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Lead Stearate Market | Scope,Demand by Regions, Key Vendors landscape, And Growth Forecast | WSD Chemical limited, American Elements, Triveni…

January 31, 2021 | Author: admin

The Lead Stearate Marketreport mainly encompasses fundamental dynamics of the market which include drivers, restraints, opportunities and challenges faced by the industry.It provides valuable insights with an emphasis on global market including some of the major players.The report puts a light on the market structures, driving forces, scope, and competitive landscape for your business whichhelps ineffortless decision-making process.

The report displays the systematic investigation of current scenario of the market, which covers several market dynamics. The principal areas of market analysis such as market definition, competitive analysis,market segmentation, and research methodology are studied very vigilantly and precisely throughout the report.

Market Definition & Scope:

Lead stearate refers to a lead based compound having the molecular formula of Pb (C17H35COO)2 which is utilized as chemicaladditive. Other names of this compound are Octadecanoic acid, Stearic Acid Lead Salt, lead octadecanoate and Lead(II) Stearate. The product is synthesized through various chemical processes such as metathesis method and requires lead acetate and stearic acid as raw materials during commercial production.Lead stearate market is expected to gain market growth in the forecast period of 2021 to 2028. Data Bridge Market Research analyses that the market is growing with the CAGR of 1.80% in the forecast period of 2021 to 2028 and is estimated to reach USD value of 150.80 million by 2028. The increase in use of lead stearate as chemical additive across the globe is escalating the growth of lead stearate market.

Competitors Analysis:CompetitorsProfiles Includes: Overview, Product & Services Offerings, Financials,New Developments and Innovation

The major players covered in the lead stearate market report are WSD Chemical limited, American Elements, Triveni Interchem Private Limited, aivitchem, Pratham Metchem LLP, POCL Enterprises Limited, Beijing Yunbang Biosciences Co.Ltd., Shristab Pvt. Ltd., Chongqing ChangFeng Chemical Co.,Ltd., Xiamen Hisunny Chemical Co., LTD, Qingdao Echemi Technology Co., Ltd., Hengshui Taocheng Chemical Auxiliary Co., Ltd., Hangzhou Oleochemicals Co., Ltd., Hunan Shaoyang Tiantang Auxiliaries Chemical, Co., Ltd, Asian Organo Industries, Sancheti Polymers, Zauba Technologies Pvt Ltd, Stabplastchemo., Nexus Polychem., Almstab, Vishal Pharmakem among other domestic and global players. Market share data is available for global, North America, Europe, Asia-Pacific (APAC), Middle East and Africa (MEA) and South America separately. DBMR analysts understand competitive strengths and provide competitive analysis for each competitor separately.

Download Free PDF Sample Report with Statistical info @https://www.databridgemarketresearch.com/request-a-sample/?dbmr=global-lead-stearate-market

Lead Stearate Market report covers the different market scenarios that have direct impact on the growth of the market. The report is structured with the meticulous efforts of an innovative, enthusiastic, knowledgeable and experienced team of analysts, researchers, industry experts, and forecasters. In the end, the report makes some important proposal of the new project of Lead Stearate industry before evaluating its feasibility.

Report includes analysis on:

Key Market Development:

The report provides in-depth information about profitable showing markets and analyzes the markets for the global Lead Stearate market. It provides full information about new product launches, current developments, and investments in the global market. The report delivers an complete evaluation of market shares, strategies, products, and manufacturing capabilities of the top players in the global market.

For Any Enquiry or Specific Requirement Speak to Our Analyst @https://www.databridgemarketresearch.com/speak-to-analyst/?dbmr=global-lead-stearate-market

This Lead Stearate report provides Scope of the market where it identifies industry trends, determines brand awareness and influence, provides industry insights and offers competitive intelligence. Lead Stearate Market report includes noteworthy information alongside future conjecture and point by point market scanning on a worldwide, regional and local level for the industry.

