Category Archives: Data Science
EIROforum to Host Conference on Grand Challenges in AI and Data Science – HPCwire
March 30, 2022 Artificial intelligence (AI) and machine learning (ML) are pushing scientific research into new domains, providing new opportunities to answer the complex societal and economic challenges facing our societies. From understanding the universe to tracking how viruses infect humans, producing large-scale scientific research requires increasingly innovative AI and ML, both for the running of cutting-edge scientific instruments and for the complex analysis of large amounts of data.
On April 28, 2022, the EIROforum alliance of European scientific infrastructures, CERN, ESO, ESA, EMBL, ESRF, ILL, European XFEL and EUROFusion will hold a conference focusing on the grand challenges in AI and data science. Hosted at the EMBL Heidelberg, with a free live streaming option for virtual participants, the conference will include workshops and talks presenting leading data science and AI from the EIROforum members, and explore how they can contribute to scientific progress with societal and economic impact.
Conference Chair, Director of EMBL-EBI and Deputy Director General of EMBL Ewan Birney explains: Europes shared scientific infrastructures include the worlds best from particle accelerators recreating the earliest moments of the Universe, to telescopes able to detect the earliest light in that Universe, and from research facilities unlocking the inner workings of cells to understand how life works on this planet, to satellite platforms able to examine the entire planet at macro and micro scales. As world leaders, we were quick to recognise the importance of data, analysis and artificial intelligence across all our diverse sciences, and the transformative impact these developments will have on science and on society. This conference will bring together the leaders in the field alongside policy makers, and stimulate further discussion on how to harness our access to large-scale scientific data with artificial intelligence, and thus help Europe thrive now and in the future.
Registration
To see the program and register for this event, please visit: https://www.embl.org/about/info/course-and-conference-office/events/eir22-01.
Simone Campana, CERN
Our infrastructures share the common challenge to collect, analyse and curate large volumes of scientific data. The novel methodologies and experience we acquired for this purpose present a solution for the needs of other sectors and society at large.
Tim Smith, CERN
Openly sharing data, technologies and infrastructure is common place in science as it enables us to build on the findings and creations of others, advancing everyone. What works for sciences grand challenges can empower society as well, as the basis for fact-based decision making.
Andreas Kaufer,ESO
EIROForum members operate complex scientific instruments and costly infrastructures. This conference is a great opportunity to share experiences in the use of new technologies such as ML and AI in this field and to discuss their potential for more effective, efficient, and sustainable solutions in scientific instrument control, operation and data production.
Vincent Favre-Nicolin, ESRF
All major research infrastructures are faced with big data challenges to process increasing amounts of data both faster and smarter, and to provide the results to users in the most efficient and durable way. The infrastructure, algorithmic and social aspects are very similar across all EIROforum members, and this conference will be an excellent opportunity to gain a wide overview.
Joo Figueiredo, EUROFusion
In modern science, the challenges of distributing, processing and analyzing vast amounts of data is of paramount importance to optimise and fully profit from the research carried forward using the infrastructures of the largest scientific organizations. The sharing of the combined know-how of the EIROforum members in data science and the applications of artificial intelligence, used in telescopes and microscopes, in fusion reactors and space satellites, is certainly of great interest.
Paolo Mutti, ILL
AI and ML are everywhere nowadays, in our connected objects, telephones and even at the hospital. The benefit of these techniques have started to enter the scientific world as well, but their potential has still to be fully exploited. Great advantages can be achieved in the way scientists perform experiments and in the quantity and quality of information that can be extracted from the measured data. This EIROforum event is a great way to exchange practices between the different partners to move forward in exploring the new opportunities offered by AI.
Source: EIROforum
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EIROforum to Host Conference on Grand Challenges in AI and Data Science - HPCwire
Analytics and Data Science News for the Week of April 1; Updates from Oracle, Domo, and Gartner, Inc. – Solutions Review
The editors at Solutions Review have curated this list of the most noteworthy analytics and data science news items for the week of April 1, 2022. In this weeks roundup, news from Oracle, Domo, and Gartner, Inc.
Keeping tabs on all the most relevant data management news can be a time-consuming task. As a result, our editorial team aims to provide a summary of the top headlines from the last month, in this space. Solutions Review editors will curate vendor product news, mergers and acquisitions, venture capital funding, talent acquisition, and other noteworthy data science and analytics news items.
MySQL HeatWave ML fully automates the ML lifecycle and stores all trained models inside the MySQL database, eliminating the need to move data or the model to a machine learning tool or service. HeatWave ML is included with the MySQL HeatWave database cloud service in all 37 Oracle Cloud Infrastructure (OCI) regions. All models generated by HeatWave ML can provide model and prediction explanations.
Read on for more.
