Category Archives: Data Science
The 9 Best Data Wrangling Courses and Online Training for 2021 – Solutions Review
The editors at Solutions Review have compiled this list of the best data wrangling courses and online training to consider.
Data wrangling is the process of cleaning, structuring and enriching raw data into the desired format. The practice has become increasingly important as data volumes and varieties continue to grow larger. Data wrangling typically involves six iterative steps, including data discovery, structuring, data cleaning, data enrichment, data validation, and publishing. The end-result of this time-consuming process is curated data sets that are easy to access, analyze and generate insights from.
With this in mind, weve compiled this list of the best data wrangling courses and online training to consider if youre looking to grow your data management or analytics skills for work or play. This is not an exhaustive list, but one that features the best data wrangling courses and online training from trusted online platforms. We made sure to mention and link to related courses on each platform that may be worth exploring as well. Click Go to training to learn more and register.
Platform: Coursera
Description: This course allows you to apply the SQL skills taught in SQL for Data Science to four increasingly complex and authentic data science inquiry case studies. Students will learn how to convert timestamps of all types to common formats and perform date/time calculations. Youll also select and perform the optimal JOIN for a data science inquiry and clean data within an analysis dataset by deduping, running quality checks, backfilling, and handling nulls.
Related path/track: Process Data from Dirty to Clean
Platform: DataCamp
Description: The real world is messy and your job is to make sense of it. Toy datasets like MTCars and Iris are the result of careful curation and cleaning, even so, the data needs to be transformed for it to be useful for powerful machine learning algorithms to extract meaning, forecast, classify, or cluster. This course will cover the gritty details that data scientists are spending 70-80% of their time on; data wrangling and feature engineering.
Related path/track: Interactive Data Visualization with rbokeh
Platform: Edureka
Description: Edurekas Machine Learning Certification Training using Python will help you gain expertise in various machine learning algorithms such as regression, clustering, decision trees, random forest, Nave Bayes, and Q-Learning. This module will also help you understand the concepts of statistics, time-series, and different classes of machine learning algorithms like supervised, unsupervised, and reinforcement algorithms.
Related path/track: Data Science Certification Course using R
Platform: edX
Description: This introductory Excel course will equip you with a strong foundational knowledge of Excel to organize, analyze and work with data. You will develop essential Excel skills, such as simple data wrangling and managing spreadsheets, along with a foundational understanding of business data analysis.
Related paths/tracks: Excel for Everyone: Data Analysis Fundamentals, Excel for Everyone: Data Management, Data Science: R Basics, Data Analytics Basics for Everyone, Learning Analytics Fundamentals, Data Science: Wrangling
Platform: Experfy
Description: This course will teach you from start to finish how to get your data into R efficiently and polish it up so that it is as good as it can be. This will let you or your team focus after this step on the statistical modeling, visualization, reporting, sharing, or any other post-processing task you wish to perform. Confidence, reliability, and reproducibility in your data acquisition and preparation are the kingpins to being able to maximize your datas value.
Related paths/tracks: Data Pre-Processing, Data Curation for Decision Making
Platform: LinkedIn Learning
Description: In this course, learn about the principles of tidy data, and discover how to create and manipulate data tibblestransforming them from source data into tidy formats. Instructor Mike Chapple uses the R programming language and the tidyverse packages to teach the concept of data wranglingthe data cleaning and data transformation tasks that consume a substantial portion of analysts time.
Related path/track:R Essential Training: Wrangling and Visualizing Data
Platform: Pluralsight
Description: This course, Data Wrangling with Python, is aimed at helping you do exactly that. First, youll see how to merge data from different sources using the methods concat, append, and merge. Next, youll discover how to combine data into groups. The primary function used here is groupby. In the next two sections, youll explore how to transform and normalize data. Youll learn why these processes are necessary, and then proceed to see how they work in practice.
Related paths/tracks: SQL Data Wrangling in Oracle: Table Data, Data Wrangling with Pandas for Machine Learning Engineers
Platform: Udacity
Description: Advance your programming skills and refine your ability to work with messy, complex datasets. Youll learn to manipulate and prepare data for analysis, and create visualizations for data exploration. Finally, youll learn to use your data skills to tell a story with data.
Related paths/tracks: Predictive Analytics for Business, Data Wrangling with MongoDB, Learn Spark
Platform: Udemy
Description: This course enables learners to acquire the knowledge and statistical data analysis wrangling and visualization skills that are most important. The module will take you (even if you have no prior statistical modeling/analysis background) from a basic level to performing some of the most common data wrangling tasks in Python. It will also equip you to use some of the most important Python data wrangling and visualization packages such as seaborn.
Related paths/tracks: Data Wrangling in Pandas for Machine Learning Engineers, Complete Data Wrangling & Data Visualisation in R
Tim is Solutions Review's Editorial Director and leads coverage on big data, business intelligence, and data analytics. A 2017 and 2018 Most Influential Business Journalist and 2021 "Who's Who" in data management and data integration, Tim is a recognized influencer and thought leader in enterprise business software. Reach him via tking at solutionsreview dot com.
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The 9 Best Data Wrangling Courses and Online Training for 2021 - Solutions Review
Data Science Platform Market 2021 Industry Challenges Business Overview And Forecast Research Study 2025 | IBM Corporation,BRIDGEi2i Analytics…
Global Data Science Platform Market Research Report Covers, Future Trends, Size, Share, Past, Present Data and Deep Analysis, And Forecast, 2021-2027 market published by Adroit Market Research provides a close watch on current market trends, opportunities, and revenue growth. The report on the Data Science Platform market through its overview section facilitates qualitative analysis of estimation and dynamics of prevailing market opportunities during the forecast period. The report is curated into understandable formats. Experts have included pie charts, data tables, illustrations, and other stats charts to enable users to understand the market patterns easily.
