Analytics Vidhya

SEPTEMBER 10, 2019

Overview Sampling is a popular statistical concept – learn how it works in this article We will also talk about eight different types of. The post A Data Scientist’s Guide to 8 Types of Sampling Techniques appeared first on Analytics Vidhya.

Statistics 306

Statistics Analytics IT 306

Corinium

SEPTEMBER 10, 2019

Tell us about your experience in working with the data analytics community at Cloudera? Why do you like working in this space?

Data Analytics 150

Data Analytics Analytics 150

KDnuggets

SEPTEMBER 10, 2019

How does the scikit-learn machine learning library for Python compare to the mlr package for R? Following along with a machine learning workflow through each approach, and see if you can gain a competitive advantage by knowing both frameworks.

Machine Learning 120

Machine Learning 120

Srividya Sridharan

SEPTEMBER 10, 2019

Summer’s lease hath all too short a date. It always seems to pass by in the blink of an eye, and this year was no exception. Though I am excited for cooler temperatures and the prismatic colors of New England in the fall, I am sorry to see summer come to an end. The end […].

Data Analytics 81

Data Analytics Analytics B2B IT 81

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Many organizations today are unlocking the power of their data by using graph databases to feed downstream analytics, enahance visualizations, and more. Yet, when different graph nodes represent the same entity, graphs get messy. Watch this essential video with Senzing CEO Jeff Jonas on how adding entity resolution to a graph database condenses network graphs to improve analytics and save your analysts time.

Visualization

KDnuggets

SEPTEMBER 10, 2019

In this post, I am going to be talking about some of the most important graph algorithms you should know and how to implement them using Python.

Data Science 123

Data Science 123

DataCamp

SEPTEMBER 10, 2019

Building AI for Good with changemakers in 60+ countries.

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Insight

SEPTEMBER 10, 2019

Are you interested in working on high-impact projects and transitioning to a career in data? Sign up to learn more about the Insight Fellows programs and start your application today. Installing Spark from scratch and getting it to run in a distributed mode on a cloud computing system can be a hurdle for many new data engineers. Through this blog post, I hope to make it easier or at least provide guidance on reducing the time you must spend on the process.

Metadata 40

Metadata Data Processing Technology Data Science 40

KDnuggets

SEPTEMBER 10, 2019

Also: Top Handy SQL Features for Data Scientists; 12 NLP Researchers, Practitioners & Innovators You Should Be Following; Knowing Your Neighbours: Machine Learning on Graphs.

Marketing 66

Marketing Machine Learning 66

Octopai

SEPTEMBER 10, 2019

Enterprises today are focused on ensuring robust data governance, and are exploring different tools and approaches to support their efforts. Some organizations know exactly what they need, while others can be overwhelmed or confused by all the different solutions out there that claim to support data governance. Let’s take “data dictionary” and “business glossary” for example.

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Metadata Data Governance Cost-Benefit Reporting 40

Depict Data Studio

SEPTEMBER 10, 2019

Emily Rose Barter is one member of the Data, Reporting, and Evaluation team at Emerge Community Development in Minneapolis, Minnesota. Her teammates and co-learners include Britani Baker and their fearless leader, Danci Greene. Danci has been taking Ann Emery’s Great Graphs class since last October, and sharing her learnings with the team. This has multiplied the impact of the class, leveling up the data visualizations the team can produce for the entire agency.

Visualization 40

Visualization Dashboards Metrics Reporting 40

Speaker: Scott Sehlhorst

We know we want to create products which our customers find to be valuable. Whether we label it as customer-centric or product-led depends on how long we've been doing product management. There are three challenges we face when doing this. The obvious challenge is figuring out what our users need; the non-obvious challenges are in creating a shared understanding of those needs and in sensing if what we're doing is meeting those needs.

Management