A Data App, which combines data, analytics, and workflows, is experienced as a personalized standalone experience on a mobile device or embedded into existing apps and processes where work is already happening. Data Apps can quickly leverage data from existing systems regardless of where data lives whether it be in a cloud data warehouse or data lake, or a core application like SAP, Salesforce, or NetSuite.
Read on for more.
Gartner notes that the market is represented by an emphasis on visual self-service for end-users, as well as augmented AI to deliver automated insights. However, that augmentation is largely shifting from the analyst to consumers and decision makers. These platforms are also beginning to capture more information about user behavior and interests in order to deliver the most impactful experience possible.
Read on for more.
For consideration in future data analytics news roundups, send your announcements to tking@solutionsreview.com.
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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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Top 10 Colleges that Create the Best Data Scientists in the World – Analytics Insight
Data Scientists help the company to acquire customers by analyzing their needs.
Data scienceis booming in the global tech industry with its effective data management. Data science is closely related to data mining and big data, and all of these including analytics are becoming increasingly crucial with the evolution of technology for enterprise efficiency.Data Scientistshelp the company to acquire customers by analyzing their needs. This allows the companies to tailor products best suited for the requirements of their potential customers. Data holds the key for companies to understand their clients. Educational institutes have identified the core need to bridge the gap between the demand and supply ofdata scientistsacross different companies in all kinds of industries. Various colleges are offering attractivedata science courses. There are multiplecolleges for data sciencein the world with eminent faculty and curriculum. This article features the top 10 colleges that create thebest data scientistsin the world.
Located in London, Imperial College is a world-reputed public research university in medicine, science, business, and engineering. Imperial has been consistently ranked among the top 8 universities in the world by various professional surveys. There are various data science courses offered by Imperial College which makes it one of the top colleges that creates the best data scientists in the world.
This institution offers admission in fields like arts, science, commerce, and engineering, is considered one of the best colleges to create the best data scientists in the world. The college is known for its scientific and industrial research organization (SIRO) and other co-curricular activities and clubs.
Indian Institute of Science provides a unique interdisciplinary program that aims to bring together computational and data science aspects to address major scientific and tech-related problems existing in the modern industry. It trains students to model problems and simulates processes that vary across various disciplines of science and tech.
Columbia University is a lucrative university for data science in the world that creates the best data scientists. This course allows students to apply data science techniques, tools, projects, and others efficiently without any potential error. The curriculum covers computer systems for data science, machine learning for data science, algorithms for data science, and many more.
The data science interdisciplinary minor is open to students from all academic divisions who wish to develop skills in using and analyzing data. Such data skills can complement and enhance liberal arts study across a broad range of subject matters and interests. It is one of the top colleges that create data scientists in the world.
Located in Saint Paul, Macalester College offers various data science courses. If you are interested in pursuing graduate study, consult with an advisor in the MSCS department to discuss the most appropriate choices. You should also seek out opportunities to apply statistics to real data problems in your junior and senior years.
MIT is one of the popular US data science universities offering Applied Data Science Program to provide a deep understanding of the intricacies of data science techniques, machine learning techniques, programming languages, and many more with an industry-related portfolio of data science projects. There is also a MicroMasters Program in Statistics and Data Science that consists of four online courses to provide knowledge of tools in data science.
Indian Institute of Science provides a unique interdisciplinary program that aims to bring together computational and data science aspects to address major scientific and tech-related problems existing in the modern industry. It trains students to model problems and simulates processes that vary across various disciplines of science and tech.
The Master of Data Sciences & Business Analytics program is offered by Europes two of the most reputed Business and Engineering Schools ESSEC and Centrale Supelec. The program is taught in two locations France and Singapore. It is one of the best colleges that creates good data scientists in the world
IE School of Human Sciences & Technology offers various data science courses. IEs competitive full-time programs have been training graduates to master data science tools, big data technologies, and business transformation techniques. Programs also feature real-world case studies, multimedia simulations, debates, international projects, and so on.
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Top 10 Colleges that Create the Best Data Scientists in the World - Analytics Insight
Sophomore Opportunity Leads to Published Research on AI in Cancer Studies – Oberlin College and Conservatory
A published manuscript coauthored by Ella Halbert 23 offers software tools that pathologists could use to assist with a tedious, yet necessary process in the study of disease.
The study of disease often involves analyzing tissue samples to diagnose specific diseases, explains Halbert, a biology and Hispanic studies major. Typically, a pathologist will analyze slides of tissue samples to determine any abnormalities, but this process can be made more efficient through the use of digital pathology, which relies on images of slides, image processing, and machine learning. Essentially, machine learning programs can be trained to recognize and identify abnormalities in tissue samples, saving time and energy, and supporting pathologists workloads.
Halberts contribution to this effort began last February, when she connected with Jacob Rosenthal 18 through SOAR. The colleges Sophomore Opportunities and Academic Resources program provides opportunities for students to connect their interests inside and outside the classroom as well as offers instruction on how to use Oberlin resources, and provides individualized help with resums.
