6 Powerful Feature Engineering Techniques For Time Series Data (using Python)

Analytics Vidhya

Overview Feature engineering is a skill every data scientist should know how to perform, especially in the case of time series We’ll discuss 6. The post 6 Powerful Feature Engineering Techniques For Time Series Data (using Python) appeared first on Analytics Vidhya.

Build Better and Accurate Clusters with Gaussian Mixture Models

Analytics Vidhya

Algorithm Clustering Intermediate Machine Learning Python Statistics Structured Data Technique Unsupervised clustering EM expectation maximization Gaussian Distribution gaussian mixture models GMM kmeans Probability density function python

Very Meta … Unlocking Data’s Potential with Metadata Management Solutions


Untapped data, if mined, represents tremendous potential for your organization. While there has been a lot of talk about big data over the years, the real hero in unlocking the value of enterprise data is metadata , or the data about the data.

Creating a Big Data Platform Roadmap

Perficient Data & Analytics

One of the most frequently asked questions by our customers is the roadmap to deploying a Big Data Platform and becoming a truly data-driven enterprise. Just as you can’t build a house without a foundation, you can’t start down a big data path without first establishing groundwork for success. There are several key steps to prepare the organization to realize the benefits of a big data solution with both structured and unstructured data.

How to Gain Valuable Insights from Untapped Data Using AI

Perficient Data & Analytics

You probably know your organization needs to invest in artificial intelligence (AI) solutions to take advantage of the deluge of data that mobile and digital users are creating, but do you know why or how? LEGACY ANALYTICS METHODS AREN’T EQUIPPED TO PROCESS ALL DATA TYPES. The majority of data is unstructured (around 80%) which means it isn’t clearly defined or easily searchable the way that structured data is. LEVERAGE YOUR DATA WITH AI.

Webinar: Natural Language Processing for Digital Transformation of Unstructured Text


Learn how pharma and healthcare organizations are using the power of Natural Language Processing (NLP) to transform unstructured text into actionable structured data.

A Comprehensive Guide to Natural Language Generation


In its essence, it automatically generates narratives that describe, summarize or explain input structured data in a human-like manner at the speed of thousands of pages per second.

Building tools for enterprise data science

O'Reilly on Data

The O’Reilly Data Show Podcast: Vitaly Gordon on the rise of automation tools in data science. In this episode of the Data Show , I spoke with Vitaly Gordon , VP of data science and engineering at Salesforce. Continue reading Building tools for enterprise data science

How Artificial Intelligence Will Disrupt the Financial Sector


Artificial intelligence thrives with data. The more data you have, the better your algorithms will be. However, just having a lot of data is not sufficient anymore. More data beats clever algorithm, but better data beats more data." - Peter Norvig - Director of Research, Google.

NLP vs. NLU: from Understanding a Language to Its Processing


They both attempt to make sense of unstructured data, like language, as opposed to structured data like statistics, actions, etc. However, NLP and NLU are opposites of a lot of other data mining techniques.

Text Analytics – Understanding the Voice of Consumers


Text analytics helps to draw the insights from the unstructured data. . – into structured data to develop actionable managerial insights to enhance their operations. . .

Mastering Data Variety


Data variety — the middle child of the three Vs of Big Data — is in big trouble. . It’s in the critical path of enterprise data becoming an asset. Meanwhile, most enterprises have unconsciously built up extreme data variety over the last 50+ years. Data Types.

Don’t get left behind the modern data warehouse train!


Why are most organizations replatforming and moving to a modern data warehouse? Instead, they are guided by data serving up answers to questions, perhaps asked by experts who are in those boardrooms. This requires direct and fast access to data and lots of it.

Big Data Ingestion: Parameters, Challenges, and Best Practices


Businesses are going through a major change where business operations are becoming predominantly data-intensive. quintillions of bytes of data are being created each day. This pace suggests that 90% of the data in the world is generated over the past two years alone. Big Data.