Key Pointers in Table of Content:

Chapter 1. Research Objective1.1 Objective,Definition & Scope1.2 Methodology1.2.1 Primary Research1.2.2 Secondary Research1.2.3 Market Forecast Estimation & Approach1.2.4 Assumptions & Assessments1.3 Insights and Growth Relevancy Mapping1.4 Data mining & efficiency

Chapter 2. Executive Summary2.1 Lead Stearate Market Overview2.2 Interconnectivity & Related markets2.3 Lead Stearate Market Business Segmentation2.4 Lead Stearate Market Geographic Segmentation2.5 Competition Outlook2.6 Key Statistics

Chapter 3. Strategic Analysis3.1 Lead Stearate Market Revenue Opportunities3.2 Cost Optimization3.3 Covid19 aftermath Analyst view

Chapter 4. Market Dynamics4.1 DROC4.1.1 Drivers4.1.2 Restraints4.1.3 Opportunities4.1.4 Challenges4.2 PEST Analysis4.2.1 Political4.2.2 Economic4.2.3 Social4.2.4 Technological4.3 Market Impacting Trends4.4 Porters 5-force Analysis

Chapter 5. Segmentation & Statistics5.1 Segmentation Overview5.2 Demand Forecast & Market SizingContinued..

Get Full Table of Contents with Charts, Figures & Tables @ https://www.databridgemarketresearch.com/toc/?dbmr=global-lead-stearate-market

About Us:

Data Bridge set forth itself as an unconventional and neoteric Market research and consulting firm with unparalleled level of resilience and integrated approaches. We are determined to unearth the best market opportunities and foster efficient information for your business to thrive in the market. Data Bridge endeavors to provide appropriate solutions to the complex business challenges and initiates an effortless decision-making process.We ponder into the heterogeneous markets in accord with our clients needs and scoop out the best possible solutions and detailed information about the market trends. Data Bridge delve into the markets across Asia, North America, South America, Africa to name few.Data Bridge adepts in creating satisfied clients who reckon upon our services and rely on our hard work with certitude. We are content with our glorious 99.9 % client satisfying rate.

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Berkeley’s data science leader dedicated to advancing diversity in computing – UC Berkeley

January 31, 2021 | Author: admin

Jennifer Chayes, associate provost of the Division of Computing, Data Science, and Society, and dean of the School of Information, discussed her vision for the future of data science at UC Berkeley. (UC Berkeley video)

From dictating which posts appear in our social media feeds to deciding whether or not a suspect might be guilty of a crime, data and computing have come to permeate nearly all aspects of our lives. But while these systems can offer many benefits, their faults whether data breaches, unintentional biases in algorithms or the proliferation of misinformation can have disastrous effects, especially on already marginalized individuals and communities.

Thats why Jennifer Chayes, UC Berkeleys new data science leader, is dedicated to creating an environment where data and computing are informed by leaders from all disciplines, including ethics and the humanities, and where people of all races, genders and socioeconomic backgrounds are welcomed at the table.

Chayes, associate provost of the Division of Computing, Data Science, and Society (CDSS) and dean of the School of Information at Berkeley, discussed her vision for the future of CDSS at a virtual Campus Conversations event on Wednesday.

More and more of our public systems (our) criminal justice system, our health system, our education system, our social welfare system [are] being mediated by computing. As [data science] becomes the fabric of our society, [we need to ensure) that it is a fabric that will serve its purpose properly, Chayes said. We need women, we need Black people, we need Latinx and Indigenous people building this fabric, because they will understand in ways different from the majority how [data] may be used.

Chayes left her position as a technical fellow at Microsoft Research to lead CDSS in January 2020. Part of what drew her to Berkeley was the sheer scale of the data science research happening on campus, coupled with the wide variety of fields data scientists were working in from climate change and sustainability to biomedicine and public health to human rights.

I think, at Berkeley, we are going to have just many, many more disciplines interacting with each other, Chayes said, when asked about her hopes for the future of the division. I will feel like a failure if we dont have joint faculty with every division and school and college on campus because I think that all voices have to be here, everyone has to be at the table for this to be a success.

To help increase racial diversity in data science fields, Chayes said that the division has approached historically Black colleges and universities about creating joint masters programs. The data science major also tends to attract a diverse array of students, many of whom didnt necessarily intend to go into data and computing when they entered Berkeley.

The CDSS is also planning the construction of a new data science building that will include extensive convening space for students, staff and faculty to collaborate.

People really need to mix with each other, Chayes said. Its something that I learned at Microsoft. I tried to have as flat of organizations as possible with philosophers, anthropologists and biologists and physicists and mathematicians and computer scientists and lawyers coming together and talking with each other. Its not just learning the language of another discipline, it is really understanding what are the important problems of other disciplines and why.

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The chief data scientist: Who they are and what they do – Siliconrepublic.com

January 31, 2021 | Author: admin

Anodots Ira Cohen shares what it means to be a chief data scientist, particularly in the era of Covid-19.