After submitting her resum and interest materials, Rosenthal, an imaging data scientist and data engineer, invited Halbert to intern at the Dana-Farber Cancer Institute. She was joined by nine other members of the group who included scientists, public health professionals, and professors of research in pathology from the Dana-Farber Cancer Institute, Massachusetts Institute of Technology, Weill Cornell Medicine, and Harvard T.H. Chan School of Public Health.
As the only undergraduate student member of such an experienced team, the two-month internship would afford Halbert a broader understanding of pathology and introduce her to a facet of medicine that was new to her: data science.
I knew a little about pathology going into the internship, but I didnt know anything about the challenges of combining data science, imaging techniques, and pathology, says Halbert. This experience has made me think more deeply about how the process of treating patients is changing as technology advances.
To prepare for her internship, Halbert independently studied the basics of Pythona high-level, general-purpose programming language. The skill set made her a welcome addition to the groups artificial intelligence operations team, where she worked closely with Rosenthal and Renato Umeton, associate director of Artificial Intelligence Operations and Data Science Services at the Dana-Farber Cancer Institute.
The manuscript written by the group is based on PathML, a software toolkit developed by Rosenthal that processes and analyzes pathology slides. Most importantly, this toolkit is meant to lower the barrier to entry for digital pathology so that pathologists with limited programming experience can utilize this powerful tool for their own research or clinical practice, says Halbert.
Although the team was unable to work in person, one-on-one virtual meetings held several times a week and weekly group sessions kept the lines of communication flowing.
The completed abstract highlights three themes to guide development of computational tools: scalability, standardization, and ease of use. The group then applied these principles to develop PathML, describe the design of the softwares framework, and demonstrate applications in diverse use cases.
In December 2021, the groups completed manuscriptBuilding Tools for Machine Learning and Artificial Intelligence in Cancer Research: Best Practices and a Case Study with the PathML Toolkit for Computational Pathology was published in Molecular Cancer Research, a monthly journal produced by the American Association for Cancer Research. Halbert received author credit for her work with the PathML software.
I think global awareness and cultural competencies are really important for any field of study, particularly the sciences, says Halbert. Im planning to pursue a medical degree, and being able to relate with patients across cultural differences is a vital skill.
Halbert currently studies the ecology of disease in professor Mary Garvins biology lab, and has applied to several summer research experiences for undergraduates that relate to disease physiology and ecology.
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What’s behind the success of post-grad computer science programs? – ZDNet
Online learning isn't a new idea. It's rooted in correspondence courses. Back in the late 1800s, postal mail services powered learning and communication platforms. Today, it's all digital, with teacher-student interaction available in real-time and on your own time.
The ongoing pandemic prompted people to reconsider their career outlook. As a result, many people decided to expand or refresh their education. In response, colleges enhanced existing online learning options and introduced new programs.
Atlanta-based Georgia Tech says it was the first accredited university to offer an online master of science in computer science, or OMSCS for short. The degree is available in a massive online format. Georgia Tech partnered with Udacity and AT&T to launch its OMSCS program in 2014.
For the spring 2022 semester, 12,016 students enrolled in the program. For the fall 2021 semester, 837 people graduated. Nearly 6,500 students have graduated so far.
David Joyner, Ph.D., is the executive director of online education and OMSCS at the College of Computing at Georgia Tech. Joyner pointed to four key factors that contributed to the success of the OMSCS program.
"Hindsight makes the success of OMSCS seem like a foregone conclusion, but at the time, it was a risky endeavor," Joyner said. "The low tuition could have undermined our on-campus program enrollment, and the high admission rate could diminish the perceived quality of the degree."
But the opposite happened. In less than a decade, the OMSCS program's reputation and visibility have driven more applications to the on-campus program. Joyner feels the online students' "incredible quality" has improved the college's reputation.
Joyner credited the willingness of the program's founders and visionaries along with Georgia Tech's administrative leaders to move forward despite the risks of starting something new.
In addition, "the faculty embraced the idea of building the online program and making sure it adhered to the standards we have come to expect on campus," Joyner said. "The courses are taught by the same professors who teach in person and who do the research that then becomes material for their classes, and that provides an authenticity that gives the program its magic."
Once the program enrolled more than 2,000 students, Joyner and his colleagues realized they couldn't support the program's growth with only on-campus teaching assistants.
"But online students have stepped up in droves to support the program," Joyner said.
Now the program employs over 400 teaching assistants, almost half of whom are alumni. Many are now professionals working in the field. As a result, their firsthand professional experience, perspectives, and insight improve the courses they're supporting, according to Joyner.
Finally, Joyner said, technology "recently reached a point where rich, authentic, active learning experiences and dynamic social learning communities can be created and scaled around the world with relative ease."
