Members of the Siliconrepublic.com community highlight some of the in-demand skills for data professionals right now.

There are many reasons someone might choose to work in data. Theres huge variety in the field, with roles spanning analytics, mining, compliance and more. And, according to Glassdoor, analysts can typically earn around 35,000 in Ireland while senior data scientists can earn up to 73,000 each year.

But what are employers in the industry looking for right now? We asked some people in the Siliconrepublic.com community to share their thoughts on the skills in high demand.

Coding is an essential part of any data job, but there are hundreds of programming languages to choose from.

Python is one of the most popular languages to learn. At Aon, employees draw on Python to help companies mitigate against possible risks and inform future decisions. According to Fergal Collins, CEO of the Aon Centre for Innovation and Analytics in Dublin, Python is a key skill when it comes to delivering impactful analytics from data.

Its accessibility and ease of use, which facilitates rapid exploration of data to deliver analytics that solve client problems, is what makes it stand out from the crowd, Collins said. Python is very versatile and flexible, lending itself not only to data analytics but to web development and the ability to deploy code to many environments from local to cloud.

The active and supportive community driving solutions and research in Python goes a long way to making Python my teams go-to choice of skill when it comes to working effectively with data.

Other tools to think about include TensorFlow, Keras and Spacy, which Brian OHalloran used in his data science role at Liberty IT.

Hays global head of technology, James Milligan, recently highlighted to us the top data science skills for the post-Covid world, including the ongoing demand for data visualisation skills. Visualising data involves taking the numbers, extracting trends from them such as potential opportunities and risks and presenting this information in an easily digestible way to stakeholders, he said.

Mark Greville, vice-president of architecture at Workhuman, agrees. Organisations will need to embrace interactive data-visualisation tools in the future, he told us. We will be rethinking how we help our humans engage with the amazing insights, so these will become the norm.

Being able to interact with the data in new and visually appealing ways will add another level to whats possible.

Milligan also emphasised the importance of data governance skills. He said that in order to bring value to an organisation, data should have integrity and insight.

To assess this, data professionals must investigate where the data came from, how clean it is and how it has been analysed. Getting answers to these questions requires a worker to critique the terminology that has been used, how information has been tagged and whether this can be interpreted with integrity, Milligan said. If something isnt correct, someone needs to spot this and rectify it immediately.

Equally important are the security measures you take with the data youre handling. As OffSecs Ning Wang recently told us: Doing security well is not just following the process and going through the motions; it requires people to be able to think critically and creatively.

Communication is important for almost every role and the data sector is no exception. Whether youre a data steward like Dun & Bradstreets Deirdre Linnane or a senior data scientist like Fidelity Investments Sean Curran, effective communication is key.

When Linnane first began in her role, she was surprised to learn how frequently shed be interacting with clients. Her background in customer-facing roles helped her adjust, but shes still careful about using too much jargon in her conversations.

For Curran, storytelling skills have proved invaluable to his work in data. I use my technical skills on a daily basis across R, Python, SQL, maths and stats, he told us. However, I think the best skill you can learn as a data scientist is the ability to tell a story of how to approach business problems and how to come up with a solution that the client understands and trusts.

There are other data roles that place even greater focus on the ability to communicate. As a data science evangelist, Rosaria Silipos goal is to help others engage with data science more deeply. She leads public presentations, lectures and courses and writes blog posts, articles and books on the topic.

Working in data isnt all about numbers. In fact, many professionals list creativity as one of the top skills they draw on in their data roles. And for Silipo, its one of her favourite things about working in the industry.

Setting out to solve a problem in data science is a highly creative process, she told us. In this phase there is nothing wrong; you can let your creativity run free, experiment.

Creativity isnt simply a nice-to-have in todays working world; its a critical skill. As Hays Karen Young highlighted in this guest article, roles like chief innovation officer have emerged in recent years. US consulting company West Monroe is even carving out space for all sorts of creative leadership, from chief coffee officer to chief hot sauce officer.

But whether youre a leader or not, the importance of bringing your creative flair to the world of data cant be overstated. Human creativity is immune to automation, Young said.

If youre interested in data, chances are youre a curious person by nature. Many data jobs share a fundamental characteristic; their goal is to arrive at answers by solving the problems presented by large amounts of information.

