Trystan  Doyle

Trystan Doyle

1592498546

Learning Pandas.Series(Part-7)( Handling NaN/Missing Data in Series)

Handling NaN in Series is Mandatory to learn to start with handling the Missing Data in field of Data Analytics … Let’s explore the same…

#python #pandas #programming #pandas-series #pandas.series #nan

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Buddha Community

Learning Pandas.Series(Part-7)( Handling NaN/Missing Data in Series)
Trystan  Doyle

Trystan Doyle

1592498546

Learning Pandas.Series(Part-7)( Handling NaN/Missing Data in Series)

Handling NaN in Series is Mandatory to learn to start with handling the Missing Data in field of Data Analytics … Let’s explore the same…

#python #pandas #programming #pandas-series #pandas.series #nan

 iOS App Dev

iOS App Dev

1620466520

Your Data Architecture: Simple Best Practices for Your Data Strategy

If you accumulate data on which you base your decision-making as an organization, you should probably think about your data architecture and possible best practices.

If you accumulate data on which you base your decision-making as an organization, you most probably need to think about your data architecture and consider possible best practices. Gaining a competitive edge, remaining customer-centric to the greatest extent possible, and streamlining processes to get on-the-button outcomes can all be traced back to an organization’s capacity to build a future-ready data architecture.

In what follows, we offer a short overview of the overarching capabilities of data architecture. These include user-centricity, elasticity, robustness, and the capacity to ensure the seamless flow of data at all times. Added to these are automation enablement, plus security and data governance considerations. These points from our checklist for what we perceive to be an anticipatory analytics ecosystem.

#big data #data science #big data analytics #data analysis #data architecture #data transformation #data platform #data strategy #cloud data platform #data acquisition

Gerhard  Brink

Gerhard Brink

1620629020

Getting Started With Data Lakes

Frameworks for Efficient Enterprise Analytics

The opportunities big data offers also come with very real challenges that many organizations are facing today. Often, it’s finding the most cost-effective, scalable way to store and process boundless volumes of data in multiple formats that come from a growing number of sources. Then organizations need the analytical capabilities and flexibility to turn this data into insights that can meet their specific business objectives.

This Refcard dives into how a data lake helps tackle these challenges at both ends — from its enhanced architecture that’s designed for efficient data ingestion, storage, and management to its advanced analytics functionality and performance flexibility. You’ll also explore key benefits and common use cases.

Introduction

As technology continues to evolve with new data sources, such as IoT sensors and social media churning out large volumes of data, there has never been a better time to discuss the possibilities and challenges of managing such data for varying analytical insights. In this Refcard, we dig deep into how data lakes solve the problem of storing and processing enormous amounts of data. While doing so, we also explore the benefits of data lakes, their use cases, and how they differ from data warehouses (DWHs).


This is a preview of the Getting Started With Data Lakes Refcard. To read the entire Refcard, please download the PDF from the link above.

#big data #data analytics #data analysis #business analytics #data warehouse #data storage #data lake #data lake architecture #data lake governance #data lake management

Macey  Kling

Macey Kling

1597579680

Applications Of Data Science On 3D Imagery Data

CVDC 2020, the Computer Vision conference of the year, is scheduled for 13th and 14th of August to bring together the leading experts on Computer Vision from around the world. Organised by the Association of Data Scientists (ADaSCi), the premier global professional body of data science and machine learning professionals, it is a first-of-its-kind virtual conference on Computer Vision.

The second day of the conference started with quite an informative talk on the current pandemic situation. Speaking of talks, the second session “Application of Data Science Algorithms on 3D Imagery Data” was presented by Ramana M, who is the Principal Data Scientist in Analytics at Cyient Ltd.

Ramana talked about one of the most important assets of organisations, data and how the digital world is moving from using 2D data to 3D data for highly accurate information along with realistic user experiences.

The agenda of the talk included an introduction to 3D data, its applications and case studies, 3D data alignment, 3D data for object detection and two general case studies, which are-

  • Industrial metrology for quality assurance.
  • 3d object detection and its volumetric analysis.

This talk discussed the recent advances in 3D data processing, feature extraction methods, object type detection, object segmentation, and object measurements in different body cross-sections. It also covered the 3D imagery concepts, the various algorithms for faster data processing on the GPU environment, and the application of deep learning techniques for object detection and segmentation.

#developers corner #3d data #3d data alignment #applications of data science on 3d imagery data #computer vision #cvdc 2020 #deep learning techniques for 3d data #mesh data #point cloud data #uav data

Cyrus  Kreiger

Cyrus Kreiger

1617731760

An Introduction to Data Connectors: Your First Step to Data Analytics

Modern analytics teams are hungry for data. They are generating incredible insights that make their organizations smarter and are emphasizing the need for data-driven decision making across the board. However, data comes in many shapes and forms and is often siloed away. What actually makes the work of analytics teams possible is the aggregation of data from a variety of sources into a single location where it is easy to query and transform. And, of course, this data needs to be accurate and up-to-date at all times.

Let’s take an example. Maybe you’re trying to understand how COVID-19 is impacting your churn rates, so you can plan your sales and marketing spends appropriately in 2021. For this, you need to extract and combine data from a few different sources:

  • MySQL database that details all the interactions your users are having with your product
  • Salesforce account that contains the latest information about your current and prospective customers
  • Zendesk account that has all support tickets raised by your customers

#data-analytics #data-science #data-engineering #data #data-warehouse #snowflake #data-connector #machine-learning