Georgia Tech claims to be the first. But today, dozens of colleges offer online-only post-grad computer science programs. They include the University of Texas at Austin, which launched its master of computer science online (MCSO) degree in 2019.
Eric Busch, Ph.D., is the director for online programs in computer science and data science at UT Austin. He said the tech job market is a factor making this kind of master's in computer science worth it for many students.
"The effects of the pandemic notwithstanding, we believe that MCSO's early success is rooted in the stark disparities of the education and labor markets in computer science fields which the program is in part designed to address," Busch said.
The gap between the number of computer science graduates and the number of open computing jobs is well documented. That scarcity creates massive unmet demand for skilled CS workers in a wide variety of areas and job functions.
The Society for Human Resource Management predicted employers would struggle to find and keep IT workers in 2022. About three months into the year, SHRM's prediction appears to be coming true.
"The gap between the number of computer science graduates and the number of open computing jobs is well documented," Busch continued. "That scarcity creates massive unmet demand for skilled CS workers in a wide variety of areas and job functions. Although companies in the tech space have raised salaries to compensate, the supply of skilled labor in these fields remains relatively inelastic."
Busch said that inelasticity is rooted in educational scarcity. Even large on-campus computer science programs like UT Austin's can only accommodate so many in-person students in any year.
"On-campus capacity remains limited in terms of financial aid capacity and physical space," Busch said.
For the spring 2022 semester, UT Austin had 860 students enrolled in the MCSO program. UT Austin faculty teach the courses, which feature lessons designed for online learning.
"Programs like MCSO represent an important intervention in this dynamic of scarcity," Busch added. "Because our online, asynchronous curriculum format can handle much higher volumes of students, we are able to admit all applicants who are qualified and capable of earning a master's degree."
We've operated for the past two years with no online program manager or MOOC partner, and I think we've been better off for it as it lets us design every element of the program to our own needs.
Joyner says academic content shifts also contribute to the OMSCS program's success.
When the program started, OMSCS partnered with a massive open online course provider that produced and hosted the school's course content.
"Now, we handle production ourselves and host content on our own platforms," Joyner said. "We've operated for the past two years with no online program manager or MOOC partner, and I think we've been better off for it as it lets us design every element of the program to our own needs."
"Our early classes were relatively lecture-heavy, and while they used a lot of active learning strategies, there was a major focus on the prerecorded video content," said Joyner. But now, he said, online M.S. in computer science courses are instead built around six focal points:
In application cycles since the pandemic started, applications for Georgia Tech's OMSCS were up 14%.
Joyner suspects the increase in applicants to this online post-graduate program in computer science is temporary. He thinks students are attracted to affordable online education at a time when "there is so much uncertainty around personal finances, global economics, and public health."
Joyner also highlighted a noteworthy demographic shift at Georgia Tech. The average age of incoming OMSCS students has dropped from 37 to 30.
That likely indicates "we are drawing more students early in their careers and fewer mid-career professionals who have been waiting more than 15 years for an opportunity to study CS in a more formal program."
"That said," he continued, "we have been wrong before: We thought we had stabilized in the first three years of the program, only to see explosive growth after that."
Busch, at UT Austin, also has a positive outlook for post-grad computer science education.
"We anticipate continued enrollment growth in both the MCSO program and in online graduate education in general," he said. "MCSO continues to add new courses, and expects to remain among the market leaders in online computer science education based on its use of tenured faculty to teach online courses, and its focus on rigor and building student community."
In 2019, Monali Mirel Chuatico graduated with her bachelor's in computer science, which gave her the foundation that she needed to excel in roles such as data engineer, front-end developer, UX designer, and computer science instructor.
Monali is currently a data engineer at Mission Lane. As a data analytics captain at a nonprofit called COOP Careers, Monali helps new grads and young professionals overcome underemployment by teaching them data analytics tools and mentoring them on their professional development journey.
Monali is passionate about implementing creative solutions, building community, advocating for mental health, empowering women, and educating youth. Monali's goal is to gain more experience in her field, expand her skill set, and do meaningful work that will positively impact the world.
Monali Mirel Chuatico is a paid member of the Red Ventures Education freelance review network.
Last reviewed March 21, 2022.
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What's behind the success of post-grad computer science programs? - ZDNet
The 15 Best DataCamp Courses and Online Training for 2022 – Solutions Review
The editors at Solutions Review compiled this list of the best DataCamp courses for data science, analytics, big data, and data engineering.
DataCamps mission is to democratize data skill for everyone by offering more than 350 different data science and analytics courses and 12 distinct career tracks. More than 2,000 companies, 3,000 organizations, and 8 million users from 180 countries have used DataCamp since its founding. DataCamps entire course catalog is interactive which makes it perfect for learning at your own pace. The online course and training leader also touts a growing list of new modules worth exploring.
Its with this in mind that the editors at Solutions Review compiled this directory of the best DataCamp courses and online training to consider, in the fields of data science, analytics, big data, and data engineering. Editor picks included in this list represent our complete coverage of the best DataCamp courses and online training from across our library of e-learning content.
