Data Leaning is a concept in analytics that involves using data to identify and solve problems. The concept focuses on data that is incomplete, irrelevant or duplicate.
Duplicate records can be a big problem when you are using data to make important business decisions. If you don't catch them, you may end up with inaccurate reports, which will negatively impact your writing. This can cost your business time and money.
There are many different types of duplicates.
If you are building a data mining application, there is a good chance that you will have to deal with a lot of redundant information. It's also possible to receive information from a variety of sources, such as data from multiple clients or departments. There is a need to remove the chaff from the wheat to get the best bang for your buck.
This is where an advanced machine-learning tool can come in handy. By enabling your machine to monitor the performance of your algorithms in real time, you can avoid wasted effort.
In terms of data cleaning, the number one rule of thumb is to always validate your data using a dedicated software tool. One of the most important parts of data cleansing is deduplication. Fortunately, there are several tools to help you out.
One of the most useful is the 'no name' or 'no brand' software. These tools are especially helpful if you are collecting data from a range of sources. They can be invaluable if you are in the market for a new business venture, as they will ensure that your data is not compromised by a third party.
Incomplete data has many possible causes. Some of these include human error, data entry, and machine errors. A good missing data strategy can help you identify and rectify these mistakes before they cost you the customer. There are many ways to go about this, but you need to select the right techniques. Choosing a suitable imputation method is paramount to successful missing data recovery.
The imputation method entails the collection of several data sets. These are then analyzed using standard statistical techniques. The main goal of this process is to come up with a single analysis result. Depending on the complexities of the data set, multiple imputation methods may be required. This is a worthwhile exercise for many reasons. Among other things, it allows a comparison of two or more competing data sets and provides a robust way of testing the statistical significance of one or more data sets.
It also demonstrates the importance of a thorough analysis of a complete data set. An appropriate missing data strategy should be applied to all data sets to ensure that your business does not suffer from the pitfalls of poor data collection practices.
The presence of outliers in data can ruin the process of estimating statistics. In addition to introducing bias into the results, they can also reduce the statistical significance of the data. Therefore, it is important to address outliers properly.
Outliers can be caused by a variety of factors. They can come from measurement errors, participant response errors, or objective factors such as process disturbances.
An outlier is defined as a value that deviates beyond the range of the majority of other data points. These values are generally more than two standard deviations from the mean. For example, a taxi company that has a zero ride record during a pandemic lockdown is an outlier.
Several methods have been developed to address the negative impact of outliers. One of these is called Expectation Maximization. It works by replacing outliers with a more moderate value. While this may decrease the variance, it will not alter the mean of the data.
Other methods involve binning the values. This method eliminates outliers that have a strong association with each other. However, this may not be the best approach.
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
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.
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
The COVID-19 pandemic disrupted supply chains and brought economies around the world to a standstill. In turn, businesses need access to accurate, timely data more than ever before. As a result, the demand for data analytics is skyrocketing as businesses try to navigate an uncertain future. However, the sudden surge in demand comes with its own set of challenges.
Here is how the COVID-19 pandemic is affecting the data industry and how enterprises can prepare for the data challenges to come in 2021 and beyond.
#big data #data #data analysis #data security #data integration #etl #data warehouse #data breach #elt
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-
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
Data integration solutions typically advocate that one approach – either ETL or ELT – is better than the other. In reality, both ETL (extract, transform, load) and ELT (extract, load, transform) serve indispensable roles in the data integration space:
Because ETL and ELT present different strengths and weaknesses, many organizations are using a hybrid “ETLT” approach to get the best of both worlds. In this guide, we’ll help you understand the “why, what, and how” of ETLT, so you can determine if it’s right for your use-case.
#data science #data #data security #data integration #etl #data warehouse #data breach #elt #bid data