Karl Heery of Aon believes curiosity is crucial for data workers; its something he looks out for in new hires.

So if youre determined to work in data, take Silipos words of wisdom with you: Keep being curious and keep learning about new technology, new algorithms, new solutions, the work of others.

Our field is constantly changing; new uses and new techniques pop up every day. Keeping the interest and the curiosity high means to keep up with the technological evolution.

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Want to work in data? Here are 6 skills you'll need Just now - Siliconrepublic.com

BANGALORE, India, Jan. 29, 2021 /PRNewswire/ -- Praxis Business School is launching its Post Graduate Program in Data Engineering, in association with knowledge partners Genpact and LatentView Analytics, to cater to the galloping demand for industry-ready data engineering resources. This 6-months full-time industry endorsed PG program will be delivered from the Praxis Bangalore campus and aims at providing a comprehensive and immersive learning experience and placement assistance for a lift-off into a successful and recession-proof career.

Data Engineering has emerged as a top career in today's data driven world. The Data Engineering market size in India is expected to grow 4 times in the next 5 years to reach $ 42.3 billion by 2025 a whopping 41% of the Global Data Engineering services market. According to the DICE report, Data Engineering was the fastest growing career in 2019 at 45% growth.

The program at Praxis is aimed at addressing this industry requirement, globally and in India. For career seekers, data engineering offers immense opportunities as demand far outstrips supply. It's an exciting, new-age career - data engineers manage the data of large enterprises - they construct and oversee database architecture; they determine how data is collected and stored; they create the pipelines that transform raw data into useful formats for analysis. For doing all this, they need to have a range of skill-sets from conceptual clarity to hands-on experience with tools, deep understanding of Big Data and Cloud technologies, and an appreciation of business problems and analytics.

Prof. Charanpreet Singh,Founder and Director of Praxis Business Foundation,said, "Praxis has been a leader in creating industry-ready resources for Data Science over the last decade. With the incredible increase in the generation of data, the speed of communication and the formulation of new tools and algorithms for analytics, data engineering roles have evolved tremendously over this period. The supply has just not been able to keep pace with the demand; this is why Praxis has again taken the lead to create the nation's first full-time post graduate program in data engineering. We are fortunate to have Genpact and LatentView, with their enormous experience in this field, to support and guide us. This is a great time to be a data engineer, and the Praxis program will ensure that you get all the knowledge and skills you need to become one."

"At Genpact, our work with leading enterprises around the world, has taught us that laying a good data foundation requires a few key components, and talent is one of the most critical factors," saidSidharth Reddy, Data and Advanced Analytics Leader, Banking and Capital Markets, Genpact."Through this partnership, we aim at nurturing a new generation of students who can apply their theoretical knowledge on data engineering, big data management, data governance, data cognition engines and more, to solve real world business challenges."

According toMr. Ramesh Hariharan, Co-founder and Director of Data Engineering at LatentView Analytics,"In the emerging era, AI will simplify any decision-making. Engineering the right type of data platforms would be a key differentiator! Leading scalable data engineering initiatives will be critical for successful analytics transformation. Praxis is best positioned to offer the best of both worlds (data science & data engineering) and provide the right curriculum to be future ready!"

Program Highlights:

Praxis offers a comprehensive and contemporary curriculum co-developed by Genpact and LatentView Analytics and delivered by domain leaders and senior practitioners. It comprises modules on understanding the legacy tools and technologies for Data Management & Data Modelling as well as on the paradigm of Distributed Systems and Cloud Computing. Participants will get hands-on exposure on ETL Concepts, Unix/ Linux, Python, Streaming Analytics on Hadoop, MapReduce, SQL & NoSQL using Devops. A highlight of the program is the Capstone project that sees them taking data from a Legacy system and migrating it to a big-data platform hosted on the Cloud. The renowned Praxis Placement Program manages the transition of the students to a successful Data Engineering career.

Prof. Dr. Sourav Saha, Dean - Academics at Praxis Business School said "The digital era has created a new benchmark for successful Organization whose reputation today is determined not only on revenue but also on the amount of Data they handle and process. This is where the Data Engineers take the centre stage with the knowledge of the Cloud Computing platforms, tools and techniques for ensuring the assimilation, storage and processing of Big Data. Today, as the human civilization prepares at creating unprecedented value through Artificial Intelligence (AI), the Data Engineers ensure the continuous fuel for AI, i.e. Data. I am excited at the career potential of the Data Engineering graduates coming out of the program and look forward to seeing them contributing to a better future for everyone."