The Data Science Platform market research report provides a complete analysis of the fundamental information about the market overview, market size, and market growth prospects that are impacting the growth of the market. Moreover, this report offers broad information about the technological expenditure over the forecast period which offers a unique perspective on the global Data Science Platform market across several segments covered in the report.
The Worldwide Global Data Science Platform Market is deliberately investigated in the report while to a great extent focusing on top players and their business strategies, geological extension, market sections, serious scene, assembling, and estimating and cost structures. Each segment of the exploration study is exceptionally set up to investigate key parts of the Data Science Platform market. For example, the market elements area dives profound into the drivers, limitations, patterns, and chances of the Data Science Platform market.
Top Key Players Profiled in this report are: Major Companies CoveredIBM CorporationBRIDGEi2i Analytics Solutions Pvt. LtdSAS InstituteInc.KNIME AGDataikuAnacondaIncMicrosoft CorporationWNS Global Services Pvt. Ltd.TIBCO Software India Pvt. LtdClouderaInc.H2O.aiWolfram ResearchRapidMinerInc.AlteryxInc.Teradata CorporationDomino Data LabInc.GoogleInc.
Besides presenting notable insights on Data Science Platform market factors comprising above determinants, the report further in its subsequent sections of this detailed research report on Data Science Platform market states information on regional segmentation, as well as thoughtful perspectives on specific understanding comprising region specific developments as well as leading market players objectives to trigger maximum revenue generation and profits.
In addition to all of these detailed Data Science Platform market specific developments, the report sheds light on dynamic segmentation as well as optimum understanding on primary and secondary research proceeding further with in-depth SWOT and PESTEL analysis to guide optimum profits in Data Science Platform market. This section of the report specifically illuminates the core functional areas and various data compilation and triangulation practices followed by research experts to derive vital statistical inference specific to the growth story of the target market.
Global Data Science Platform market is segmented based by type, application and region.
Based on Type, the market has been segmented into: Major Types CoveredOn-PremisesOn-Demand
Based on application, the market has been segmented into: Major Applications CoveredBFSIRetailHealthcareITTransportationEnergy and utilitiesGovernment and defense
It offers an analysis of changing competitive scenario.
For making informed decisions in the businesses, it offers analytical data with strategic planning methodologies.
It offers seven-year assessment of Data Science Platform Market.
It helps in understanding the major key product segments.
Researchers throw light on the dynamics of the market such as drivers, restraints, trends, and opportunities.
It offers regional analysis of Data Science Platform Market along with business profiles of several stakeholders.
It offers massive data about trending factors that will influence the progress of the Data Science Platform Market.
1. What will be the Market Size and Growth Rate in the forecast year?
2. What are the Key Factors driving Data Science Platform Market?
3. What are the Risks and Challenges in front of the market?
4. Who are the Key Vendors in Data Science Platform Market?
5. What are the Trending Factors influencing the market shares?
6. What are the Key Outcomes of Porters five forces model?
7. Which are the Global Opportunities for Expanding the Data Science Platform Market?
Chapter 1: Data Science Platform Market Overview
Chapter 2: Economic Impact on Industry
Chapter 3: Market Competition by Manufacturers
Chapter 4: Production, Revenue (Value) by Region
Chapter 5: Supply (Production), Consumption, Export, Import by Regions
Chapter 6: Production, Revenue (Value), Price Trend by Type
Chapter 7: Market Analysis by Application
Chapter 8: Manufacturing Cost Analysis
Chapter 9: Industrial Chain, Sourcing Strategy and Downstream Buyers
Chapter 10: Marketing Strategy Analysis, Distributors/Traders
Chapter 11: Market Effect Factors Analysis
Chapter 12: Data Science Platform Market Forecast
Continued
About Us
Adroit Market Research is an India-based business analytics and consulting company incorporated in 2018. Our target audience is a wide range of corporations, manufacturing companies, product/technology development institutions and industry associations that require understanding of a markets size, key trends, participants and future outlook of an industry. We intend to become our clients knowledge partner and provide them with valuable market insights to help create opportunities that increase their revenues. We follow a code Explore, Learn and Transform. At our core, we are curious people who love to identify and understand industry patterns, create an insightful study around our findings and churn out money-making roadmaps.
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TetraScience and IDBS Partner to Connect Data in R&D – PRNewswire
BOSTON and GUILDFORD, United Kingdom, April 7, 2021 /PRNewswire/ --TetraScience, the world's first and only open R&D Data Cloud for scientific discovery, and IDBS, a leading R&D technology and solutions provider, announce a strategic partnership to integrate the IDBS E-WorkbookTMand Tetra Data Platform into a common ecosystem to provide end-to-end R&D data automation.
This partnership provides seamless connectivity between instrumentation and informatics solutions in life sciences R&D to enable the automatic collection, centralization, and harmonization of data in the Cloud. Users will benefit from bidirectional instrument control that streamlines workflows and enhances laboratory connectivity.