Weve been exploring the topic of working in data for some time on Siliconrepublic.com. Weve looked at the difference between a data scientist and a data analyst, the types of skills data professionals need and how much you could expect to earn in the industry. But something we havent discussed in depth is the role of a chief data scientist.

Ira Cohen is co-founder and chief data scientist at Anodot, a US analytics company that helps businesses detect anomalies in revenue, customer interactions and more. He also previously worked as chief data scientist at Hewlett-Packard Enterprise.

In his current position, Cohen draws on AI and machine learning to develop real-time algorithms to carry out this detection. Here, he explains the differences between his role and that of a CTO, and why chief data scientists will be critical after Covid-19.

Truly great chief data scientists know how to walk a fine line between driving creative innovation and pragmatic solutions IRA COHEN

The chief data scientist manages a range of data-driven functions including overseeing data management, creating data strategy and improving data quality. They also help their organisations extract the most valuable and relevant insights from their data, leveraging data analytics and business intelligence.

Perhaps most importantly, organisations rely on the chief data scientist to bridge the gap between management and the data science teams, helping them understand what machine learning can achieve and, conversely, not accomplish. The chief data scientist has a much deeper understanding of these technologies than the CTO, who likely has a broader knowledge base but not necessarily the deep expertise.

Machine learning is a remarkable innovation when supported by large amounts of data but the journey from big data ideas to successful machine learning implementations is often a complex and arduous one. This path requires a trusted navigator who can help the data science team overcome potential challenges that is where the chief data scientist comes in.

Experienced chief data scientists understand that data is the fuel behind key initiatives and know the non-deterministic risk of developing these capabilities. They bridge the gap between organisational expectations and the reality of what machine learning can accomplish, while understanding how to mitigate the risks associated with complex data-driven endeavours.

Many organisations are discovering that they really need a chief data scientist. For data-driven organisations, this role has become a must-have position rather than a luxury.

Since Covid-19 we have seen the rise of the chief data scientist, especially as organisations accelerate their digital transformations. Right now, everyone is engaging customers and partners in different ways in the digital world, launching new business models and finding better ways to bring products and services to market. This has led organisations to embrace more ambitious data strategies that require more experienced data science leadership.

We are seeing chief data scientists become heavily involved with board-level and C-suite-driven corporate strategies as data becomes even more central to critical company decisions. IDC recently completed a survey that revealed that 59pc of chief data scientists now report to their CEO or another C-suite executive, which illustrates just how far this role has come in a short period of time.

One of the most important things a chief data scientist can do over the next few months is to use machine learning to solve the most pressing business problems created by Covid-19 and the global recession.

For example, churn prediction is a key dilemma for organisations right now specifically, they must forecast which customers are most likely to stop being customers. This important task requires superior data analysis know-how. Moreover, assessing churn predictions requires different levels of technical and data science expertise qualities that the chief data scientist already has.

For example, some organisations need to predict churn in real time, while others must assess churn for each customer once a month. This requires different expertise and product sets with unique machine learning requirements.

Having a chief data scientist navigate these varied scenarios would likely deliver positive outcomes as that individual could understand the scope of the data science work required and complete a thorough analysis of the approaches that will or wont work all while balancing the business and technical trade-offs of taking one approach over the other.

This level of understanding will make all the difference when it comes to finding the most effective solutions that yield the desired results under the right cost and time parameters.

Truly great chief data scientists know how to walk a fine line between driving creative innovation and pragmatic solutions. As data scientists are researchers at heart, they need the time and space to explore different problem sets and possible data-driven solutions. At the same time, they must also deliver real-world data management solutions that solve their organisations pressing business problems. The ideal chief data scientist knows how to rally teams to deliver both.

For researchers, it is all too easy to go down rabbit holes searching for the best solutions. Sometimes you find the gold, but many times you do not uncover it. Talented and resourceful chief data scientists know when to pull their teams out of the rabbit holes; when they have asked all the right questions and done the hard work but still cannot find the treasure. That is when they must be pulled out to avoid wasting too much time, and then you can move them on to the next hole.

Many organisations let their data science teams spend too much time with their heads buried in rabbit holes that end up not bearing any fruit. Finding the right balance between exploration and pragmatic solutions is a key role for the chief data scientist.

Machine learning is the most important technology for chief data science officers in the year ahead.

Machine learning is particularly critical for data science professionals right now. Many of them are engaged in a build-versus-buy debate regarding products or services that offer machine learning as a core feature.