Description: In this course, youll go from zero to hero, as you discover how to use this popular business intelligence platform through hands-on exercises. Youll first learn how to confidently load and transform data using Power Query and the importance of data models, before diving into creating visualizations using Power BIs drag-and-drop functionality. Youll also learn how to drill-down into reports and make your reports fully interactive. Lastly, youll level-up your skills using DAX formulas (Data Analysis Expressions) to create customized calculated columns and fields to better analyze your data.
Description: Youll learn how to navigate Tableaus interface and connect and present data using easy-to-understand visualizations. By the end of this training, youll have the skills you need to confidently explore Tableau and build impactful data dashboards. This module features 29 videos and 70 exercises, and should take around 4 hours to complete. Chapter 1, Getting Started with Tableau, is currently free.
Description: In this course, youll develop employable analyst skills as you learn how to use time-saving keyboard shortcuts, convert and clean data types including text, times, and dates, and build impressive logic functions and conditional aggregations. Through hands-on practice, youll learn over 35 new Excel functions, including CONCATENATE, VLOOKUP, and AVERAGEIF(S), and work with real-world Kickstarter data as you use your new-found Excel skills to analyze what makes a successful project.
More Top-Rated DataCamp paths: Data Analysis in Spreadsheets
Description: In this course, youll learn how to choose the best visualization for your dataset, and how to interpret common plot types like histograms, scatter plots, line plots and bar plots. Youll also learn about best practices for using colors and shapes in your plots, and how to avoid common pitfalls. Through hands-on exercises, youll visually explore over 20 datasets including global life expectancies, Los Angeles home prices, ESPNs 100 most famous athletes, and the greatest hip-hop songs of all time.
More Top-Rated DataCamp paths: Data Visualization in R, Data Visualization in Spreadsheets, Introduction to Data Visualization in Python
Description: In this course, you will learn how to build a logistic regression model with meaningful variables. You will also learn how to use this model to make predictions and how to present it and its performance to business stakeholders. The course is instructed by Nele Verbiest, a senior data scientist at Python Predictions. At Python Predictions, she developed several predictive models and recommendation systems in the fields of banking, retail and utilities.
More Top-Rated DataCamp paths: Intermediate Predictive Analytics in Python,Predictive Analytics using Networked Data in R
Description: In this non-technical course, youll be introduced to everything you were ever too afraid to ask about this fast-growing and exciting field, without needing to write a single line of code. Through hands-on exercises, youll learn about the different data scientist roles, foundational topics like A/B testing, time series analysis, and machine learning, and how data scientists extract knowledge and insights from real-world data.
More Top-Rated DataCamp paths: Data Science for Business, Introduction to Data Science in Python, Linear Algebra for Data Science in R
Description: In this course, you will learn how to build a logistic regression model with meaningful variables. You will also learn how to use this model to make predictions and how to present it and its performance to business stakeholders. The course is instructed by Nele Verbiest, a senior data scientist at Python Predictions. At Python Predictions, she developed several predictive models and recommendation systems in the fields of banking, retail and utilities.
More Top-Rated DataCamp paths: Intermediate Predictive Analytics in Python,Predictive Analytics using Networked Data in R
Description: R is mostly optimized to help you write data analysis code quickly and readably. Apache Spark is designed to analyze huge datasets quickly. Thesparklyrpackage lets you writedplyrR code that runs on a Spark cluster, giving you the best of both worlds. This course teaches you how to manipulate Spark DataFrames using both thedplyrinterface and the native interface to Spark, as well as trying machine learning techniques. Throughout the course, youll explore the Million Song Dataset.
More Top-Rated DataCamp paths: Machine Learning with PySpark, Introduction to Spark SQL in Python, Cleaning Data with PySpark
Description: Part of DataCamps robust R course directory, this module will enable you to master the basics of this widely used open-source language, including factors, lists, and data frames. With the knowledge gained in this course, you will be ready to undertake your first very own data analysis. Oracle estimated over 2 million R users worldwide in 2012, cementing R as a leading programming language in statistics and data science.
More Top-Rated DataCamp paths: Intermediate R, Exploratory Data Analysis in R
Description: This course is a gentle introduction to the R language with every chapter providing detailed mapping of R functions to SAS procedures highlighting similarities and differences. You will orient yourself in the R environment and discover how to wrangle, visualize, and model data plus customize your output for the final presentation. Throughout the course, you will follow a consistent workflow of data quality checking and cleaning, exploring relationships, modeling, and presenting results. You will leave this course with coded examples that provide a template to use immediately with a dataset of your own.
Description: Deep learning is the machine learning technique behind the most exciting capabilities in diverse areas like robotics, natural language processing, image recognition, and artificial intelligence, including the famous AlphaGo. In this course, youll gain hands-on, practical knowledge of how to use deep learning with Keras 2.0, the latest version of a cutting-edge library for deep learning in Python.
