ArticleVideo Book This article was published as a part of the Data Science Blogathon. Introduction R is one most famous programming languages for statistical ... The post Top 10 R Packages for Data...
What is Programmatic Buying For PPC?
Advancements in the advertising industry have led to data-driven methods to promote and market your business. One relevant example is programmatic advertising. A study found programmatic ad spend will top $59.45 billion in 2019. By 2021, $81 billion of digital display ad spend will be conducted programmatically. How can programmatic buying benefit your business?
How a Data Catalog Enables Data Democratization
For many organizations, data is a business asset that’s owned by the IT department. Based on this ‘data ownership’ model, there’s limited access to data across the organization, and no transparency around what’s available internally.
Visualizing the Power Consumption of Bitcoin Mining
Bitcoin mining requires significant amounts of energy, but what does this consumption look like when compared to countries and companies?
Data Science 101: Normalization, Standardization, and Regularization
Normalization, standardization, and regularization all sound similar. However, each plays a unique role in your data preparation and model building process, so you must know when and how to use these important procedures.
Limitations Of Power Bi
It's an extraordinary apparatus to use for information investigation and finding significant bits of knowledge. Yet, let us broadly expound and find out about the favorable circumstances and detriments of Power BI so you can have some premise to contrast it and different devices.…
Five Steps to Building Stunning Visualizations Using Python
Data visualization is one powerful arsenal in data science. Visualization simply means drawing different types of graphics to represent data. At times, a data point is drawn in the form of a scatterplot or even in the form of histograms and statistical summaries. Most of the displays shown are majorly descriptive while the summaries are…
Unleashing the Business Value of Technology Part 3: Envisioning Value
In part 1 of the 4-part blog series, “Unleashing the Business Value of Technology Part 1: Framing the Challenge”, I introduced the 3 stages of “unleashing business value”: 1) Connecting to Value, 2) Envisioning Value and 3) Delivering Value
More time, tools, and details on the page experience update
Last November we announced that the page experience ranking change will go live on Google Search this year, in what we’re calling the "page experience update". To help publishers and site owners improve their page experience and prepare, today we’re announcing a few key updates.
BrandPost: Data & analytics: The next phase of digital transformation
In this two-part series, I explore the two phases of digital transformation that many organizations are undergoing. In part one, I dig into what organizations have done in the first phase of transformation and why they must think differently as they embark on the second phase. In part two, I describe how organizations should approach the second phase of transformation in order to successfully transform their data and analytics estates – with Spark as the foundation of those changes.
Build an Effective Data Analytics Team and Project Ecosystem for Success
Apply these techniques to create a data analytics program that delivers solutions that delight end-users and meet their needs.
15 data science tools to consider using in 2021
The increasing volume and complexity of enterprise data, and its central role in decision-making and strategic planning, are driving organizations to invest in the people, processes and technologies they need to make sense of and gain insights from their data assets. That includes a variety of tools commonly used in data science applications.
How to organize your data science project in 2021
Maintaining proper organization of all your data science projects will increase your productivity, minimize errors, and increase your development efficiency. This tutorial will guide you through a framework on how to keep everything in order on your local machine and in the cloud.
Unusual Opportunities for AI, Machine Learning, and Data Scientists
Here some off-the-beaten-path options to consider, when looking for a first job, a new job or extra income by leveraging your machine learning experience. Many were offers that came to my mailbox at some point in the last 10 years, mostly from people looking at my LinkedIn profile. Thus the importance of growing your network and visibility, write…
The challenges of applied machine learning
Applied machine learning, or applying artificial intelligence to practical applications, poses serious challenges. The book "Real World AI" explores these challenges in depth.
What are the different roles within cybersecurity?
People talk about the cybersecurity job market like it's a monolith, but there are a number of different roles within cybersecurity, depending not only on your skill level and experience but on what you like to do. In fact, Cybercrime Magazine came up with a list of 50 cybersecurity job titles, while CyberSN, a recruiting organization, came up with its own list of 45 cybersecurity job categories
A Kellogg marketing professor explains how to use AI and analytics to grow your business
Eric Anderson says business leaders should regularly collect and analyze data to understand what products will do well in the market.
Generate a color analysis by uploading an image
Mel Dollison and Liza Daly made a fun interactive that lets you upload…Tags: color, Emily Noyes Vanderpoel, Python, vintage
AI Weekly: Data analytics keeps attracting investment through the pandemic
The pandemic is accelerating the adoption of data analytics enabled by AI and machine learning, often in the cloud.
The Easiest Way To Deploy Machine Learning Models: PyWebIO
ArticleVideo Book This article was published as a part of the Data Science Blogathon. Creating a machine learning model is a wholesome process involving ... The post The Easiest Way To Deploy...