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To develop a successful career in data science, you need to strengthen what I consider to be the six main pillars of the area: technical skills, building a portfolio, networking, soft skills, and finally developing a niche specialty. Once you have all that, you also need to perform well at the interview stage.

Too many would-be data scientists think its all about the skills, and neglect the network. Or you rely on a network contact to get you the job interview, but stumble under the pressure, and dont do your skills justice.

None of these sections are really optional, but this is probably the most important one of the six. You might stumble into a job if you dont know the right people, or if your portfolio isnt perfect, but if you dont have the right skills, you wont get the job. Or worse: you might get the job, but youll crash and burn. And get fired.

Heres what you should focus on:

Every data science job requires a strong foundation in mathematics, statistics, and programming. Proficiency in languages like Python or R is essential. Almost every data science job description will mention one of those two languages.

I also suggest you consider learning SQL as a fundamental requirement. SQL databases are a reality of life for data scientists. And its a comparatively simple language to learn.

Its not just the recent rise of AI; data scientists have always needed mastery of machine learning. You will need to gain expertise in machine learning algorithms, data preprocessing, feature engineering, and model evaluation.

A data scientists findings are worthless unless she can communicate them to another. This is done with graphs, charts, and other types of data viz. Youll need to master data visualization tools and techniques to effectively communicate insights from data with key stakeholders at your company.

Ill get into this a little more when I talk about the soft skills, too communication is a vital skill.

Gone are the days when data scientists dealt with little data, if they ever existed. Today, youll need to be extremely familiar with big data and the requisite tools. Even if your company doesnt handle truly big data, theyll aspire to it.

Familiarize yourself with tools like Hadoop, Spark, and cloud platforms for handling large datasets.

Onto pillar two: your portfolio.

Theres a dearth of qualified data scientists, as you probably know. Bootcamp grads rose to fill the gap. That caused a new problem: lack of trust. See, companies know a degree isnt necessarily a needed qualification to do a good job. However, bad bootcamps also gave aspiring data scientists a bad rap, because many boot camps churned out graduates that didnt know a join from a subquery. Hence, your personal portfolio is a chance for you to prove you know your stuff. (Its also worth noting that boot camps are very expensive, especially compared to the slightly less optimistic job outlook currently.)

Heres what you need:

Work on personal projects that showcase your skills. These could be Kaggle competitions, open-source contributions, or your own data analysis projects. You can maintain a well-organized GitHub repository to showcase your projects, code samples, and contributions.

Consider creating a blog or personal website where you can share insights, tutorials, and case studies related to data science. Its possible to cheat this system and hire someone to do it for you, but its so expensive and time-consuming that few people try to falsify it. A blog serves as a great portfolio of your knowledge.

Be ready to explain your projects, methodologies, and problem-solving approaches. Brush up on common data science interview questions and coding challenges.

Remember the golden rule of jobs, no matter the field: potentially as many as 70% of job listings are never advertised. This is an old stat, but even if its 20 to 30 percent, it proves that who you know matters. Thats not even considering that as many as a third of job openings posted are actually fake, designed to make a company look more successful than it is. A personal network can help you avoid wasting your time.

Heres what you should do:

Join data science communities, and attend meetups, conferences, and webinars to connect with other professionals in the field. This more formal approach to a network can help you meet the right folks, make a splash in your industry, and stay up to date with current events.

More informally, you should also engage on platforms like LinkedIn, Twitter, and relevant forums to share your work, and insights, and learn from others.

Remember, hard skills are only half the battle. Thats why you need to ensure that your soft skills arent neglected. Im not saying soft skills are more important. Hard skills vs soft skills is a false dichotomy theyre both important. But people dont hire data science machines, they hire people. Here are the areas I recommend focusing on:

Remember that data viz skill? Data scientists need to effectively communicate complex technical findings to non-technical stakeholders. Its amazing how much of a data scientists job comes down to explaining why someone in marketing should understand the pretty graph.