Research quality data and research quality databases

Simply Statistics

When you are doing data science, you are doing research. You want to use data to answer a question, identify a new pattern, improve a current product, or come up with a new product. That is why the key word in data science is not data, it is science.

Key Differences between a Traditional Data Warehouse and Big Data

Perficient Data & Analytics

Traditional data warehouse solutions were originally developed out of necessity. The data captured from these traditional data sources is stored in relational databases comprised of tables with rows and columns and is known as structured data. So how do you make the data gathered more useful? This process begins with data consolidation tools like Informatica or Oracle Data Integrator. What is Big Data? Multi-Structured Data.

Investment Company Reporting Modernization Goals & Expectations

Perficient Data & Analytics

Ease of access, aggregation, and analysis of the reported data by the Commission and the public. New forms you must submit: Form N-PORT: Requires investment companies to report portfolio information monthly in a structured data format. Form N-CEN: Requires investment companies to report census-type information annually in a structured data format.

Snowflake: A New Blueprint for the Modern Data Warehouse

Sirius Computer Solutions

Companies today are struggling under the weight of their legacy data warehouse. These old and inefficient systems were designed for a different era, when data was a side project and access to analytics was limited to the executive team. Data migration and integration.

What are Different Kinds of Data Visualization?


What is Data Visualization? Data visualization provides clear, fast and effective communication according to graphical means. A good data visualization generally has the following characteristics: Accuracy. 5 Data Visualization Methods Commonly Used.

Top 10 Analytics Trends for 2019

Timo Elliott

We’ve reached the third great wave of analytics, after semantic-layer business intelligence platforms in the 90s and data discovery in the 2000s. These data-driven, self-learning business processes improve automatically over time and as people use them.

What is Big Data Analytics?

Mixpanel on Data

Companies use big data analytics to uncover new and exciting insights in large and varied datasets. It helps them forecast market trends, identify hidden correlations between data flows, and understand their customers’ preferences in fine detail. Benefits of big data analytics.

Introduction To The Basic Business Intelligence Concepts


“Without big data, you are blind and deaf and in the middle of a freeway.” – Geoffrey Moore, management consultant, and author. Your fast thinking system can take in massive amounts of data at once. They prevent you from drowning in data. The data warehouse.

Data Lakes on Cloud & it’s Usage in Healthcare


Data lakes are centralized repositories that can store all structured and unstructured data at any desired scale. The power of the data lake lies in the fact that it often is a cost-effective way to store data. Deploying Data Lakes in the cloud.

Glossary of Digital Terminology for Career Relevance

Rocket-Powered Data Science

AGI (Artificial General Intelligence): AI (Artificial Intelligence): Application of Machine Learning algorithms to robotics and machines (including bots), focused on taking actions based on sensory inputs (data). Analytics: The products of Machine Learning and Data Science (such as predictive analytics, health analytics, cyber analytics). pattern detection and pattern recognition in data). NLG is a software process that transforms structured data into human-language content.

Okay, You Got a Knowledge Graph Built with Semantic Technology… And Now What?


With several examples, you will see how knowledge management can be made smarter using the potential of semantic technology to fuse data, analyze relationships, detect patterns and infer new facts from enriched datasets. .

Interview with Dominic Sartorio, Senior Vice President for Products & Development, Protegrity


Ahead of the Chief Data Analytics Officers & Influencers, Insurance event we caught up with Dominic Sartorio, Senior Vice President for Products & Development, Protegrity to discuss how the industry is evolving. Why should Chief Data & Analytics Officers care about data security?

If Johnny Mnemonic Smuggled Linked Data


In this article, we are bringing science fiction to the semantic technology (and data management) talk to shed some light on three common data challenges: the storage, retrieval and security of information. We will talk through these from the perspective of Linked Data (and cyberpunk).