More Top-Rated DataCamp paths: Introduction to Deep Learning with PyTorch, Introduction to Deep Learning with Keras, Advanced Deep Learning with Keras
Description: In this course, youll learn how to leverage powerful technologies by helping a fictional data engineer named Cody. Using Amazon Kinesis and Firehose, youll learn how to ingest data from millions of sources before using Kinesis Analytics to analyze data as it moves through the stream. Youll also spin up serverless functions in AWS Lambda that will conditionally trigger actions based on the data received.
Description: In this course, youll learn about a data engineers core responsibilities, how they differ from data scientists and facilitate the flow of data through an organization. Through hands-on exercises youll follow Spotflix, a fictional music streaming company, to understand how their data engineers collect, clean, and catalog their data.
More Top-Rated DataCamp paths: Building Data Engineering Pipelines in Python, Introduction to Data Engineering
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.
Description: This course covers the fundamentals of Big Data via PySpark. Spark is a lightning-fast cluster computing framework for Big Data. It provides a general data processing platform engine and lets you run programs up to 100x faster in memory, or 10x faster on disk than Hadoop. Youll use PySpark, a Python package for spark programming and its powerful, higher-level libraries such as SparkSQL, MLlib (for machine learning), etc., to interact with works of William Shakespeare, analyze Fifa football 2018 data, and perform clustering of genomic datasets.
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 15 Best DataCamp Courses and Online Training for 2022 - Solutions Review
Delaware and Pyramid Will Present on Selecting the Right Analytics Tool for Your Enterprise Platform at UKISUG Analytics Symposium – Business Wire
BIRMINGHAM, England & LONDON--(BUSINESS WIRE)--Experts from Delaware and Pyramid Analytics will explore why and how Decision Intelligence whats next in analytics and business intelligence (ABI) should be introduced into enterprise platforms at the UKISUG Analytics Symposium, an annual event for the independent UK & Ireland SAP User Group being held on 5 April, 2022 at The Vox Conference Centre, Birmingham. The conference offers the opportunity for networking and collaboration among peers specialising in SAP Analytics Cloud, data preparation, platform & database and digital transformation.
UKISUG Analytics Symposium is free to attend. Register here.
Key Points:
Attendees, Mark Your Diaries
Chris Houlder, Analytics Lead at Delaware, and Ian MacDonald, Director of Product Management with Pyramid, will discuss how enterprises running on SAP can best understand their data and how to react to fast-changing conditions, and address the challenges of adopting a holistic platform that works directly on both SAP BW and SAP HANA. The session will also include a demo of the Pyramid Analytics Decision Intelligence platform and how to get more value from these SAP investments. The session begins at 10 a.m. GMT.
Partnership Brings Decision Intelligence to Enterprises Across the UKI
Pyramid Analytics and global IT services company Delaware have a partnership agreement through which the companies jointly sell and implement the Pyramid Platform for decision intelligence and provide consulting services. The partnership puts the Pyramid Platform for decision intelligence into the hands of more than 3,000 Delaware consultants. The Pyramid Platform uniquely combines Data Preparation, Business Analytics, and Data Science in a single Analytics and Business Intelligence (ABI) environment. Leading technology analysts BARC, Dresner Advisory Services, and Gartner rank Pyramid first in critical ABI capabilities.
Pyramid and Delaware have strong partnerships with SAP, the worlds largest provider of Enterprise Application Software. Delaware and Pyramid Analytics jointly deliver solutions enabling customers to integrate sophisticated SAP data sets and non-SAP sources, from a wide range of on-premises and cloud-based data sources, without moving or ingesting the data, through a single decision intelligence platform.
Many of Delawares clients have complex landscapes and face challenges pulling together accurate and coherent reporting. Pyramid enables all users, from data scientists to non-technical business users, to maximise their investments in SAP providing for the fastest, direct querying and analytics on BW, BEx, HANA, CDS and IQ, while maintaining the investment in business logic and security designed into SAP all within a complete point and click, no-code, governed self-service analytics environment.
Quotes
Ian Macdonald, Director of Product Management, Pyramid Analytics: Business success in todays dynamic markets requires organizations to react to trends and opportunities in real time with accuracy, speed and scale. Data drives better decisions. However, due to legacy tool limitations, integration issues and data management challenges, many SAP customers struggle to expose all the necessary data to deliver modern self-service analytic capabilities. Pyramid Decision Intelligence Platform allows business users to get more value out of their existing SAP BW and SAP HANA investments, delivering best-in-class functionality and performance that preserves the security and governance inherent in the SAP platform.