Its almost a meaningless buzzword at this point, so make sure you actually understand what problem-solving really means. In the context of data science, solving problems isnt just debugging. Its also knowing when it makes sense to collaborate with different departments, when to rejig a projects tech stack to meet new specs, or going back over your model if it stumbles on the test dataset.

Another almost-buzzword that merits deeper consideration. Critical thinking means the ability to analyze data from multiple angles, question assumptions, and think creatively to derive meaningful insights.

Data scientists dont work in a vacuum. Youll work with web developers, data analysts, business analysts, marketers, salespeople, and CXOs. Collaborate with cross-functional teams to understand business needs and align data-driven solutions.

Havent you heard? Were in the middle of a tech winter for hiring. Venture capital money isnt flowing like it used to, and companies are tightening their belts. Its not a good time to be a generalist. Youll need to specialize to survive.

Data science spans various industries, such as healthcare, finance, e-commerce, and more. Specializing in a particular domain can make you more attractive to employers in that field. Look for what youre naturally interested in, or where you might already have extra knowledge.

Acquire domain-specific knowledge relevant to the industry you want to work in. This helps you understand the nuances of the data and make more informed decisions. For example, if you want to work at Google, youll need to know the intricacies of search algorithms and user behavior.

Last, but certainly not least: prepare for interviews. You can nail the first five pillars and still stumble at the finish line. Heres how I recommend you prepare:

You can know a concept without really being able to explain it to others. For the interviews, you will have to be ready to explain your projects, methodologies, and problem-solving approaches.

Take the time to ensure you not only have a complete understanding of what you did, why you did it, and why it works for all your projects but that youre able to explain it well enough that a layperson could understand. (this is also a great way of practicing that communication soft skill.)

The whiteboard is a famous pillar of coding interviews, yet so many people panic when faced with that blank, white surface. The more you practice interview questions ahead of time, the better youll perform under pressure on the day.

Its a little presumptuous to even pretend theres a single right answer here, or that it could be explained in an article. Hopefully, this blog post acts more like a roadmap than a comprehensive solution. Practice these six pillars of data science jobs, and youll be well on your way to developing a career in data science to last as long as you want.

Nate Rosidi is a data scientist and in product strategy. He's also an adjunct professor teaching analytics, and is the founder of StrataScratch, a platform helping data scientists prepare for their interviews with real interview questions from top companies. Connect with him on Twitter: StrataScratch or LinkedIn.

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Mastering the Data Universe: Key Steps to a Thriving Data Science ... - KDnuggets

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We have 11 divisions and offices whose work spans the spectrum of translational science. Together, they identify and devise plans for new opportunities, build tools and technologies, support research here and beyond, train scientists, and so much more.

The Office of the Director develops and provides leadership for the centers translational science programs, manages and directs executive-level activities, and provides policy guidance and coordination for each of the centers components.

The Division of Clinical Innovation (DCI) plans, conducts and supports research across the clinical phases of the translational science spectrum. DCI plans, conducts and supports research to develop new methods and technologies to enhance clinical processes, as well as to evaluate existing and developing approaches, technologies, and processes in the clinical spectrum. It also supports training programs relevant to clinical phases of translational science and allocates resources to clinical and translational infrastructure and investigators.

The Division of Extramural Activities advises NCATS leadership on issues related to policy and procedures for extramural activities. It works closely with other parts of NIH to develop, coordinate and implement new extramural policies for research grants and contracts. Its Office of the Director manages the membership and operations of the NCATS Advisory Council and Cures Acceleration Network Review Board, and it oversees and coordinates prize competitions and all aspects of initiative management. It also coordinates the receipt and referral of grant/advisory/council applications, among other activities.

The Division of Preclinical Innovation (DPI) plans and conducts collaborative research projects across the preclinical phases of the translational science spectrum, using both internal and contract resources to advance them. DPI plans, conducts and collaborates on research to develop new methods and technologies to enhance preclinical processes, as well as to evaluate existing and developing approaches, technologies, and processes in the preclinical spectrum. It also supports training programs relevant to preclinical phases of translational science and allocates DPI resources to preclinical extramural and intramural investigators.