Organisations that opt to build their own platforms with machine learning capabilities should only do so if they are creating mission-critical applications. Otherwise, they will likely spend a great deal of time and expense building an internal technology that will not deliver as much value as the time and effort they put into it.

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Software AG’s TrendMiner 2021.R1 Release Puts Data Science in the Hands of Operational Experts – Yahoo Finance

January 31, 2021 | Author: admin

HOUSTON, TX, HASSELT, BELGIUM and DARMSTADT, GERMANY / ACCESSWIRE / January 28, 2021 / Software AG's TrendMiner has announced the release of TrendMiner 2021.R1. This latest release brings a completely new functionality of notebook integration, which helps users access both data dashboards and code-based data analysis. Also in 2021.R1 are extended capabilities to support multiple asset frameworks and many new user-driven features to help end users improve operational performance and overall profitability.

Bridge the knowledge-gap between engineers and data scientists with embedded notebooksTrendMiner enables operational experts in process industries to analyze, monitor, and predict operational performance using sensor-generated time-series data. The goal of TrendMiner has always been to empower engineers with analytics for improving operational excellence, without the need to rely on data scientists. In doing so, TrendMiner brought data science to the engineer. In the 2021.R1 release, TrendMiner makes the next step of this journey by integrating notebook functionality into the software so that users can easily jump from looking at data in a TrendMiner view to working with it in a code-based data science environment.

With their data science libraries of choice (e.g. Pandas, NumPy, SciPy, SciKit-Learn), engineers can create and run custom scripts themselves for advanced statistical analyses and use AutoML capabilities to build machine learning models for anomaly detection. On top of that, they can operationalize the resulting notebook visualizations (also created with libraries of their choice such as Matplotlib, Plotly, Seaborn) as dashboard tiles in TrendMiner DashHub.

Thomas Dhollander, CTO at TrendMiner commented, "Classical data science depends on bringing process / asset know-how to the data scientist, while self-service analytics aims at packaging a subset of data science modeling capabilities and bringing these to the subject matter expert as a robust set of features (no technical tuning parameters, no data science training needed). Companies that recognize the potential in interweaving these complementary approaches will be the ones that can accelerate their operational efficiency and competitive advantage."

Story continues

Support for multiple asset frameworks for globally operating usersTo support enterprise rollouts and the increased complexity of existing IT-landscapes, TrendMiner has extended its capabilities for handling multiple plant breakdown structures also known as asset frameworks. OSIsoft PI users can easily connect multiple OSIsoft PI Asset Framework servers and set access permissions. Besides support for multiple PI AF structures, multiple CSV asset trees can be imported for use as a data source within TrendMiner. As a result, System Administrators can better control accessibility with the ability to publish and unpublish structures, while the users have more flexibility to analyze the operational performance of multiple plants and production lines, each with their separate plant breakdown structures.

Further informationIn each release, TrendMiner adds a new range of features and enhancements that are requested by its users. There are many more improvements in the TrendMiner 2021.R1 release, which users can find in the TrendMiner release notes on the website: http://www.trendminer.com. To see TrendMiner's functionality in-action and learn how analytics-empowered process and asset experts can help accelerate operational performance and increase profitability, click here to request a demo.

About TrendMinerTrendMiner, a Software AG company and part of the IoT & Analytics division, delivers self-service data analytics to optimize process performance in industries such as chemical, petrochemical, oil & gas, water & wastewater, pharmaceutical, metals & mining, and other process industries. TrendMiner software is based on a high-performance analytics engine for time-series data that allows users to question data directly, without the support of data scientists. The plug-and-play software adds immediate value upon deployment, eliminating the need for infrastructure investment and long implementation projects. Search, diagnostic, and predictive capabilities enable users to speed up root cause analysis, define optimal processes, and configure early warnings to monitor production. TrendMiner software also helps team members to capture feedback and leverage knowledge across teams and sites. In addition, TrendMiner offers standard integrations with a wide range of historians such as OSIsoft PI, Yokogawa Exaquantum, AspenTech IP.21, Honeywell PHD, GE Proficy Historian, and Wonderware InSQL.

Founded in 2008 and now part of Software AG, TrendMiner's global headquarters is located in Belgium and has offices in the U.S., Germany, Spain, and the Netherlands.