Complete, Unified Decision Intelligence
Pyramids Decision Intelligence Platform unifies Data Preparation, Business Analytics, and Data Science on a single, integrated platform. This eliminates the need to use multiple disparate tools and the associated license cost and management complexity. Lower Total Cost of Ownership (TCO), rapid rollout, quicker and direct access to all available data, and industry-leading user adoption means faster time to value. Pyramids Decision Intelligence Platform can be deployed on-premises, into a private or public cloud, embedded into other apps or delivered through Managed Services Providers (MSP).
About Delaware
Delaware is a fast-growing, global company that delivers advanced solutions and services to organisations striving for a sustainable, competitive advantage. Delaware guides its customers through their business transformation, applying the ecosystems of its main business partners, SAP and Microsoft. Delaware continues to service its customers afterwards, assuring continuity and continuous improvement. Delaware has over 3000 professionals in 24 offices around the world. For more information, please visit http://www.delaware.co.uk.
About Pyramid Analytics
Pyramid is whats next in analytics. Our unified decision intelligence platform delivers insights for everyone to make faster, more informed decisions. It provides direct access to any data, enables governed self-service for any person, and serves any analytics need in a no-code environment. The Pyramid Decision Intelligence Platform uniquely combines Data Prep, Business Analytics, and Data Science in a single environment with AI guidance, reducing cost and complexity while accelerating growth and innovation. The Pyramid Platform enables a strategic, organization-wide approach to Business Intelligence and Analytics, from the simple to the sophisticated. Schedule a demo.
Pyramid Analytics is incorporated in Amsterdam and has regional headquarters in global innovation and business centers, including London, New York City, and Tel-Aviv. Our team lives worldwide because geography should not be a barrier to talent and opportunity. Investors include Jerusalem Venture Partners (JVP), Sequoia Capital and Viola Growth. Learn more at Pyramid Analytics.
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Heap Named to The World’s Top Data Startups List – Business Wire
SAN FRANCISCO--(BUSINESS WIRE)--Heap, the leading digital analytics provider, announced that it was named to the inaugural Data50: The Worlds Top Data Startups list by VC firm a16z. The top 50 were chosen based on their technologies ability to compile and obtain meaningful insights from that technology, which is critical to business success.
We are pleased to receive this latest recognition that further validates our product analytics offering, which delivers better insights faster, enabling teams to create great digital experiences, said Ken Fine, CEO of Heap. Heap is challenging the status quo of legacy analytics that take months to set-up and deliver limited insights due to their limited data capture. By blending a complete set of behavioral data with integrated data science capabilities, Heap gives teams significant advantages over their competitors.
The Data50 list is compiled by a16z, the Andreessen Horowitz VC company, and showcases software businesses founded after 2008 that have raised new funding in the past two years. To qualify for the list, companies must also have a growing employee base of at least 30% YoY, and provide horizontal technologies that service teams across industries through data or data application.
About Heap
Heap is the future of digital insights, providing the best alternative to costly, slow and inaccurate legacy analytics. Heaps low-code, easy-to-use digital analytics software provides the quickest time to insight so teams can create the best possible digital experiences and accelerate their business. Over 8,000 businesses trust Heap to increase revenue, improve conversion, accelerate decision-making, and drive business impact at scale.
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Heap Named to The World's Top Data Startups List - Business Wire
CSRWire – Bayer: The Breakthrough Innovation Forum – CSRwire.com
Published 04-01-22
Submitted by Bayer
What do you get when you combine the best ideas coming out of biology, chemistry and data science?
A powerful tool with the potential to change lives for the better.
From curing incurable diseases, providing people with preventive tools to live healthier, better and longer lives, to producing enough food for our growing population without starving the planet, we are on the brink of unlocking a world of enormous potential.
While the worlds biggest challenges may appear to be very different in nature, the key to overcoming them could be similar. It all comes down to this confluence of the life sciences and data science.
Join us for the Bayer Breakthrough Innovation Forum where we will shed light on the promise this new era in the Life Sciences holds for humanity.
Join us April 1 for a virtual event-Add to calendar
What does this mean for health and nutrition?
While still in its early stages, the convergence of chemistry, biology and data science to accelerate innovation is much more than a theoretical scientific concept. Scientists around the world are already working on applications that leverage todays enhanced technological toolkit to decode and engineer biology for the benefit of people and the planet.
We are driven to push the limits of what medicine can do today. Cell and gene therapies can move the needle from managing sick care, to providing true healthcare. Precise, personalized care could one day be available to everyone on the planet beforethey get sick.
In agriculture, biotechnology will be a critical enabler for our ability to feed the 10 billion people that will be on the planet by 2050 while at the same time fighting the impact of climate change. To grow more food with fewer resources like water, we will need to shift to a regenerative approach and make crops more resilient to climate impacts.
Shorter stature corn, and the number of resources it saves by not snapping in high winds, is just one example of climate-smart agriculture already in action. And advances in digital farming are giving growers the opportunity to maximize the amount of carbon they capture from the atmosphere. Our work to establish the carbon marketplace is one more example of genetics and data coming together to help solve what previously seemed unsolvable.