"E-WorkBook is a market-leadingplatform for the planning, execution, and reporting of scientific workflows across the R&D value chain. With over 30 years of expertise, IDBS understands the need for seamless integration of our platform within a customer's ecosystem of laboratory instruments, informatics tools and data pipelines. However, it is no longer enough to simply improve laboratory efficiency or intra-lab collaboration; our customers are demanding easy access to well-managedand contextualized data to power advanced analytics that accelerate the pace of innovation. This partnership will dramatically improve systems' interoperability and will offer the best possible digitization experience to mutual customers of IDBS and TetraScience", notes Scott Weiss, VP of Product Strategy, IDBS.
This partnership will help save time in the lab, enabling scientists to focus more on drug discovery. TetraScience and IDBS are committed to creating FAIRdata to encourage the growth of biopharma collaborations and innovations.
"As an open platform, TetraScience has built a large network of integrations, partners, and best-of-breed solutions to drive the future of life sciences R&D. We are committed to connecting every single data source and target in the biopharma R&D lab landscape, to accelerate innovation and scientific discovery," says Siping "Spin" Wang, CTO and President of TetraScience. "We are delighted to welcome IDBS as a valued member of the Tetra Partner Network."
"In order to unlock the potential of life science R&D labs and dramatically accelerate discovery, we must capitalize on the power of AI and data science. A precondition to enabling these capabilities is moving the industry away from a legacy data model of silos and point-to-point integrations, to a native and unified cloud-based data paradigm," explains Patrick Grady, Chief Executive Officer, TetraScience. "Our partnership with IDBS is an example of what can now be done to enable the life sciences industry to accelerate discoveries that can help improve lives."
About IDBS
IDBS helps research and development (R&D) teams around the world make discoveries that have the potential to transform the lives of populations worldwide. Our diverse customer list includes 22 of the top 25 global pharmaceutical companies, and other R&D-driven organizations in biotechnology, agricultural sciences, chemicals, consumer goods, energy, food and beverage, and healthcare serving over 50,000 researchers in 25 countries. Privately held since 1989, IDBS joined Danaher's Life Sciences platform at the end of 2017. IDBS will help provide the foundation for a portfolio of life sciences informatics and knowledge management solutions, within Danaher, that will accelerate the speed of discovering, developing, and producing new drugs and therapies. To learn more, visit http://www.idbs.com.
About TetraScience
TetraScience is the leading global R&D Data Cloud company with a mission to transform life sciences R&D, accelerate discovery, and improve human life. The TetraScience R&D Data Cloud provides life sciences companies with the flexibility, scalability, and data-centric capabilities to enable easy access to centralized, standardized, and actionable scientific data and is actively deployed across enterprise pharma and biotech organizations. As an open platform, TetraScience has built the largest integration network of lab instruments, informatics applications, CRO/CDMOs, analytics, and data science partners, creating seamless interoperability and an innovation feedback loop that will drive the future of life sciences R&D. For more information, please visit tetrascience.com.
SOURCE TetraScience
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TetraScience and IDBS Partner to Connect Data in R&D - PRNewswire
The best in AI appointed at Norwich Research Park – The John Innes Centre
Scientists across Norwich Research Park are part of a major integrated UK research-industry programme led by The Alan Turing Institute, seeking out the best talent in AI and data science, developing bioscience leaders and supporting the UK economy.
The 600,000 Fellowship programme is funding six fellows to support life science researchers from the Earlham Institute, the John Innes Centre, Quadram Institute and The Sainsbury Laboratory. Each fellow has been paired with a project that could benefit from data science approaches.
The fellowships are funded through The Alan Turing Institutes AI for Science and Government Strategic Priorities Fund award and a strategic award from the Biotechnology and Biological Sciences Research Council (UKRI-BBSRC) to the Earlham Institute and John Innes Centre.
Data science uses complex machine learning algorithms to build predictive models. With the ever-increasing amount of data being produced from biological research, the projects will help drive forward AI research and innovation in the UK. Developing capability and capacity to bring together industry and academia, the fellowship programme aims for AI practices to be adopted by trades through inter-sector career paths.
The year-long collaborative research projects, fellows and lead Institutes include:
James Maas, John Innes Centre
Investigating plant development by exploiting the vast amount of data at our disposal to uncover patterns in gene regulation that aims to help understand genetic behaviour during flowering time an adaptive and agronomic trait of major importance for food security.
Bethany Nichols, John Innes Centre
A major challenge in crop research is to achieve a sufficient understanding of mapping the genotype to phenotype to design crops behaviour to sync with their environment. This project develops genetic and phenotypic resources in Brassicas through computational approaches to identify genetic regulators that control important developmental transitions.
Connor Reynolds, Earlham Institute
The circadian clock is life on earths internal molecular timer, orchestrating day/night cycles and seasonal changes. The project will explore the vital role of the circadian clock in developing gene expression and regulating genetic traits associated with fitness and survival to improve key crop species.
Vladimir Uzun, Earlham Institute
Current genegene and geneprotein interaction networks which are the cornerstone of fulfilling their biological function are very poorly characterised. This project aims to apply the latest developments in machine learning to reconstruct regulatory networks across human tissues in order to predict and quantify the functional impact of genetic variation.
Odin Manuel Moron Garcia, Earlham Institute and Quadram Institute
Environments spanning from the human gut to the open oceans, hundreds of thousands of large-scale metagenomes have accelerated our understanding of the genomic diversity of the prokaryotic world. This has also brought a deluge of genomic data and the pressing need to develop new data interpretation methods through phenotypic machine learning.
Ruth Veevers, The Sainsbury Laboratory
Developing deep learning computational models to detect plant pathogen immunity by rendering recognisable visual patterns to identify interacting proteins that trigger plant pathogen immunity responses, with increased accuracy of over 90%.