Media Contact:

Dawn FontaineRipple Effect Communicationsdawn@rippleeffectpr.com+1-617-536-8887

SOURCE: TrendMiner

View source version on accesswire.com: https://www.accesswire.com/626468/Software-AGs-TrendMiner-2021R1-Release-Puts-Data-Science-in-the-Hands-of-Operational-Experts

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Book Review: Hands-On Exploratory Data Analysis with Python – insideBIGDATA

January 31, 2021 | Author: admin

The new data science title Hands-On Exploratory Data Analysis with Python, by Suresh Kumar Mukhiya and Usman Ahmed from Packt Publshing is a welcome addition to the growing list of books directed to help newbie data scientists improve their skills. Im always on the lookout for texts that can help my students find their way along the challenging path toward becoming a data scientist. I think this book fills a void for Exploratory Data Analysis (EDA) learning resources. But as Ill discuss, the book goes beyond just EDA, and is maybe mistitled its really an introduction to data science and machine learning using the Python language.

The book includes important EDA topics like Descriptive Statistics (Chapter 5), Grouping Datasets (Chapter 6), Correlation (Chapter 7), Time Series Analysis (Chapter 8), and Hypothesis Testing (first part of Chapter 9). These are all critical pieces of the data science process, and lucid discussions along with clear and simple code examples help the reader get moving. The publisher provides all the Python code from the book so the reader can hit the ground running.

My favorite part of the book is Chapter 4 on Data Transformation (aka data munging, or data wrangling). This is a very important area that often accounts for a majority of a projects time and cost budget, and the examples provided in this chapter cover the most commonly needed tasks for a typical data science project (e.g. missing data handling, discretization, random sampling, etc.). Interestingly, data transformation isnt really part of EDA, but I welcome the discussion as it broadens the scope of the book.

Chapter 2 on data visualization is a nice adjunct to the EDA discussions, because these two areas typically go hand-in-hand. Chapter 3 offers up an interesting use-case for demonstrating data access, data transformation, EDA, and data viz. The example centers around reading in all the emails from your Google account and performing a useful data analysis on the data. Nice touch!

Finally, the book also enters the realm of supervised machine learning, starting with the last part of Chapter 9 on regression models. Then Chapter 10 is a short introduction to various machine learning techniques. This chapter, however, is too brief to be a standalone learning resource, but it does kick-start the reader into thinking about this important topic.

The presumed goal of the last chapter, Chapter 11, is to offer a comprehensive data science example using the well-known Wine Quality data set from the UCI Machine Learning Repository. Ive used this data set in my own class materials many times, and its well-suite for this purpose. My only caveat about this chapter is that its too simplistic and too short. But it does give a correct feel for the steps in the data science process, culminating in the use of a number of common ML algorithms and their interpretation.

I would say Hands-On Exploratory Data Analysis with Python is a good addition to the library of a newbie data scientist as it contains many of the most common techniques for putting together a solid machine learning solution. I will be adding this title to my data science bibliography given out to my Introduction to Data Science students.

Contributed by Daniel D. Gutierrez, Editor-in-Chief and Resident Data Scientist for insideBIGDATA. In addition to being a tech journalist, Daniel also is a consultant in data scientist, author, educator and sits on a number of advisory boards for various start-up companies.

Sign up for the free insideBIGDATAnewsletter.

Join us on Twitter:@InsideBigData1 https://twitter.com/InsideBigData1

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The 12 Best R Courses and Online Training to Consider for 2021 – Solutions Review

January 31, 2021 | Author: admin

The editors at Solutions Review have compiled this list of the best R courses and online training to consider for 2021.

R is a language and environment for statistical computing and graphics. R can be considered as a different implementation of S, and while there are some important differences, much of the code written for S runs unaltered on R. The language provides a variety of statistical and graphical techniques including linear and nonlinear modeling, classical statistical tests, time-series analysis, and classification and clustering. R capabilities are enhanced via user-created packages that allow for special statistical techniques, graphical devices and reporting.

With this in mind, weve compiled this list of the best R courses and online training to consider if youre looking to grow your data science programming skills for work or career advancement. This is not an exhaustive list, but one that features the best R courses 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.

Note: The best R courses and training modules are listed alphabetically by online learning platform name.

Platform: Codecademy

Description: Part of the Analyze Data with R skill path, this course will expose you to fundamental programming concepts in R. After the basics, youll learn how to organize, modify and clean data frames, a useful data structure in R. Then youll learn how to create data visualizations to showcase insights in data! Finish up with statistics and hypothesis testing to become a data analysis expert. You do not need to know how to code to enroll in this course.