Deep automation in machine learning

O'Reilly on Data

We need to do more than automate model building with autoML; we need to automate tasks at every stage of the data pipeline. We won’t be writing code to optimize scheduling in a manufacturing plant; we’ll be training ML algorithms to find optimum performance based on historical data.

Data, Databases and Deeds: A SPARQL Query to the Rescue


Data, Databases and Deeds: A SPARQL Query to the Rescue. quintillion bytes of data created each day, the bar for enterprise knowledge and information systems, and especially for their search functions and capabilities, is raised high. Normalizing data values (if needed).

Governance in Healthcare: Big Data is Table Stakes

Perficient Data & Analytics

Big data itself does not alter the approach to governance nor its framework. And big data isn’t just about data – it’s also concerned with managing and governing vast amounts of content of varying types such as video, images, voice, etc.

Approaches to Embrace Big Data

Perficient Data & Analytics

Not every organization starts its big data journey from the same place. However, in order to drive efficiencies, support expected future growth and to continue its evolution to a data-driven company, most organizations are reviewing their current suite of software solutions, platforms and documenting processes and areas of improvement along with devising and executing a strategy to deploy modern business intelligence capabilities. Baby Steps from EDW to Big Data.

What is Data Variety?


Enterprise-level data is constantly growing and developing—and organizations are starting to recognize the value in collecting it. But when it comes to actually leveraging that data as an asset, enterprises are faced with several unique challenges. Why is Data Variety Different?

Create a Value Blizzard with Snowflake and Microsoft Azure

Sirius Computer Solutions

There are many benefits of using a cloud-based data warehouse, and the market for cloud-based data warehouses is growing as organizations realize the value of making the switch from an on-premises data warehouse.

Okay, You Got a Knowledge Graph Built with Semantic Technology… And Now What?


Whether you refer to the use of semantic technology as Linked Data technology or smart data management technology, these concepts boil down to connectivity. Connectivity in the sense of connecting data from different sources and assigning these data additional machine-readable meaning.

Here’s How To Implement Manufacturing Analytics Today

Smart Data Collective

Big data is everywhere , and it’s finding its way into a multitude of industries and applications. One of the most fascinating big data industries is manufacturing. In an environment of fast-paced production and competitive markets, big data helps companies rise to the top and stay efficient and relevant. Manufacturing innovation has long been an integral piece of our economic success, and it seems that big data allows for great industry gains.

Different Types of Databases for Modern Data Challenges


We live in an era of Big Data. The sheer volume of data currently existing is huge enough without also grappling with the amount of new information that’s generated every day. It’s no wonder, then, that NoSQL databases are seeing a lot of use in Big Data and real-time web apps.

Evolution of AI in Sales (Part 3)


Traditional algorithmic solutions around structured data have gained and continue to gain traction. A strong case study where a global MNC used AI enabled deal classification, data input enhancement, account analytics, and external data feeds to transform their sales organization in Nigeria resulting in “a substantial increase in sales productivity and forecasted Y-o-Y revenue growth.” Voice data quality). Examples of AI Solutions Usage.

Sales 40

Five Signs That You Might Have a Know-Your-Customer Problem


If your business is experiencing any of these symptoms, then you may be suffering from dirty, duplicate data. And these aren’t the only symptoms, making dirty, duplicate data very much a universal Silent Killer. The eventual result: lots of duplicate, “dirty” data.

Evolution of AI in Sales (Part 2)


Victor Antonio, in his book, “Sales Ex Machina: How Artificial Intelligence is Changing the World of Selling”, writes that today’s AI algorithms can look at structured data points like size of the deal, product specifications, compliance, number of competitors, company size, territory/region, client’s industry, client’s annual revenues, public or private company, level of decision-makers and influencers involved, timing etc.

Sales 40

Data, Databases and Deeds: A SPARQL Query to the Rescue


quintillion bytes of data created each day, the bar for enterprise knowledge and information systems, and especially for their search functions and capabilities, is raised high. Normalizing data values (if needed). In a world of 2.5