Professor Richard Morris, Group Leader at the John Innes Centre, said: This initiative is a fantastic opportunity to accelerate the application of AI across the Norwich Bioscience Institutes. We were excited to see such an excellent set of projects being put forward that demonstate the huge potential for and interest in AI at the institutes and were delighted to have attracted such outstanding fellows.
The fellows will receive specialist training from their host lab, latest training in state-of-the-art AI and data science from the Turing Institute and take part in a tailored mentoring programme. We hope that this initiative will strengthen our links with the Alan Turing Institute and promote the embedding of AI into our research to tackle key biological challenges.
Professor Neil Hall, Director of the Earlham Institute, said: Modern life science research produces huge amounts of data at an exponential rate. The only way to make sense of this magnitude and complexity is for biologists to upskill themselves in the latest data science techniques, which makes this initiative hugely exciting.
Its not just about learning how to use a computer; its about choosing the right tools and techniques to spot patterns and generate valuable biological information. Another key part of data science is how we manage and curate our data so that its accessible and useful for others carrying out their own research, which will also be a critical aspect of this partnership.
Professor Jonathan Rowe, Programme Director of Data Science For Science at The Alan Turing Institute, said: The amount of data being produced in biological research is only increasing data science techniques offer the opportunity to harness and utilise this data. The appointment of these new Fellows offers an exciting opportunity to combine the Turings expertise in machine learning and artificial intelligence with the knowledge of the Norwich Biosciences Institutes to help solve some of the real-world problems in bioscience.
Professor Ian Charles, Director of the Quadram Institute, said: AI will be a key component for the future of healthcare and the prediction and prevention of disease. It is great to see these fellowships bringing further expertise in the understanding and exploitation of the complex datasets that are generated by modern bioscience.
Professor Nick Talbot, Executive Director of The Sainsbury Laboratory says: AI is set to revolutionise life sciences research, enabling predictions to be made about complex biological questions, which can then be tested by experiment. This is hugely exciting for our investigations of plant immunity and crop diseases at The Sainsbury Laboratory. The Research Fellows funded by this initiative with The Alan Turing Institute will be true pioneers in applying AI to plant and microbial sciences, working across the Norwich Research Park
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The best in AI appointed at Norwich Research Park - The John Innes Centre
Data science industry becoming more propitious field for young minds: All you need to know – India Today
The Scope of Data science has emerged as an attractive career option for freshers and professionals as well. Almost every industry, whether it be Retail, healthcare, IT, telecommunication or finances and insurance, all have opened their doors for the role of data analytics professionals.
Data has become an essential part of our life even the Google map is wholly dependent on Data to commute you to your destination. Data is one of the fast-growing and valuable commodities. It is the future oil. Today's most jobs are focused on data from social media to apps that we use on a daily basis. All over the world, organizations are focusing on methods to organize and harness the data for their strategic goals. Advertising, product designing, and implementing strategy are powered by data. The jobs related to data are growing across the globe.
Career Scope
A career in Data Science is promising, and it is one of the growing industries. Rising demands of data scientists in the current landscape, is being considered as the hottest job of the century. Scope of Data science is very wide and alluring. Being a data scientist covers a range of professions which include engineers, computer scientists, statisticians, physicists, operations researchers, actuaries, and machine learners.
Skills Required
To opt data science as a career option, students could get a Bachelors degree in Mathematics and Statistics, Computer Science, Physics, Applied Mathematics, Social Science and Engineering. The degree of these courses will help students. After Completing the bachelor degree students need to enroll in a master programmed which is related to Data Science.
Data science industry becoming more propitious field for young minds: All you need to know
In the Current Scenario Companies across various countries and regions are offering a handsome package to hire skilled and well-qualified data science professionals. Almost all industrialized countries are utilizing data science in some amount, shape or form and data scientists are in demand in most countries. Top most countries who pay the most to Data Scientists are USA, Switzerland, Norway, Australia, Canada, Germany, South Africa, France, Netherlands, and UK.
According to the Bureau of Labor Statistics the job outlook for computer and information research scientists, data scientists is projected to grow by at least 19 percent by 2026. List of the top data science companies across the globe in which every aspiring learner should apply are Microsoft, Facebook, IBM, Amazon, Google, Apple, Oracle, JP Morgan, Fractal Analytics, and Crayon Data. India is also not lagging behind to promote the job of Data Scientists and the five biggest companies in India that tremendously demand data Scientists are Fractal Analytics, Accenture, IBM, Absolute data, and Genpact.
Data is a very much reliable tool for various sectors. Almost all companies are using data analyzing tools to draw meaningful insights for their Future Growth. If a company uses their data well then data science definitely can add value to their business. As the demand for Data Science professionals in the coming years grows tremendously and mostly both new and old companies are starting to invest in this field without limits on the right professionals. So, begin your career today and be a part of a highly desired talent pool.
Read more| 4 tips to create a future-ready workplace post Covid-19
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Trifacta goes all in on the cloud – ZDNet
Trifacta, which has become the last pure play data prep tools provider still standing, sees its future as a broader based cloud software-as-a-service (SaaS) service. This week, it is unveiling a new Data Engineering Cloud that will deliver a fully managed service on each of the major clouds. That will be in addition to, not instead of Wrangler, its long-established on-premises prep suite.