Related path/track: Learn Statistics with R

Platform: Coursera

Description: In this course, you will learn how to program in R and how to use R for effective data analysis. You will learn how to install and configure software necessary for a statistical programming environment and describe generic programming language concepts as they are implemented in a high-level statistical language. The course covers practical issues in statistical computing which include programming in R, reading data into R, accessing R packages, writing R functions, debugging, profiling R code, and organizing and commenting on R code. Topics in statistical data analysis will provide working examples.

Related path/track: Introduction to Probability and Data with R (Duke University)

Platform: DataCamp

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.

Related path/track: Intermediate R, Exploratory Data Analysis in R

Platform: Edureka

Description: Edurekas Data Science Training lets you gain expertise in machine learning algorithms like K-Means Clustering, Decision Trees, Random Forest, and Naive Bayes using R. Data science training encompasses a conceptual understanding of Statistics, Time Series, Text Mining and an introduction to Deep Learning. Throughout this Data Science Course, you will implement real-life use-cases on Media, Healthcare, Social Media, Aviation, and HR.

Related path/track: Data Analytics with R Certification Training, Advanced Predictive Modelling in R Certification Training

Platform: edX

Description: The first in edXs Professional Certificate Program in Data Science, this course will introduce you to the basics of R programming. You can better retain R when you learn it to solve a specific problem, so youll use a real-world dataset about crime in the United States. You will learn the R skills needed to answer essential questions about differences in crime across the different states. The demand for skilled data science practitioners is rapidly growing, and this series prepares you to tackle real-world data analysis challenges.

Related path/track: Statistics and R

Platform: Experfy

Description: This R course is step-by-step. In every new tutorial, students build on what they had already learned and move one extra step forward. After every video, you learn a new valuable concept that you can apply right away. And the best part is that you learn through live examples. This training is packed with real-life analytical challenges which you will learn to solve. In summary, this course has been designed for all skill levels and even if you have no programming or statistical background you will be successful in this course.

Related path/track: Data Wrangling in R, Data Science Masterclass with R, Probability and Statistics for Data Science with R

Platform: Intellipaat

Description: This data scientist course lets you master skills, such as data analytics, R programming, statistical computing, machine learning algorithms, k-means clustering, and more. It includes multiple hands-on exercises and project work in the domains of banking, finance, entertainment, etc. Intellipaats online data science courses are well recognized across 500+ employers helping you to land your dream job.

Platform: LinkedIn Learning

Description: Learn the basics of R and get started finding insights from your own data, in this course with professor and data scientist Barton Poulson. The lessons explain how to get started with R, including installing R, RStudio, and code packages that extend Rs power. You also see first-hand how to use R and RStudio for beginner-level data modeling, visualization, and statistical analysis. By the end of the course, youll have a thorough introduction to the power and flexibility of R, and understand how to leverage this tool to explore and analyze a wide variety of data.

Related path/track: R for Data Science: Lunchbreak Lessons, R Programming in Data Science: Setup and Start, R for Excel Users, Data Wrangling in R

Platform: Pluralsight

Description: In this course, Programming with R, you will learn how to manipulate different objects. First, you will learn the basic syntax. Next, you will explore data types and data structures available in R. Finally, you will discover how to write your own functions by implementing control flow statements. When you are finished with this course, you will have a foundational knowledge of R programming that will help you as you move forward to data science.

Related path/track: Data Science with R, Understanding Machine Learning with R

Platform: Simplilearn

Description: The Data Science with R programming certification training covers data exploration, data visualization, predictive analytics, and descriptive analytics techniques with the R language. You will learn about R packages, how to import and export data in R, data structures in R, various statistical concepts, cluster analysis, and forecasting. This module enables several different training options.

Platform: Udacity

Description: Learn the programming fundamentals required for a career in data science. By the end of the program, you will be able to use R, SQL, Command Line, and Git. There are no prerequisites for this program, aside from basic computer skills. With real world projects and immersive content built in partnership with top tier companies, youll master the tech skills companies want.

Platform: Udemy

Description: This course is truly step-by-step. In every new tutorial, instructors build on what students had already learned and move one extra step forward. After every video, you learn a new valuable concept that you can apply right away. And the best part is that you learn through live examples. This training is packed with real-life analytical challenges which you will learn to solve. In summary, this course has been designed for all skill levels and even if you have no programming or statistical background you will be successful in this course.