The Division of Rare Diseases Research Innovation facilitates and coordinates many of our rare disease programs as well as NIH-wide activities involving research for a broad array of rare diseases. DRDRI also develops and manages rare disease information resources for patients and patient advocacy organizations.

The Office of Administrative Management directs the administrative, information technology and financial operations management of the center. It also oversees personnel management and workforce planning.

The Office of Drug Development Partnership Programs (ODDPP) promotes innovations that improve the efficiency of drug development from target identification including expanding the target landscape through early-stage clinical trials. Programs administered by ODDPP often involve partnerships with the private sector, other parts of NIH or the U.S. Government, and NCATS own intramural scientists. The ODDPP also works with national and international stakeholders, providing leadership for the national response to public health emergencies.

The Office of Policy, Communications and Education develops and communicates critical priorities for NCATS in a highly collaborative manner.

The Office of Special Initiatives (OSI) addresses translational problems with innovative solutions through the development and implementation of disruptive technologies using interdisciplinary approaches and novel public-private partnerships. The programs and initiatives within OSI are intended to be catalytic and transformative, resulting in a paradigm shift in the field. The OSI also takes the lead on NIH-wide research activities that are supported through the NIH Common Fund.

The Office of Strategic Alliances establishes and advances public-private partnerships, as well as develops innovative approaches, policies and methods to reduce, remove or bypass bottlenecks in translational science collaborations.

The Office of Translational Medicine (OTM) uses expertise across clinical and other relevant disciplines to amplify NCATS ability to foster innovative translational science and improve health. The OTM interprets and implements research-related policies, convenes stakeholders, and, in certain circumstances, arbitrates or makes decisions. Topics of interest include human subjects; diversity, equity, inclusion and accessibility; regulation and drug development; clinical research, including clinical trials and clinical epidemiology; and ethics, for which OTM operates a grant program.

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Divisions and Offices - NCBI

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Why do some people have a more severe course of COVID-19 disease than others? A genome sequence database created by an international collaboration of researchers, including many from the University of Toronto and partner hospitals,may hold the answers to this question and many more.

The origins of the Canadian COVID-19 Human Host Genome Sequencing Databank, known asCGEn HostSeq, can be traced to the earliest days of the pandemic.

Lisa Strug,senior scientist at The Hospital for Sick Children (SickKids) and academic director of U of Ts Data Sciences Institute, one of several U of T institutional strategic initiatives, says genetic data was top of mind for her and other researchers inlate 2019 and early 2020 as reports of a novel form of coronavirus emerged from China and then other locations across the globe.

In my research, I use data science techniques to map the genes responsible for complex traits, says Strug, who is a professor in U of Ts departments of statistical sciences and computer science in the Faculty of Arts & Science and in the biostatistics division of the Dalla Lana School of Public Health.

We knew that genes were a factor in the severity of previous SARS infections, so it made sense that COVID-19, which is caused by a closely related virus, would have a genetic component, too.

Very early on, I started getting messages from several scientists who wanted to set up different studies that would help us find those genes.

Over the next few months, Strug who is also the associate director of SickKids Centre for Applied Genomics, one of three sites across Canada that form CGEn, Canadas national platform for genome sequencing infrastructure for research collaborated with nearly 100 researchers from across U of T and partner hospitals and institutions, as well as other researchers from across Canada to enrol individuals with COVID-19 and sequence their genomes.

Some of the key team members from the Toronto community included:

The projected was initiated by Scherer and CGEnsNaveed Aziz, along with Strug, and a $20-million grant was secured from Innovation, Science and Economic Development Canada, administered through Genome Canada.

We had to go right to the top to get this project funded fast and our labs and teams worked seven days a week on the project right through the pandemic,Scherer recalls.

