KDnuggets

JANUARY 8, 2020

The standard job description for a Data Scientist has long highlighted skills in R, Python, SQL, and Machine Learning. With the field evolving, these core competencies are no longer enough to stay competitive in the job market.

Data Science 160

Data Science Machine Learning SQL Python 160

Confluent

JANUARY 8, 2020

This article show how you can offload data from on-premises transactional (OLTP) databases to cloud-based datastores, including Snowflake and Amazon S3 with Athena. I’m also going to take the opportunity […].

Cloud 27

Cloud Database Data Kafka 27

Data Engineering Podcast

JANUARY 5, 2020

Summary Databases are useful for inspecting the current state of your application, but inspecting the history of that data can get messy without a way to track changes as they happen. Debezium is an open source platform for reliable change data capture that you can use to build supplemental systems for everything from maintaining audit trails to real-time updates of your data warehouse.

Database 100

Database Kafka PostgreSQL MySQL 100

Teradata

JANUARY 8, 2020

Kevin Lewis offers his predictions for the data management and analytic trends that will accelerate in 2020. Read more!

Data Management 71

Data Management Management Data 71

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Apache Airflow® is the open-source standard to manage workflows as code. It is a versatile tool used in companies across the world from agile startups to tech giants to flagship enterprises across all industries. Due to its widespread adoption, Airflow knowledge is paramount to success in the field of data engineering.

Datasets

KDnuggets

JANUARY 9, 2020

This book is thought for beginners in Machine Learning, that are looking for a practical approach to learning by building projects and studying the different Machine Learning algorithms within a specific context.

Machine Learning 159

Machine Learning Algorithm Project Building 159

Grouparoo

JANUARY 9, 2020

At Grouparoo , we have been interviewing a lot of marketers. The overall learning is that it's a hard job. The biggest reason is that they need data to make their campaigns work and do not have the means to get that data. Basically, they need Engineers to prioritize writing code to get the data into the tool they are using. That rarely happens.

Coding 52

Coding Project Engineering Building 52

Rockset

JANUARY 10, 2020

Performing ad-hoc analysis is a daily part of life for most data scientists and analysts on operations teams. They are often held back by not having direct and immediate access to their data because the data might not be in a data warehouse or it might be stored across multiple systems in different formats. This typically means that a data engineer will need to help develop pipelines and tables that can be accessed in order for the analysts to do their work.

Data Warehouse 40

Data Warehouse Data Pipeline Insurance Data Engineering 40

KDnuggets

JANUARY 7, 2020

Follow this overview of Natural Language Generation covering its applications in theory and practice. The evolution of NLG architecture is also described from simple gap-filling to dynamic document creation along with a summary of the most popular NLG models.

Architecture 159

Architecture IT Process 159

KDnuggets

JANUARY 7, 2020

An estimated 8,650% growth of the volume of Data to 175 zetabytes from 2010 to 2025 has created an enormous need for Data Engineers to build an organization's big data platform to be fast, efficient and scalable.

Data Engineering 159

Data Engineering Data Engineer Engineering Big Data 159

Speaker: Tamara Fingerlin, Developer Advocate

In this new webinar, Tamara Fingerlin, Developer Advocate, will walk you through many Airflow best practices and advanced features that can help you make your pipelines more manageable, adaptive, and robust. She'll focus on how to write best-in-class Airflow DAGs using the latest Airflow features like dynamic task mapping and data-driven scheduling!

Data

KDnuggets

JANUARY 8, 2020

Check out this collection of 10 Python snippets that can be taken as a reference for your daily work.

Python 154

Python Programming 154

KDnuggets

JANUARY 10, 2020

A resume plays a key role in bagging that dream data science job. We break down the nuances of a job-winning data science resume so that you can go ahead and transform your own resume.

Data Science 150

Data Science Data 150

KDnuggets

JANUARY 6, 2020

Reducing images to numbers makes them amenable to computation. Let's take a look at the why and the how using Python and Numpy.

Python 145

Python Process 145

KDnuggets

JANUARY 8, 2020

Simply put: This post is about installing SQL, explaining SQL and running SQL.

SQL 138

SQL MySQL Programming 138

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Many software teams have migrated their testing and production workloads to the cloud, yet development environments often remain tied to outdated local setups, limiting efficiency and growth. This is where Coder comes in. In our 101 Coder webinar, you’ll explore how cloud-based development environments can unlock new levels of productivity. Discover how to transition from local setups to a secure, cloud-powered ecosystem with ease.

Cloud

KDnuggets

JANUARY 4, 2020

Ethics in AI has received significant attention recently, and the new KDnuggets cartoon examines the problem of teaching ethics to artificially intelligent entities.

