What is churn analytics? Churn analytics is the process of measuring and understanding the rate at which customers quit the product, site…
Data transformation tools help standardize data formatting, apply business logic, and otherwise play the transform role in ETL (extract, transform, load) or ELT (extract, load, transform). These tools are used to provide a more consistent, uniform execution of data transformations, regardless of data source.
Data profiling is the process of examining data from various sources and collecting statistics or summaries about the data. This process can help you check if you have the right kind of data for your problem, as well as ensure data quality.
In this post, I will highlight the core paradoxes that Gartner introduced to help the data and analytics community unleash innovation and transform uncertainty. By unifying seemingly disparate concepts, Gartner’s summit created opportunities for new perspectives on age-old problems.
We cover everything you need to know to get started with data preparation, so if you're already a data scientist or you are researching particular sub-areas in this field, this post is for you.
In a notebook, you can do a lot–from preprocessing data to EDA to tuning machine learning models–which is great! But, in notebooks, there’s a lot of upfront work that you, as data scientists, must do every time before, and as you start analyzing data and building models.
We conducted a survey about the top challenges facing data scientists and data professionals across industries. Remarkably few of the responses were about model accuracy, but much of it was around collaboration, process, and communication.
Data science notebooks are powerful, flexible tools that data scientists use every day. But they are code-heavy linear workflows which do not properly address data scientists' need for multi-stakeholder collaboration, reproducibility, fast iterative discovery, and operational work to deploy. We explore a few ways notebooks fail data scientists here.
Historically, Machine Learning algorithms were a bit painful to use, and required tedious human intervention in order to tune hyperparameters. Recent innovations in AutoML means that data scientists can now get better models in less time, by using new tools that support automatic exploration of how to assemble the best ML pipeline.
Low-code tools are revolutionizing businesses, enabling citizen developers to create new business applications that drive innovation. Now, the same thing is starting to happen for citizen data scientists.
As organizations made data analytics a strategic priority, demand for data analysis outputs exceeded supply of trained data scientists. To bridge the gap, no code workflow platforms (KNIME, Alteryx…) were developed to make advanced data science easier, and give access to wide audiences.
Move fast and break things — but still be data informed. Startups must tailor their data analytics practices to focus on on delivering strategic insights quickly. These are a few observations we’ve observed in our partnerships with startups, as Einblick helps lean organizations produce better analytics.
While code can accomplish everything, there is a set of repetitive operations where visual-based no code operators will help every data scientist. In that way, no code operators are just the next logical extension of importing libraries.
Why have advancements in Machine Learning (ML) imperfectly translated to better data driven decision making? How can business line stakeholders and data scientists bridge the gap between quality analysis and executed changes?
Here are some of the collaboration challenges in data science today, and a case study of how one of our clients implemented live co-working sessions to solve them.
As organizations empower democratized analytics, they must recognize how advanced tools like AutoML needs to be augmented with human intuition. Reducing the need to code has not invalidated the need for human-led explorations of data.
In data science, there are many different versions of correctness. Accuracy itself can be highly misleading: We don't want accurate nuclear launch detection and we don't want accurate self driving cars.
But it’s 2022 and it’s time to say goodbye to spreadsheets as the primary tool for data analysis. You should be able to work in a fast, collaborative space for business analysis, and harness innovations in AI/ML to quickly identify key drivers and even access predictive modeling.