Identifying associations between individual genes and complex traits typically requires thousands of genomes both from those with the trait and those without. Though there was no shortage of cases to choose from, it was critical to gather and sequence DNA and then organize the data in a way that would be ethical, efficient and useful to researchers now and in the future.

One of our key mandates at the Data Sciences Institute is developing techniques and programs that ensure that data remains as open, accessible and as re-producible as it can be, Strug says.

That vision was brought to bear as we assembled the data infrastructure for this project for example, ensuring that consent forms were as broad as possible so that this data could be linked with other sources, from electronic medical records to other health databases.

We wanted to be sure that even after the COVID-19 pandemic was over this could be a national whole genome sequencing resource to ask all kinds of questions about health and our genes. The development of the database and its open nature also enabled Canada to collaborate effectively with similar projects in other countries.

In the end,the project gathered more than 11,000 full genome sequences from across Canada, representing patients with a wide range of health outcomes. Those data were then combined with even more sequences from patients in other countries under what came to be called the COVID-19 Host Genetics Initiative.

It didnt take long for patterns to start to emerge. Apaper published inNaturein 2021identified 13 genome-wide significant loci that are associated with SARS-CoV-2 infection or severe manifestations of COVID-19.

Since then, even more data have been added, and subsequent analysis has confirmed the significance of existing loci while also identifying new ones. The most recent update to the project,published inNatureearlier this year, brings the total number of distinct, genome-wide significant loci to 51.

Identification of these loci can help one predict who might be more prone to a severe course of COVID-19 disease, says Strug.

When you identify a trait-associated locus, you can also unravel the mechanism by which this genetic region contributes to COVID-19 disease. This potentially identifies therapeutic targets and approaches that a future drug could be designed around.

While it will take many more years to fully untangle the effects of the different loci that have been identified, Strug says that the database is already showing its worth in other ways.

It can be difficult to find datasets with whole genome sequence and approved for linkage with other health information that are this large, and we want people to know that it is open and available for all kinds of research well beyond COVID through a completely independent data access committee, she says.

For example, several investigators from across Canada have been approved to use these data and weve even provided funding to trainees to encourage them to develop new data science methodologies or ask novel health questions using the CGen HostSeq data.

This was a humongous effort, where researchers from across Canada came together during the COVID-19 pandemic to recruit, obtain and sequence DNA from more than 11,000 Canadians in a systematic, co-operative, aligned way to create a made-in-Canada data resource that will hopefully be useful for years to come. I think that was really miraculous.

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Why is COVID-19 more severe in some people? Researchers use ... - University of Toronto

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By the BF StaffFrom theSeptember / October 2023 Issue

The Data Science and Literacy Act is proposed legislation to increase access to data science education, reduce course equity gaps for all students, and help build Americas 21st century STEM workforce. The bill was introduced in February 2023 by Reps. Haley Stevens, D-Mich.; Don Beyer, D-Va.; Young Kim, R-Calif.; and Jim Baird, R-Ind.

To compete in a 21st-century economy, we need a 21st-century STEM workforce that reflects the diversity that makes the United States the greatest nation on earth, Rep. Stevens said. Ever since Michigans 11th District sent me to Congress, I have been laser-focused on increasing access to STEM education for more young women and low-income communities. The Data Science and Literacy Act is a critical part of that mission. Data touches everything we do. Data education is integral to bolstering our global competitiveness, unlocking good-paying jobs, and fostering a well-informed society. I am proud to introduce this legislation that helps ensure, no matter their background or zip code, that all students are equipped with the necessary tools and knowledge to prepare them for a career in the STEM fields.

We live in a world full of data from the logistics information collected to streamline supply chain operations, to the tracking done by the public health industry to halt the spread of diseases, to the data collected by our smartphones about our everyday lives. As the use of data to optimize operations across industries increases, so does the demand for data literacy in Americas workforce, Rep. Beyer said. Our bill would provide educators with the resources necessary to expand access to a quality data science education and prepare students for 21st century jobs.