Trifacta's niche will continue to be serving as the front end design studio where the data engineer, data scientist, or business developer creates the "recipes" for data preparation and transformation. The Trifacta Data Engineering Cloud will extend beyond data prep to encompass cleansing, validation, profiling, and the monitoring of data pipelines. But those pipelines will run in the downstream execution tool of choice. The Trifacta Data Engineering Cloud service won't replace the Databricks or Snowflakes of the world, but instead let users run data prep inside them. And, as for Databricks, Trifacta is also announcing today that it is taking the partnership up a notch with native integration of its data prep pipelines into the Lakehouse platform that is built around Delta Lake.
In the run-up to the announcement, Trifacta has had a good dress rehearsal for the SaaS service as the OEM partner behind Google Cloud Dataprep. The GCP offering put the Trifacta suite on a cloud-native platform running on Kubernetes (K8s), and while it was initially focused on ELT working with Google BigQuery and cloud storage, it recently added a premium tier that added support for non-Google data sources such as Oracle, SQL Server, MySQL, PostgreSQL, and salesforce.com. The premium edition serves as a prelude to the new Trifacta Data Engineering Cloud offering, which also takes advantage of the microservices and K8s architecture of the Google offering to provide the cookie cutter template for rollout to other clouds.
Beyond multi-cloud support, the Trifacta offering broadens beyond the no-code, drag and drop tool for business analyst to provide multiple pathways for designing data preparation. It now offers three views. It includes the original "grid" view, that provided the spreadsheet view for data preparation tasks, where values were reconciled to the right columns. Then it adds a flow view, which shows the entity relationships familiar to SQL developers, and the "code" view that is suited for Python programmers. While SQL developers can use DBT (Data Building tool) for writing transformations using SQL Select statements, data scientists can write transforms in Python from their Jupyter notebooks; the results populate Trifacta recipes that are handed down to execution environments. A rich library of 180+ connectors are also provided. Once the recipes are created, they can be integrated into the data pipelines or workflows of external tools or services, such as Databricks, through APIs.
When Trifacta emerged roughly a decade ago, data preparation was targeted at data lakes, viewed as a rough-cut alternative to traditional ETL tools, typically using a spreadsheet-like interface where rudimentary machine learning capabilities would suggest columns names, spot specific types of data patterns such as street address, names, or personally-identifiable data such as account numbers, and then suggest which columns could be consolidated and modest corrections to make data more correct or uniform.
These capabilities eventually became commodity, and as such, ended up getting incorporated into ETL suites, data science tools, data catalogs, and so on. Unlike the old days of enterprise data warehousing, where IT or database developers handled data transformation, data preparation became a broad-based responsibility as end users, from business analysts to data scientists, clamored for self-service. Instead of forcing these folks into different tools, data prep grew ubiquitous in their existing workspaces and tools of choice.
Also: What is low-code and no-code? A guide to development platforms
Not surprisingly, most of Trifacta's pure play rivals have either disappeared or been acquired, among them, Paxata by Data Robot less than a year and a half ago. At this point, Alteryx, which also positions itself as an "analytics process automation" workbench for citizen data scientists, remains Trifacta's best-known rival.
Not surprisingly, with core data prep functions commoditized, the new Trifacta offering goes beyond that with predictive transformation that autodetects data formats and structures and infers transformation logic; "adaptive" data quality that statistically profiles data to identify complex patterns and suggest transformation rules; and "smart" data pipelines that model data flows. While data integration, data science, and analytic tools cover data prep, Trifacta is positioning its Data Engineering Cloud as a more deluxe service.
With the new cloud service, not surprisingly, Trifacta is rolling out consumption-based pricing, providing a contrast to the traditional licensing of its Wrangler on-premises suite. It's an expected route for SaaS providers, and for Trifacta, is intended to open up its addressable market beyond large enterprises that start with six-figure investments with tiers that start with free trials and starter subscriptions at $80/month.
The service, not surprisingly, is patterned off and expands on the OEM service that Trifacta has delivered with Google for the past three years. There will be feature parity across AWS and Azure, in addition to GCP. Nonetheless, GCP will remain first among equals as a jointly supported and sold OEM offering natively integrated to BigQuery.
Trifacta's challenge is akin to that of third party databases or analytic tools that are not the captive of a specific cloud provider, analytics tool, or data science workspace. It's the classic choice between umbrella platform vs. best of breed, and single cloud vs. multi-cloud. For Trifacta, it is enterprises whose data assets and analytic platforms are heterogenous and likely to remain so. With APIs, Trifacta aims to embed its data engineering services into the workflows of whatever runtimes that business analysts, data engineers, or data scientists are using. Thanks to its three years running an OEM service on Google Cloud, Trifacta is not entering the world of SaaS as a rookie.
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Customers expect personalisation, but you dont need to be a data scientist to get there – Mumbrella
In 2021, machine learning and AI-driven personalisation are no longer a mere nice to have, but are the bare minimum of what customers expect from brands. In fact, as Amazon Web Services Worldwide Head of Business Development for Applied Artificial Intelligence Zoe Hillenmeyer shared during a recent webinar on the topic, 63% of customers see personalisation as a standard level of service.
This means when I show up, it had better be recommendation or personalisation, you had better be understanding me when I arrive, she told the audience during the virtual event. Thats a really interesting table stake that has become the norm very, very quickly.
As Hillenmeyer explained, high customer expectations around personalisation have led some marketers to question whether they have enough knowledge around data and AI to truly meet their customers demands. Much of this uncertainty is driven by the belief that only those with a deep knowledge of data science can implement AI-driven personalisation.