Related path/track: Data Science and Machine Learning Bootcamp with R, R Programming: Advanced Analytics In R For Data Science

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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O’Reilly Analysis Unveils Python’s Growing Demand as Searches for Data Science, Cloud, and ITOps Topics Accelerate – Business Wire

January 31, 2021 | Author: admin

BOSTON--(BUSINESS WIRE)--OReilly, the premier source for insight-driven learning on technology and business, today announced the findings of its annual platform analysis, identifying the most-searched technology content from OReilly online learning. Each year, OReilly gathers usage data across OReilly online learning, publishing partners and learning modes, live online training courses, and virtual events to provide technology leaders with an overview of key trends, tools, and topics to watch and adopt within their own businesses.

Of note, the research found Python continues to be the most popular programming language to learn, building on the growing demand from the previous year by 27%. This is a significant increase for a language that was already topping the list. While scikit-learn, Pythons machine learning (ML) library, remains a front-runner in usage with 11% year-over-year growth, the popularity of PyTorch, an ML framework used for deep learning, increased by a staggering 159%.

Additional findings from the analysis include:

At the rate new technology emerges, its important to identify the trends and tools that really impact learning among the tech practitioners themselves, said Mike Loukides, vice president of emerging technology content at OReilly. Analyzing yearly trends in technology usage while keeping tabs on whats gaining traction helps our community stay on track to remain current and competitive in the real world. What were seeing within programming languages, AI/ML, data science, IT operations, and security provides a forecast on the systems and tools that will fuel innovation in 2021 and beyond.

For the full platform analysis and data, please visit: https://www.oreilly.com/radar/where-programming-ops-ai-and-the-cloud-are-headed-in-2021/.

About OReillyFor 40 years, OReilly has provided technology and business training, knowledge, and insight to help companies succeed. Our unique network of experts and innovators share their knowledge and expertise through the companys SaaS-based training and learning solution, OReilly online learning. OReilly delivers highly topical and comprehensive technology and business learning solutions to millions of users across enterprise, consumer, and university channels. For more information, visit http://www.oreilly.com.

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Here’s how much Amazon pays its Boston-based employees – Business Insider

January 31, 2021 | Author: admin

Amazon's presence in Boston is growing. The firm announced Tuesday that it would add 3,000 jobs in the Boston area, including human resources, artificial intelligence, and software development roles.

The firm already has a large presence in the Boston area it has at least 3,700 employees at its existing Boston Tech Hub. The firm leased an additional 17-story building in the city.

Read more: Amazon exec Jay Carney pens letter in support of $15 minimum wage increase

When a US-based firm hires a foreign worker, they have to file a visa application with the US Office of Foreign Labor Certification. The applications for these visas are published online. Insider analyzed more than 200 of Amazon's visa applications for Boston-based employees from 2019 and 2020 to understand how Amazon pays employees.

It's important to note that the visa application data only reflects base salaries, and does not include bonuses, incentive awards, or benefits that would typically be part of a total rewards package.

Let's take a look at job families in Amazon's Boston offices, and how much you could make.

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Datavant and Kythera Increase the Value Of Healthcare Data Through Expanded Data Science Platform Partnership – GlobeNewswire

January 31, 2021 | Author: admin

SAN FRANCISCO and FRANKLIN, Tenn., Jan. 26, 2021 (GLOBE NEWSWIRE) -- Datavant, the leader in helping healthcare organizations safely connect their data, and Kythera, a healthcare cloud- based data science platform company with data representing over 310 million US patients, announced an expanded partnership to serve healthcare businesses.

Healthcare information users face constant challenges when evaluating and integrating data. Utilizing Datavants privacy-protecting patient-level linking technology through Kytheras Wayfinder platform-as-a-service, users are able to extract maximum value from data investments by securely evaluating and integrating data across a wide variety of sources. Kytheras Wayfinder enables any data to be matched and analyzed, including claims, electronic health records, digital health information, payer inputs, lab and imaging records, and consumer records. By applying market-proven machine learning principles, Wayfinder also includes standardized provider and payer directories to align and integrate data at greater velocity, with greater accuracy, and at lower cost - unlocking the value of matched information sets faster and less expensively at scale.

Members of the Datavant ecosystem will be able to leverage Kytheras Wayfinder platform to efficiently evaluate data assets from partners and other sources by exploring and understanding complementary data sets available through Datavant. Wayfinder enables immediate consumption and integration, leading to more accurate and actionable insights for life sciences companies, health systems, public health entities, and other non-health care organizations that utilize health care data to improve their understanding of markets and customers.