STEM education expands opportunities for students, grows our economy, strengthens our workforce across industries and boosts our nations global competitiveness, Rep. Kim said. The Data Science and Literacy Act will equip educational institutions with the tools they need to teach students of all ages and across all regions of the country the skills needed to get good-paying jobs and help our nation win the future. I will always support opportunities for students to access a quality education and achieve their dream.

As world leaders in technological advancement, its essential that we create programs that increase access to data science and literacy education so students from an early age can earn a well-rounded STEM education, Rep. Baird said. Improved access to these tools is essential for building tomorrows workforce, and I look forward to working with Congresswoman Stevens to get this bipartisan investment in STEM education across the finish line.

Statistics and data science are fundamental to production, innovation, and discovery, so there is a high demand for a workforce with statistics and data science skills, said Katherine B. Ensor, 2022 President, American Statistical Association. Everyone receives data-driven information and faces data-driven decisions daily. The Stevens-Baird-Beyer-Kim Data Science and Literacy Act brings attention to the tremendous job opportunities for data-savvy students. It helps schools provide statistics and data science education that meets workforce and society demands and prepares future researchers.

Ensuring that data literacy is a foundational aspect of education is a cornerstone of the United States long-term economic and national security. Data is everywhere, and the ideas and demands to create more of it, and the evermore complex ways to deploy it, are growing more significant by the day, said Jeff Cohen, Chief Strategy and Innovation Officer, INFORMS. This bill is a key part of addressing the substantial shortfall of STEM-prepared students for the workforce of tomorrow. Operations Research, analytics, and the science and technology of decision making are predicated on the sound and ethical use of data. INFORMS strongly supports the bi-partisan Data Science and Literacy Act of 2023 and looks forward to working to ensure its passage and effective implementation.

U.S. education is facing a perfect storm. Students are struggling to regain lost time from pandemic disruptions while data-driven technologies, like artificial intelligence and machine learning, are quickly changing the basic skills and knowledge needed to succeed, said Zarek Drozda, Director, Data Science 4 Everyone. Congressional leadership will be paramount to ensuring the next generation can build the digital and physical world rather than simply respond to it. Investing in K-16 data science will guarantee our global competitiveness, national security, and leadership in the new knowledge economy. The Stevens-Baird-Beyer-Kim Data Science and Literacy Act will be a critical step forward for preparing all students to leap into the future already here.

The Data Science and Literacy Act of 2023 supports a voluntary program at the Department of Education through which educational entities (from elementary to two- and four-year colleges) can apply for funding to increase access to data science and literacy education. Specifically, grant funding can be used for the following:

The state of Arkansas is building a talent pipeline today to meet tomorrows demands. As technology continues to evolve and impact every industry sector, it is imperative that companies have a workforce skilled in the latest advances. Arkansas is rising to the top as a national leader in developing a tech-based workforce as the state collaborates with business, industry, and education to meet these demands.

It starts in Arkansas schools where middle schoolers learn to code and high-school students are required to earn at least one credit in a computer science class before graduating. The goal is to prepare students not just for college, but also for the skills they need in the workforce.

Homegrown tech company Apptegy announced in early 2023 that it would add more than 300 jobs to its current 400-member workforce in Arkansas. After launching in 2015, Apptegy quickly became one of the fastest-growing education technology companies in the country. Today, the company works with more than 3,000 school districts across the U.S. and recently partnered with its first two international schools. Apptegy began operations in Arkansas with the Venture Center as the first tenant at the Little Rock Technology Park, and quickly outgrew the space. Today, the company gives back by partnering with the Venture Center to nurture and encourage the next class of tech entrepreneurs.

When global cybersecurity provider Sequretek announced the opening of its U.S. headquarters in the Little Rock Technology Park, the company attributed the innovative technology focus, talent access, and growth-friendly business environment as top reasons for selecting Arkansas.

And when SupplyPike, a start-up that offers a digital supply chain management platform for consumer-packaged goods, announced it would begin operations with 180 jobs, Fayetteville was the new companys ideal location. SupplyPike first served as the research and development wing within CaseStack, a private equity-backed, cloud-based logistics company also operating in Fayetteville.