There tends to be a feeling that you must have a lot of depth in data science to be able to participate in crafting that experience, said Hillenmeyer. Thats the wrong way to think about whats possible with machine learning capabilities and personalisation. Technology is becoming a bridge between data science and design.
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The best implementations of machine learning and AI-driven personalisation and recommendation, she said, are being crafted by hybrid teams, made up of both creative and scientific minds. People are learning from one another in a really active agile way, she said. Its very fast, very interactive.
AWS Webinar on machine learning for CMOs
She pointed to an example from make-up store Mecca, where the brand was able to implement the capacity to personalise its email marketing within the space of a few weeks, which eventually led to a 65% increase in email click-through rates and a corresponding increase in email revenue.
The reality is that everything is personal, or at least customers want everything to be personal. So its about getting the right products, the right images, the right product, title, default categories, product rankings, outbound messaging; the whole thing.
John OMahony, partner at Deloitte Access Economics, who was also on the panel, reminded the audience that data analytics is not simply the same old marketing with some numbers added. Its also changing where marketing fits inside organisations, he said.
In a report from AWS and Deloitte called Demystifying Data, researchers spoke to 300 ANZ businesses to understand their perspectives on data. They discovered that there were gaps in perception around what kinds of data is important inside organisations.
The researchers discovered that just 35% of businesses identified industry or customer research as organisational data; while just 38% of businesses identified call centre recordings or logs as organisational data.
OMahony explained that while customers will produce data that will be relevant for marketers, that data will also be important across the entire customer service journey, and can be utilised beyond marketing alone.
Using call centre logs as an example, OMahony explained that while this data will clearly be helpful for helping to improve the outcomes for customers, it will also have benefits for other parts of the organisation including monitoring compliance, research, and supporting lead generation.
One of the frustrations Ive seen from CMOs is how sometimes their role can be narrow or compartmentalised, he said. It can be difficult to explain the benefits of marketing activity. Data analytics and machine learning offer the opportunity to better track what youre doing, and to be able to get the investments that you need to change the organisation and the marketing function.
AWS Ben Kidney, who joined Hillenmeyer and OMahony on the webinar, explained that data gives brands an opportunity to not just say, but do. He shared the example of Aussie food brand Tip Top, which was able to make good on its promise to reduce waste, doing so through the use of data.
The intent from Tip Top was to reduce waste without limiting the physical availability of their products, he said. This resulted in a 30% reduction in overstocking and a 10% reduction in understocking. So there was an environmental benefit, there was a huge cost saving, and they delivered fresher products to their customers. That is a really strong marketing proposition, enabled by data.
So how can marketers start to encourage the entire business function to support investment in machine learning? According to OMahony, in order to get funding and business support for these initiatives, marketers must get better at business advocacy.
A lot of whats happening will require interaction with other parts of the business that are holding the data, or with finance, in order to get whats needed for investment, he said. In marketing, the first thing we need to be able to do here is to put together the business case, to be given the permission internally in your business to be able to take the investment steps, get your hands on the data, and to execute something thats small, something thats doable.
Finally, for those who remain uncertain of their own knowledge base, Hillenmeyer suggests building a culture where its okay to not be an expert yet. My team have learning days once a quarter where we take the day to talk about what were working on learning, she said. We share, and we encourage each other on that journey.
Resources such as AWS free ebook, Unlock the Potential of Machine Learning for Executives in Australia & New Zealand, are also a great resource for those just starting out on their learning journey.
To discover more insights from the webinar, and to rewatch in full, click here.
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Customers expect personalisation, but you dont need to be a data scientist to get there - Mumbrella
Automated Data Science and Machine Learning Platforms Market 2021: Potential growth, attractive valuation make it is a long-term investment | Know the…
Automated Data Science and Machine Learning Platforms Market Report Coverage: Key Growth Factors & Challenges, Segmentation & Regional Outlook, Top Industry Trends & Opportunities, Competition Analysis, COVID-19 Impact Analysis & Projected Recovery, and Market Sizing & Forecast.
A detailed report on Global Automated Data Science and Machine Learning Platforms market providing a complete information on the current market situation and offering robust insights about the potential size, volume, and dynamics of the market during the forecast period, 2021-2027. The research study offers complete analysis of critical aspects of the global Automated Data Science and Machine Learning Platforms market, including competition, segmentation, geographical progress, manufacturing cost analysis, and price structure. We have provided CAGR, value, volume, sales, production, revenue, and other estimations for the global as well as regional markets.
Major Key players profiled in the report include:Palantier, MathWorks, Alteryx, SAS, Databricks, TIBCO Software, Dataiku, H2O.ai, IBM, Microsoft, Google, KNIME, DataRobot, RapidMiner, Anaconda, Domino, Altair and More
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The regional study of the global Automated Data Science and Machine Learning Platforms market explains how different regions and country-level markets are making developments. Furthermore, it gives a statistical representation of their progress during the course of the forecast period. Our analysts have used advanced Primary and Secondary Research methodologies to compile the research study on the global Automated Data Science and Machine Learning Platforms market.
Market Segment by Type, covers:Cloud-basedOn-premises
Market Segment by Applications, can be divided into:Small and Medium Enterprises (SMEs)Large Enterprises
Competitive Landscape: Competitive landscape of a market explains the competition in the Automated Data Science and Machine Learning Platforms Market taking into consideration price, revenue, sales, and market share by company, market concentration rate, competitive situations, trends, and market shares of top companies. Strategies incorporated by key vendors of the market such as investment strategies, marketing strategies, and product development plans are also further included in the report. The research integrates data regarding the producers product range, top product applications, and product specifications.