Who can join this program:People who understand tech and like working in tech; enjoy solving problems; are analytical in their approach; like working in collaborative teams; and thrive in positions of responsibility. Engineering, Science, BCA/ MCA graduates will find this an exciting area to work in.

About Praxis Business School

Praxis Business School is a premier institution with campuses in Kolkata and Bangalore. The name Praxis symbolizes the philosophy of the institute. The root of Praxis is Greek, meaning 'to do', or the practice of an art, science, or technical occupation.Praxis is committed to playing a significant role in creating a strong pool of resources who understand the interplay between data, technology and business. Praxis offers AICTE approved 2-years full-time Post Graduate Diploma in Management, full-time Post-Graduate Program in Data Science, full-time Post-Graduate Program in Cyber Security and full-time Post Graduate Program in Data Engineering.

Media Contact:Sushanta Saha Head of marketing[emailprotected] +91- 9035003073

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Praxis Business School is launching its Post Graduate Program in Data Engineering in association with Knowledge Partners - Genpact and LatentView...

The Global Data Science Platform Market Research Report 2020-2025 is a valuable source of insightful data for business strategists. It provides the industry overview with growth analysis and historical & futuristic cost, revenue, demand, and supply data (as applicable). The research analysts provide an elaborate description of the value chain and its distributor analysis. This Market study provides comprehensive data that enhances the understanding, scope, and application of this report.

Top Companies in the Global Data Science Platform Market: Microsoft Corporation?, IBM Corporation?, Google?, Wolfram?, DataRobot?, Sense?, RapidMiner?, Domino Data Lab?, Dataiku?, Alteryx?, Continuum Analytics AndOther

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Data Science Platform Market Insights, Industry Outlook, Growing Trends and Demands 2020 to 2025 The Courier - The Courier

The original version of this article appeared in Toward Data Science.

When I started teaching data science and artificial intelligence in Duke Universitys Pratt School of Engineering, I was frustrated by how little insight I actually felt I had into how effective my teaching was, until the end-of-semester final exam grades and student assessments came in.

Being new to teaching, I spent time reading up on pedagogical best practices and how methods like mastery learning and one-on-one personalized guidance could drastically improve student outcomes. Yet even with my relatively small class sizes I did not feel I had enough insight into each individual students learning to provide useful personalized guidance to them. In the middle of the semester, if you had asked me to tell you exactly what a specific student had mastered from the class to date and where he or she was struggling, I would not have been able to give you a very good answer. When students came to me for one-on-one coaching, I had to ask them where they needed help and hope that they were self-aware enough to know.

Knowing that my colleagues in other programs and universities teach much larger class sizes than mine, I asked them how aware they felt they were of each of their students level of mastery at any point in time. For the most part, they admitted they were also largely flying blind until final assessment results came in. It is historically one of the most vexing problems in education that there is a tradeoff between scale and achievable quality of instruction: As class sizes grow larger, the ability of a teacher to provide the type of personalized guidance shown by learning science research to be most effective is diminished.

Yet as instructors in the new world of online education, we have access to ever-increasing amounts of datafrom recorded lecture videos, electronically submitted homework, discussion forums, and online quizzes and assessmentsthat may give us insights into individual student learning. In summer 2020, we began a research project at Duke to explore how we could use this data to help us as instructors do our job better. The specific question we set out to answer was: As an instructor, how can I use the data available to me to support my ability to provide effective personalized guidance to my students?

What we wanted to know was, for any given student in a class at any point during a semester, what material have they mastered and what are they struggling with? The model of Knowledge Space Theory, introduced by Doignon and Falmagne in 1985 and significantly expanded on since, posits that a given domain of knowledge (such as the subject of a course) contains a discrete set of topics (or items) that often have interdependencies. The set of topics that a student has mastered to date is called their knowledge state. In order to provide effective instruction for the whole class and to provide personalized guidance for individual students, understanding the knowledge state of each student at any point is critical.

