and ship software. Transcript.

Management 110

Management Machine Learning Experimentation Metrics 110

Alation

SEPTEMBER 28, 2022

DataOps is a set of technologies, processes, and best practices that combine a process-focused perspective on data and the automation methods of the Agile software development methodology to improve speed and quality and foster a collaborative culture of rapid, continuous improvement in the data analytics field. What Is DataOps? Simplicity.

Cost-Benefit 52

Cost-Benefit Data Quality Manufacturing Testing 52

Domino Data Lab

MAY 8, 2019

Software engineering made major breakthroughs two decades ago by applying reductionist techniques to project planning and management. They also require advanced skills in statistics, experimental design, causal inference, and so on – more than most data science teams will have. Quick summary: there’s a persnickety problem.

Machine Learning 105

Machine Learning Data Science Modeling Visualization 105

Insight

APRIL 28, 2020

You’ll often see the name “data challenge” used when the take-home assignment involves machine learning or statistics or “coding challenge” when the focus is on evaluating a candidate’s software engineering skills. Length: Highly Variable. Teams new to hiring often make this mistake of creating long multi-stage screening processes.

Data-driven 53

Data-driven Data Science Experimentation Measurement 53

The Unofficial Google Data Science Blog

MARCH 22, 2018

But what if users don't immediately uptake the new experimental version? Background At Google, experimentation is an invaluable tool for making decisions and inference about new products and features. by DANIEL PERCIVAL Randomized experiments are invaluable in making product decisions, including on mobile apps.

Experimentation 62

Experimentation Measurement Modeling Statistics 62

Domino Data Lab

AUGUST 26, 2019

This control prevents overfitting and reduction in predictive accuracy on new test data. One of the difficulties of building machines, including physical computers and software programs, is that there is often more than one way to do it. We can take this model to other, novel data and perform a final hold-out test assessment.

Testing 79

Testing Modeling Machine Learning Metrics 79

Occam's Razor

MARCH 23, 2010

Part of it is fueled by a vocal minority genuinely upset that 10 years on we are still not a statistically powered bunch doing complicated analysis that is shifting paradigms. You already have the paid analytics software combos mentioned above. Usually at least a test. Part of it fueled by some Consultants. Likely not.

Analytics 118

Analytics Consulting Metrics Experimentation 118

O'Reilly on Data

MARCH 23, 2023

Maybe it’s surprising that ChatGPT can write software, maybe it isn’t; we’ve had over a year to get used to GitHub Copilot, which was based on an earlier version of GPT. What Software Are We Talking About? It’s by far the most convincing example of a conversation with a machine; it has certainly passed the Turing test.

IT 262

IT Modeling Testing Risk 262

O'Reilly on Data

DECEMBER 9, 2019

We develop an ordinary least squares (OLS) linear regression model of equity returns using Statsmodels, a Python statistical package, to illustrate these three error types. We use the diagnostic test results of our regression model to support the reasons why CIs should not be used in financial data analyses. and an error term ??

Statistics 27

Statistics Uncertainty Risk Marketing 27

IBM Big Data Hub

SEPTEMBER 25, 2023

If the exploratory work needs to move on to testing and production, they can plan appropriately. Extending the analytical capabilities and use cases of Presto To extend the analytical capabilities of Presto, Uber uses many out-of-the-box functions provided with the open source software. This way, the queries run much faster.

OLAP 93

OLAP Data Lake Data-driven Snapshot 93