Other tech companies finding success in The Natural State include First Orion, Acxiom, IPG, FIS, Genpact, Inuvo, Metova, Arkana Labs, and Zebra Technologies.

Even companies that are not traditional tech companies are finding themselves in need of skilled talent.

Walmart and Tyson Foods, both founded and headquartered in Arkansas, are great examples of companies in non-tech industries that employ hundreds of tech workers. Walmarts Spatialand, a v-commerce startup, introduced its first virtual reality experience in partnership with DreamWorks Animation. Tyson Foods venture fund focuses on investing in entrepreneurial food businesses, products, and technology to keep its businessand Arkansasat the forefront of tech trends in the food and beverage industry.

While the states resources and business climate attract companies, outdoor opportunities are increasingly attracting workers, and Arkansas has always been a prime destination for outdoor enthusiasts. Biking and hiking trails, lakes and rivers, and duck and deer make The Natural State a magnet for tech talent.

Arkansas has consistently lowered taxes during each fiscal legislative session and has removed bureaucratic red tape to make Arkansas more competitive.

Visit http://www.ArkansasEDC.com for more information.

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Making Moves To Develop Tech Talent - Business Facilities Magazine

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About Mohammed VI polytechnic University (UM6P)

Mohammed VI Polytechnic University (UM6P) is an internationally oriented institution of higher learning, that is committed to an educational system based on the highest standards of teaching and research in fields related to the sustainable economic development of Morocco and Africa. UM6P is an institution oriented towards applied research and innovation. On a specific focus on Africa, UM6P aims to position these fields as the forefront and become a university of international standing.

More than just a traditional academic institution, UM6P is a platform for experimentation and a pool of opportunities, for students, professors and staff. It offers a high-quality living and study environment thanks to its state-of-the-art infrastructure. With an innovative approach, UM6P places research and innovation at the heart of its educational project as a driving force of a business model.

In its research approach, the UM6P promotes transdisciplinary, entrepreneurship spirit and collaboration with external institutions for developing up to date science and at continent level in order to address real challenges.

All our programs run as start-ups and can be self-organized when they reach a critical mass. Thus, academic liberty is promoted as far as funding is developed by research teams.

The research programs are integrated from long-term research to short-term applications in linkage with incubation and start-up ecosystems.

DESCRIPTION-GSMI

Geology and Sustainable Mining Institute (GSMI) is based on a new strategic vision of research and training, based on the integration of the different components of the mining life cycle (geology, extraction, mineral processing, sustainable mining, mining environment and CSR). In addition to conventional mineral resources (phosphates, metals, etc.), the institute will also develop new topics such as mining economics, water management in the mine, decarbonization of the mining cycle, CSR, etc.

Job Description

The Geology and Sustainable Mining Institute (GSMI) of Mohammed VI Polytechnic University (UM6P) invites applications for a full-time researcher position in datascience applied to geosciences, with a particular focus on the use of AI in mineralogy, ore processing, water treatment, and prediction of geochemical behavior of solid mine waste.

The expected start date is September 1st, 2023. The appointment will be at the scientist/professor level depending on the CV and past experience of the successful candidate.

The candidate is expected to combine machine learning and geosciences to develop innovative research related to geo-informatic. We seek candidates whose research interests complement and enhance the existing GSMI strengths. The candidate must demonstrate a proven ability to design, coordinate and organize scientific research projects, as well as to establish intra and extramural funded research programs. The ability to develop partnerships with industry and state entities is highly desirable. Finally, the candidate should demonstrate good address for meeting and conferring with others, and potential for academic and scientific leadership. Strong skills in coding using R, Python and Matlab will be a plus. The successful candidate will contribute to building a solid research portfolio at the Institute.

Areas of research:

Required documents include:

All documents above should be zipped in a single file.

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GSMI - Professor in Data Science job with MOHAMMED VI ... - Times Higher Education

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