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The authors of the report have analyzed both developing and developed regions considered for the research and analysis of the global Automated Data Science and Machine Learning Platforms market. The regional analysis section of the report provides an extensive research study on different regional and country-wise Automated Data Science and Machine Learning Platforms industry to help players plan effective expansion strategies.
Regions Covered in the Global Automated Data Science and Machine Learning Platforms Market: The Middle East and Africa (GCC Countries and Egypt) North America (the United States, Mexico, and Canada) South America (Brazil etc.) Europe (Turkey, Germany, Russia UK, Italy, France, etc.) Asia-Pacific (Vietnam, China, Malaysia, Japan, Philippines, Korea, Thailand, India, Indonesia, and Australia)
Years Considered to Estimate the Market Size:History Year: 2015-2019Base Year: 2019Estimated Year: 2021Forecast Year: 2021-2026
Table of Contents: Global Automated Data Science and Machine Learning Platforms Market Research Report 2021 2026
Chapter 1 Automated Data Science and Machine Learning Platforms Market OverviewChapter 2 Global Economic Impact on IndustryChapter 3 Global Market Competition by ManufacturersChapter 4 Global Production, Revenue (Value) by RegionChapter 5 Global Supply (Production), Consumption, Export, Import by RegionsChapter 6 Global Production, Revenue (Value), Price Trend by TypeChapter 7 Global Market Analysis by ApplicationChapter 8 Manufacturing Cost AnalysisChapter 9 Industrial Chain, Sourcing Strategy and Downstream BuyersChapter 10 Marketing Strategy Analysis, Distributors/TradersChapter 11 Market Effect Factors AnalysisChapter 12 Global Automated Data Science and Machine Learning Platforms Market Forecast
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It helps companies make strategic decisions.
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Why Choose Market Info Reports?:Market Info Reports Research delivers strategic market research reports, industry analysis, statistical surveys and forecast data on products and services, markets and companies. Our clientele ranges mix of global business leaders, government organizations, SMEs, individuals and Start-ups, top management consulting firms, universities, etc. Our library of 600,000 + reports targets high growth emerging markets in the USA, Europe Middle East, Africa, Asia Pacific covering industries like IT, Telecom, Chemical, Semiconductor, Healthcare, Pharmaceutical, Energy and Power, Manufacturing, Automotive and Transportation, Food and Beverages, etc. This large collection of insightful reports assists clients to stay ahead of time and competition. We help in business decision-making on aspects such as market entry strategies, market sizing, market share analysis, sales and revenue, technology trends, competitive analysis, product portfolio, and application analysis, etc.
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Second Generation of Black Knight’s Rapid Analytics Platform Significantly Expands Data Marketplace and Team Collaboration Tools; Further Streamlines…
Supports critical business needs by streamlining workflows, significantly increasing the number of available datasets and providing easier access to Black Knight's robust data marketplace
- The Black Knight Rapid Analytics Platform (RAP) is a unique, cloud-based data marketplace and decision-science studio that allows users to directly access diverse data assets and develop analytics strategies within a single solution
- The latest version of RAP delivers a streamlined workflow experience, more intuitive navigation and an interactive data marketplace where users can easily view and explore the platform's wide variety of available data and analytics
- The datasets recently added to RAP's vast repository include Black Knight's Collateral Analytics solutions, which provide a unique combination of the company's top-rated automated valuation models, and comprehensive property and market data.
- The new features also improve collaboration within an organization, include pre-built workspaces and reports to address common industry use cases, and enable seamless interaction with Black Knight's RAP support team
JACKSONVILLE, Fla., April 6, 2021 /PRNewswire/ -- Today, Black Knight, Inc. (NYSE:BKI) announced the release of the second generation of its Rapid Analytics Platform (RAP), which includes a powerful new design that delivers a streamlined workflow experience for users. RAP also now includes several additional datasets that clients can leverage to address a variety of critical business needs.
Black Knight, Inc. Logo (PRNewsfoto/Black Knight, Inc.)
RAP is a unique, cloud-based data marketplace and decision-science studio that allows users to directly access Black Knight's massive, diverse data assets and create custom analytics within a single solution. Users can seamlessly source Black Knight data managed on the platform, connect to other data sources, execute queries, create advanced analytics and train machine-learning models.
"RAP had already changed the landscape for mortgage and housing-related data science by bringing together more primary-sourced data and advanced analytics than any platform currently available," said Ben Graboske, president of Black Knight's Data & Analytics division. "With this second iteration, we've significantly enhanced the user and workflow experience and increased the number of datasets available, while simultaneously boosting the power available to users."
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RAP is used by forward-looking mortgage, real estate, and capital markets professionals for portfolio retention strategy; equity analysis and valuation; prepayment and default analytics; pre- and post-bid due diligence; performance benchmarking; and much more.
In addition to providing access to many of the industry's deepest and most granular datasets available, RAP also offers a growing catalogue of "out-of-the-box" analytics. Designed with transparency in mind, this exposed code allows users to get started quickly with a deep understanding of how the analytics have been developed. Users can also choose to build their own analytics, or they can leverage Black Knight's highly experienced professionals to develop and deliver customized analytics.
Additionally, workspaces have been added to help users create custom views of the RAP resources relevant to a particular data science strategy.