So how does one identify a students knowledge state? The most common method is through assessmenteither via homework or quizzes and tests. For my classes, I use low-stakes formative quiz assessments each week. Each quiz contains around 10 questions, with roughly half of the questions evaluating student knowledge of topics covered in last weeks lecture, and the remaining half covering topics from earlier in the course. In this way, I continue to evaluate students mastery of topics from the whole course each week. In addition we have weekly homework, which tests a variety of topics covered to date.

But digging through dozens or hundreds of quiz or homework question results for tens or hundreds of students in a class to identify patterns that provide insight on the students knowledge states is not the easiest task. Effective teachers need to be good at a lot of thingsdelivering compelling lectures, creating and grading homework and assessments, etc.but most teachers are not also trained data scientists, nor should they have to be to do their jobs.

This is where machine learning comes in. Fundamentally, machine learning is used to recognize patterns in data, and in this case the technology can be used to identify students knowledge states from their performance patterns across quizzes and homework.

To help improve my own teaching and that of my fellow faculty members in Dukes AI for Product Innovation masters program, we set out to develop a system that could, given a set of class quiz and homework results and a set of learning topics, identify each students learning state at any time and present that information to both instructor and learner. This would facilitate more effective personalized guidance by the instructor and better awareness on the part of the student as to where they need to put additional focus in their study. Additionally, by aggregating this information across the class, an instructor could gain insight into where the class was successfully learning the material and where he or she needs to reinforce certain topics.

The project culminated in the creation of a prototype tool called the Intelligent Classroom Assistant. The tool reads instructor-provided class quiz or homework results and the set of learning topics covered so far in the course. It then analyzes the data using a machine learning algorithm and provides the instructor with three automated analyses about: quiz and homework topics with which the class has struggled; learning topics the class has and has not mastered; and the performance of each student.

One of the key challenges in developing the tool was the mapping of quiz and homework questions to the most relevant learning topic. To accomplish this, I developed a custom algorithm that uses natural-language processing and draws on open-source libraries to understand the context of each question and map it to the primary learning topic it was designed to evaluate.

The Intelligent Classroom Assistant tool was built while I taught the Sourcing Data for Analytics course at Duke, an introductory-level data science course for graduate engineering students that covered technical as well as regulatory and ethical topics. This gave me an opportunity to try out the tool on my class as the semester progressed.

One of the key things I wanted to evaluate was how well the algorithm behind the hood of the tool could classify each quiz or homework question into the most relevant of the 20 learning topics covered in the course. On the full set of 85 quiz questions I used during the semester, the algorithm identified the relevant learning topic correctly about 82 percent of the time. While not perfect, this was good enough to make the analyses provided by the tool useful to me.

During the course, I used the prototype in two main ways to inform my teaching. I spent extra time in lecture sessions covering learning topics and specific quiz questions that the tool flagged due to low student performance. And during one-on-one help sessions with students, I used the personalized student analysis module of the tool to understand where the student needed extra reinforcement and make tutoring sessions more focused.

It's too soon to quantify whether the tool changed student outcomes, because the course I used it in was new, which means there is no historical benchmark for comparison. But this year, we are expanding the tool's use and are working to evaluate the effects it has on student engagement and performance. We are trying it out in another engineering class of 25 and also in an undergraduate finance class of more than 200 students. I also plan to use the prototype in my spring machine learning class to guide my teaching through the semester. Since students can benefit from seeing the results of the tools analysis as much as instructors, for spring we hope to include the addition of a student portal allowing students to see their own results and providing personalized study recommendations to students based on their identified knowledge state.

The amount of electronic data now available to instructors can help support their teaching. But teachers are not (usually) data scientists themselves, and need analytics tools to help them extract value from the data. While such tools are helpful, however, their value is directly proportional to how well an instructor defines course learning objectives and structures material and assessments to support and evaluate those objectives.

Machine learning tools such as The Intelligent Classroom Assistant can not only help teachers to improve the quality of their classes (as measured by student learning outcomes), but also enable them to do so at increased scale, offering the promise of widespread personalized teaching. When teachers can teach more effectively, learners can learn more, and as a society we all reap the benefits.

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How Professors Can Use AI to Improve Their Teaching In Real Time - EdSurge