"Health and nutrition are among the most basic needs of societies around the globe. Its the definition of systemic relevance. Based on the converging worlds of genes, cells and data, we see anew foundation for scientific breakthroughs in those areas."
Werner Baumann , Chairman of the Board of Management (CEO) of Bayer AG
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Bayer: Science For A Better Life
Bayer is a global enterprise with core competencies in the Life Science fields of health care and agriculture. Its products and services are designed to benefit people and improve their quality of life. At the same time, the Group aims to create value through innovation, growth and high earning power. Bayer is committed to the principles of sustainable development and to its social and ethical responsibilities as a corporate citizen. In fiscal 2015, the Group employed around 117,000 people and had sales of EUR 46.3 billion. Capital expenditures amounted to EUR 2.6 billion, R&D expenses to EUR 4.3billion. These figures include those for the high-tech polymers business, which was floated on the stock market as an independent company named Covestro on October 6, 2015. For more information, go to http://www.bayer.com.
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CSRWire - Bayer: The Breakthrough Innovation Forum - CSRwire.com
Top 10 Big Data Resolutions that Business Must Abide by in 2022 – Analytics Insight
Let us take a look at the 10 of the best big data resolutions that businesses should follow in 2022
Big data, like all technology, is always changing, and the start of a new year is an excellent moment to take stock, identify areas for improvement, and look for new opportunities.
Big data, AI, and analytics will reach a tipping point in 2022, with more firms expecting concrete business benefits. However, from the perspective of IT, there is still a lot of work to be done.
Big data is a general term that refers to both structured and unstructured data collections that are too massive and complicated for typical data processing tools and systems to handle. Predictive analytics, user behaviour analytics, and other advanced data analytics approaches that extract value from big data are frequently powered by Big Data Resolutions and are rarely limited to a specific data set size.
To capture all of an organizations data and then feed it operationally and to analytics, Big Data Resolutions solutions are required. The Leadership Stage embraces analytics and incorporates it into all applications and business processes.
Here are the ten latest Big Data resolutions for an IT or Business which should be followed in 2022:
Many companies have just kicked the can down the field, avoiding any conversation about huge data retention. This could be due to apprehension about what would be required if the corporation were forced to conduct legal discovery in the event of a lawsuit, but its more probable that data retention is absent because no one has set aside time to do it.
IT should focus on bringing Big Data Resolutions as well as more traditional structured data into the data fabric it creates to link up all of these silos and repositories to break down departmental system silos and make across-the-organization data available to everyone for analytics and decision making.
Implementing no-code and low-code reporting technologies for analytics can help end users get more analytics reports faster while also reducing IT workload.
Its great to put an analytics application into production, but is it still serving the business as well as it did when it was initially deployed two years ago?
Businesses are always changing. There will inevitably be a drift between what analytics solutions continue to focus on and what the business requires right now. In 2022, you should evaluate the performance of the analytics applications you now have in place to determine how effectively they are working and whether they are still satisfying the requirements of the business use cases for which they were built.
Big Data Resolutions and analytics, like structured data and applications, require ongoing maintenance. However, many companies that use analytics and big data dont have maintenance practices in place. Maintenance procedures for big data and analytics in production have reached a point of maturity where they should be established and practised.
New IT skills are required for workers to manage and assist Big Data Resolutions operations and analytics. Additional training in data analysis, data science, big data storage and processing management, as well as proficiency with emerging development technologies like low-code and no-code analytics, may be required.
Big data, for example, can be obtained from a number of different third-party sources. These sources, as well as your own internal Big Data Resolutions, should be evaluated on a regular basis for compliance with company security and privacy guidelines.
Although many vendors offer big data and analytics technologies, not all of them provide the same level of support when you need it. Working with providers who provide active assistance for your employees in the use of Big Data Resolutions and analytics tools, as well as direction throughout significant projects, is critical. If youre working with vendors who dont provide the degree of assistance you require, its a good idea to switch to someone who does.
Almost every business aims to improve its consumers experience with it. The development of customer-facing automation and support aids for assisting customers in having requests, queries, and issues answered is at the heart of this process.
Customer-facing systems that employ NLP (natural language processing) and AI (artificial intelligence) to interpret customer sentiment and engage in discussions are still in the early stages of development. Companies that concentrate on enhancing NLP and AI efficiency in these areas will gain a competitive advantage in the upcoming years.
When Big Data Resolutions and analytics were first deployed in businesses, there was a lot of talk about them. These technologies are now more developed and are making their way into the mainstream of corporate systems.
CIOs should meet with other C-level executives and stakeholders in 2022 to review AI and analytics progress and win support for the next steps.
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Top 10 Big Data Resolutions that Business Must Abide by in 2022 - Analytics Insight