The datasets recently added to RAP's vast repository include Black Knight's Collateral Analytics solutions, which provide a unique combination of the company's top-rated automated valuation models, and comprehensive property and market data. RAP now also offers daily mortgage loan rate-lock data from Black Knight's leading product and pricing engine, Optimal Blue PPE, as well as daily forbearance, payment, and delinquency data and other Black Knight datasets.
"Our focus with RAP has always been to deliver enhancements and innovations that help clients gain the critical insights they need from within a powerful, unified interface," Graboske continued. "The enhancements in this version, including the new and diverse datasets available via the interactive data marketplace, are key to this goal, and will keep growing with time, so RAP can continue transforming how organizations leverage data and analytics."
About Black Knight Black Knight, Inc. (NYSE: BKI) is an award-winning software, data and analytics company that drives innovation in the mortgage lending and servicing and real estate industries, as well as the capital and secondary markets. Businesses leverage our robust, integrated solutions across the entire homeownership life cycle to help retain existing customers, gain new customers, mitigate risk, and operate more effectively.
Our clients rely on our proven, comprehensive, scalable products and our unwavering commitment to delivering superior client support to achieve their strategic goals and better serve their customers. For more information on Black Knight, please visit http://www.blackknightinc.com.
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SOURCE Black Knight, Inc.
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Environmental Factor – April 2021: Data science paves the way with new tools, insights for SRP – Environmental Factor Newsletter
The NIEHS Superfund Research Program (SRP) held its first External Use Case (EUC) Showcase Feb. 18-19. Over 140 participants joined the meeting to share experiences and recommendations about integrating datasets from SRP-sponsored research. EUCs, developed by collaborations of researchers from different SRP centers, demonstrate specific scenarios in which data management and sharing could provide new insight on research questions and to identify barriers to inform future data efforts.
NIEHS SRP Director William Suk, Ph.D., emphasized the interdisciplinary nature of SRP-funded research. Combining the diverse data coming out of these programs offers a unique opportunity to uncover new scientific connections that can help us better understand the complex interplay between exposures and health, he said. SRP research teams are well positioned to use data to accelerate the pace of research and answer new questions that could not be answered before.
At the event, EUCs were presented by grantees who received supplemental funds to bring in data scientists to enhance the integration, interoperability, and reuse of SRP-generated data.
Members of the NIEHS Office of Data Science highlighted efforts to combine datasets that resulted in new insights or information shared with communities and other stakeholders.
The Data Supplement Showcase offered an opportunity for researchers to share their progress, the challenges they encountered, and their recommendations for making data more findable, accessible, interoperable, and reusable (FAIR; see top sidebar). Major outcomes from the showcase included:
Although teams discussed common challenges, there were plenty of success stories. Researchers who had never before had the chance to connect found new, inventive ways to think through their collaborations.
One of the best parts of this project was getting to know people from other institutions and putting our heads together, combining good social science and basic science to look at a problem in a new way and offer a tangible tool to help address it, said Brown University SRP Center researcher Scott Frickel, Ph.D.
A key outcome of these data [grant] supplements was having data scientists team up with subject matter experts to together develop ways to facilitate interoperability between existing datasets, said SRP Health Scientist Administrator Michelle Heacock, Ph.D. In this way, the approach used by the data scientists considered the needs of the subject matter experts.
A flurry of publications(https://tools.niehs.nih.gov/srp/data/index.cfm) is expected to come from these partnership projects. Discussions highlighted the best practices to move forward with SRP data efforts to accelerate the pace of scientific discoveries.
By combining distinct datasets across centers and disciplines, the 19 EUCs helped address a complex research question that individual groups could not tackle alone.
For example, one team includes Texas A&M University (TAMU), Brown University, and University of California (UC), San Diego. They are working to understand how land use vacant, industrial, and green space, for example affect peoples exposure to harmful chemicals, as well as community resilience and health.
Researchers and data scientists created an online interactive map using city, local, and SRP Center datasets. The map overlays factors like social vulnerability, impervious surfaces, green space, and housing conditions. With this tool, communities, regulators, and other researchers can visualize how different factors contribute to health risks. The team also published their work in December 2020.
The Boston University and Dartmouth University SRP Centers teamed up to create a searchable platform of publicly available data on contaminants in fish, environmental factors, and SRP data.
I think one of the biggest accomplishments was working to understand the types of data that we had between our partner teams, said Dartmouth SRP Center Director Celia Chen, Ph.D. It was astounding to me that we were all mercury scientists and used very different terms for similar types of measurements. That was part of the challenge of combining the data.
The team standardized the data from different sources, integrated it, and produced a new centralized repository. Their product will underpin an interactive mapping tool to provide a broad view of contaminants in fish and potential health risks by region.
Citations:Malecha ML, Kirsch KR, Karaye I, Horney JA, Newman G. 2020. Advancing the Toxics Mobility Inventory: development and application of a Toxics Mobility Vulnerability Index to Harris County, Texas. Sustainability 13(6):282291.
Heacock ML, Amolegbe SM, Skalla LA, Trottier BA, Carlin DJ, Henry HF, Lopez AR, Duncan CG, Lawler CP, Balshaw DM, Suk WA. 2020. Sharing SRP data to reduce environmentally associated disease and promote transdisciplinary research. Rev Environ Health 35(2):111122.
(Natalie Rodriguez is a research and communication specialist for MDB Inc., a contractor for the NIEHS Superfund Research Program.)
SRP publications mapped by discipline. Integrating and reusing data generated from individual research projects within the program can accelerate the pace of research. (Image from Heacock et al., 2020 used under CC BY 4.0 license)
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