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KDnuggets

JANUARY 9, 2020

Data Scientists work with tons of data, and many times that data includes natural language text. This guide reviews 7 common techniques with code examples to introduce you the essentials of NLP, so you can begin performing analysis and building models from textual data.

Data 122

Data Coding Building Data Preparation 122

KDnuggets

JANUARY 6, 2020

This article is an overview of H2O, a scalable and fast open-source platform for machine learning. We will apply it to perform classification tasks.

Machine Learning 116

Machine Learning IT Python 116

KDnuggets

JANUARY 9, 2020

Time series analysis will be the best tool for forecasting the trend or even future. The trend chart will provide adequate guidance for the investor. So let us understand this concept in great detail and use a machine learning technique to forecast stocks.

Machine Learning 113

Machine Learning Finance 113

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Large enterprises face unique challenges in optimizing their Business Intelligence (BI) output due to the sheer scale and complexity of their operations. Unlike smaller organizations, where basic BI features and simple dashboards might suffice, enterprises must manage vast amounts of data from diverse sources. What are the top modern BI use cases for enterprise businesses to help you get a leg up on the competition?

Business Intelligence

KDnuggets

JANUARY 10, 2020

Deepfakes have instilled panic in experts since they first emerged in 2017. Microsoft and Facebook have recently announced a contest to identify deepfakes more efficiently.

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113

KDnuggets

JANUARY 10, 2020

Finding a deep learning model to perform well is an exciting feat. But, might there be other -- less complex -- models that perform just as well for your application? A simple complexity measure based on the statistical physics concept of Cascading Periodic Spectral Ergodicity (cPSE) can help us be computationally efficient by considering the least complex during model selection.

Deep Learning 110

Deep Learning 110

KDnuggets

JANUARY 7, 2020

The healthcare AI market is expected to reach 28 billion dollars by the year 2025. With such exponential growth, AI is undoubtedly likely to bring some drastic changes in the healthcare industry. Let’s look at five ways of how AI has changed the healthcare industry.

Healthcare 80

Healthcare 80

KDnuggets

JANUARY 7, 2020

There is a need for a new way to explain complex, ensembled ML models for high-stakes applications such as credit and lending. This is why we invented GIG.

Machine Learning 77

Machine Learning 77

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With over 30 million monthly downloads, Apache Airflow is the tool of choice for programmatically authoring, scheduling, and monitoring data pipelines. Airflow enables you to define workflows as Python code, allowing for dynamic and scalable pipelines suitable to any use case from ETL/ELT to running ML/AI operations in production. This introductory tutorial provides a crash course for writing and deploying your first Airflow pipeline.

Cloud

KDnuggets

JANUARY 6, 2020

Breaking into a career in Data Science can depend on where you start. See if you fit into one of these three categories of "newbies," and then find out how to make your professional transition into the field.

Data Science 71

Data Science Data Software Engineer Software Engineering 71

KDnuggets

JANUARY 8, 2020

Introduction to Data Visualization & Storytelling;The Data Science Interview Study Guide; Why Kaggle will NOT make you a great Data Scientist; Cartoon: Teaching Ethics to AI.

Data Science 69

Data Science Data 69

KDnuggets

JANUARY 6, 2020

In this webinar, Jan 15 @ 12PM EST, we'll offer solutions to the common challenges data scientists and data engineers face when building a machine learning pipeline. Register now to attend live or to watch a recording afterwards.

Machine Learning 64

Machine Learning Building Data Engineering Data Engineer 64

KDnuggets

JANUARY 10, 2020

Also: Google's New Explainable AI Service; 10 Free Top Notch Machine Learning Courses.

Machine Learning 63

Machine Learning Data 63

Speaker: Jay Allardyce, Deepak Vittal, Terrence Sheflin, and Mahyar Ghasemali

As we look ahead to 2025, business intelligence and data analytics are set to play pivotal roles in shaping success. Organizations are already starting to face a host of transformative trends as the year comes to a close, including the integration of AI in data analytics, an increased emphasis on real-time data insights, and the growing importance of user experience in BI solutions.

Data

KDnuggets

JANUARY 9, 2020

Earn a Master of Professional Studies in Data Analytics online through Penn State World Campus – and you can add in-demand skills to your wheelhouse while you continue to work.

Data Science 60

Data Science Data Analytics Data Education 60

KDnuggets

JANUARY 7, 2020

Here are our top five hands-on training focus areas that every data scientist should know and that we’re paying extra attention to at ODSC East 2020 this April 13-17 in Boston.

Data 53

Data Data Science 53

KDnuggets

JANUARY 8, 2020

First issue of 2020 brings you a summary of how to "Ultralearn" Data Science - for those in a hurry; Explains how teams work on AutoML project; Why Python is a preferred language for Data Science; and a cartoon on teaching ethics to AI.

Data Science 51

Data Science Python Data Project 51