Datavant ecosystem members can also take advantage of Wayfinder to quickly deploy their data assets as a product with enterprise grade speed, scale, and security. This benefit enables the monetization of otherwise underutilized data assets, further extending the utility and value of Datavants linking technology and partner ecosystem.

Datavant and Kythera have witnessed companies that link and combine healthcare data achieve a critical step in outperforming their competition by advancing the use of information to improve healthcare, said Travis May, Chief Executive Officer of Datavant. We are excited to deepen our relationship with Kythera and to support our shared goal ensuring life sciences manufacturers, health systems, or any organization using health care data, have access to complete and accurate information and insights.

Data consumers are looking for the best available information to support their decisions. The greatest challenges are ensuring data is complete and high quality. Our partnership with Datavant enables us to serve customers by addressing both of these challenges, said Jeff McDonald, CEO at Kythera. This partnership helps healthcare organizations have the confidence to make decisions that improve patient care, create better product-market fit, and better understand customers.

About DatavantDatavants mission is to connect the worlds health data to improve patient outcomes. Datavant works to reduce the friction of data sharing across the healthcare industry by building technology that protects the privacy of patients while supporting the linkage of de-identified patient records across datasets. Datavant is headquartered in San Francisco. Learn more about Datavant at http://www.datavant.com.

About KytheraKythera is a healthcare data science platform company that maximizes data investments by applying machine learning to improve quality, integration, and decision making through advanced machine learning. When our cloud-based technology is combined with our data assets representing more than 310 million US patients, our customers realize increased data fidelity, access more accurate insights, improve decision making, and make smarter data investments. Learn more about Kythera at http://www.kytheralabs.com.

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Data, AI and babies – BusinessLine

January 31, 2021 | Author: admin

Researchers at IIT Madrass Initiative for Biological Systems Engineering (IBSE) are poring over millions of data points to see why babies are delivered prematurely in India. They want to develop models that will predict the possibility of preterm births and help pregnant women guard against such deliveries. The IBSE is an interdisciplinary group using data science to solve biological problems with machine learning.

Professors Himanshu Sinha, quantitative geneticist, and Karthik Raman and Raghunathan Rengaswamy, chemical engineers, have the basic raw material for their research oodles of data. The Translational Health Science and Technology Institute (THSTI), a government clinical research institute of the Department of Biotechnology, under a programme called Garbh-Ini a pregnancy cohort to study preterm birth in India led by Dr Shinjini Bhatnagar has gathered from the Gurugram Civil Hospital since 2015 a mind-boggling 1,300 parameters for each of the 8,000 pregnant women surveyed.

Some of these are microbiome data collected from saliva, feces, vagina; some, information ultrasound scans, and some more on clinical parameters such as blood samples, temperature and blood pressure. Other pieces of data relate to socio-economic factors income levels, number of rooms in the house, the type of cooking stoves used (for possible smoke effects) and so on.

Now, using machine learning, the researchers will develop a model that will show, early during a pregnancy, if a woman runs the risk of a preterm delivery.

India is the preterm delivery capital of the world. Thirteen per cent of the deliveries in India are preterm, which works out to a quarter of all preterm deliveries in the world. Half of the babies delivered early in India dont survive beyond five months. (Preterm is before 37 weeks, while normal term is 40 weeks.) Obviously, this situation needs correction.

Sinha and Raman told Quantum that they got really clean data all numbers checked, outliers verified and properly formatted for machine learning. But some challenges popped up.

One was class imbalance, a common problem in machine learning. The algorithm will learn more from the majority class in the sample and less from the minority, explains Sinha. In this case, the majority of the pregnant women are normal term; only about 13 per cent are preterm. If this is not factored in there are many techniques to do that the predictions will be less accurate. The factors that cause preterm would be not learnt, Sinha says.

Non-linear effects posed another challenge. Simply put, the effect of something the pregnant woman does in the first three months may pop up, not in the second three months, but much later in the pregnancy. It is easier to predict linear effects than non-linear ones.

The researchers have analysed the data of the first three months and developed the first India-specific model to date the pregnancy in the first trimester. Data pertaining to the next two trimesters is being processed right now.

In the end, the doctors will have tools to know if there is a high likelihood of a preterm birth which will point to the need for corrective measures. The outcome will be healthy babies and happy mothers.

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