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Data is in demand. And it is no surprise that jobs that help to collect, discern, and utilize data are growingand fast.

Occupations that deal with data are projected to have strong job growth by 2031, according to the U.S. Bureau of Labor Statistics. For context, on average all occupations are expected to grow by 5%. Data scientists, as just one example of data-related occupations, are growing by over seven times that amountat 36%.

Moreover, data-related occupations had a median annual wage above the median for all occupations in 2022, and data scientists in particular make double the median wage in the U.S.

The terms data science and data scientists have only been popularized within the past decade, according to Wade Fagen-Ulmschneider, a teaching associate professor of computer science at the University of Illinois. Data analytics, on the other hand, has been around for longer and is a field that many students of statistics, economics, and even some social sciences end up pursing.

With the two areas often discussed in conjunction with one another, you may be wondering, whats the real difference between data science and data analytics? Fortune is here to help.

Broadly speaking, data science is the study of using and applying data to solve real-world problems. It encompasses multiple areas, including AI machine learning, and algorithms and intersects closely with subjects like computer science, statistics, and business. It can also encompass data analytics itself.

Data science is typically about estimation of unknown phenomena and prediction of future events, Joel Shapiro, clinical associate professor of managerial economics and decision sciences at Northwestern Universitys Kellogg School of Management tells Fortune. Data science can include capturing and managing data, building algorithms, and articulating the implications of results.

Fagen-Ulmschneider previously told Fortune that he believes data science skills will soon be as ubiquitous as knowing Microsoft Office skills.

Instead of looking at the future, data analytics focuses more on the pastas well as the now.

Data analytics uses historical data to identify trends and articulate the implications of those trends, Shapiro says. Experts in the field also tend to be adept at data visualization techniques.

The field is important, Shapiro adds, because it helps uncover the stories that otherwise may not be seen or found.

There are so many things that can be measured, and it is impossible for any person to track all of them, let alone really understand how they relate to one another.Those trends and relationships enable us to understand and synthesize past events, which then can be used to make future-looking decisions, Shapiro says.

Theres no question that data science and data analytics are inherently similar. And from a business perspective, both can be critical components to decision-making.

In terms of skills, those working in data science and data analytics will likely be working as part of a team of experts, so having effective communication and collaboration skills are important.

On a more technical side, Fagen-Ulmschneider notes that data science and data analytics will benefit from learning skills in statistics, mathematics, and computer science. For those particularly interested in data science, he suggests students lean heavily on computer science, and for students wanting to become a consultant should focus on statistics or even actuarial/finance.

Shapiro goes further and notes that data science requires a deeper knowledge of things like statistics, machine learning, coding, experimentation, and predictive modeling. Data science, he adds, is better at the individualized level like customized customer experiences, optimized pricing, and differentiated messaging for digital users.

On the other hand, data analytics, Shapiro says, typically requires knowledge of basic data management, some statistics and data visualization techniques and technologies.

So, if things like AI, machine learning, and predictive models excite you, focusing on data science may be for you, whereas if using data to identify and visualize trends, you may want to take a closer look at the analytical side.

Overall, though, data science or data analytics lean on each other, and many of the skills and expertise needed to succeed in either area are similar. Neither data science or data analytics are mutually exclusive, and both play a major role in solving the biggest problems in todays world.

See more here:

What is the difference between data science and data analytics? - Fortune

In today's digital age, the quote by Michael Hakvoort, "If you're not paying for the product, then you are the product", has never been more relevant. While we often think of this in relation to social media platforms like Facebook, it also applies to seemingly harmless free resources such as YouTube courses.

Sure, the platform earns revenue through ads, but what about the time, energy, and motivation you invest? As data becomes increasingly valuable, it's essential to carefully evaluate the potential impact of free data science courses on your learning journey.

With so many options available, it can be overwhelming to determine which ones will provide real value. That's why taking a step back to consider some critical factors before diving into any free resource is crucial. By doing so, you'll ensure that you make the most out of your learning experience while avoiding common pitfalls associated with free courses.

Free courses often provide a one-size-fits-all curriculum, which might not align with your specific learning needs or skill level. They might cover fundamental concepts but lack the depth required for a comprehensive understanding or for tackling complex, real-world problems. Some free courses may have all the necessary ingredients to solve real-world data problems, but they lack structure, leaving you confused about where to start.

Learning a programming language alone can be challenging, especially if you come from a non-technical background. Data Science is a field that demands a hands-on approach. The free courses often offer limited opportunities for interactive learning, such as live coding sessions, quizzes, projects, or instructor feedback. This passive learning experience might prevent you from applying concepts effectively, and eventually, you will give up on learning.

The internet is flooded with free courses, making it challenging to discern the quality and credibility of the content. Some might be outdated or taught by individuals with limited expertise (Fake Gurus). Investing your time in a course that doesn't offer accurate or up-to-date information can be counterproductive.

Here is a list of free courses that I believe are of high quality:

Unlike paid courses, free resources do not come with external accountability measures such as deadlines or grades, making it easy to lose momentum and abandon the course midway. The lack of financial commitment means that students must rely solely on their internal drive and discipline to stay motivated and committed to completing the course. College is a great example of this. Students think 100 times before leaving college because of the costs involved. Most students complete their bachelor's degree because they have taken a student loan and need to pay it back.

Networking is a significant part of building a career in data science. Free courses typically lack the community aspect found in paid programs, such as peer interaction, mentorship, or alumni networks, which are invaluable for career growth and opportunities. There are Slack and Discord groups available but they are usually community-driven and may be inactive. However, in a paid course, there are moderators and community managers who are responsible for making networking easier between students.

Paid courses often provide career services, such as resume reviews, certification, job placement assistance, and interview preparation. These services are essential for individuals transitioning into a data science role but are typically unavailable in free programs. It is crucial to have guidance throughout the hiring process and know how to handle technical interview questions.

While not always necessary, certifications can boost your resume and credibility. Free courses may offer certificates, but they often don't carry the same weight as those from accredited institutions (Harvard / Stanford) or recognized platforms. Employers might not value them as highly, which could impact your job prospects. Additionally, certification exams evaluate key skills essential for working with data in any job. They assess your coding, data management, data analysis, reporting, and presentation abilities.

While free courses on data science can be a valuable resource for initial learning or brushing up on skills, they have certain limitations. It's important to consider these limitations against your personal goals, learning style, financial situation, and career aspirations. To ensure a well-rounded and effective learning experience, you should consider supplementing free resources with other forms of learning or investing in a paid bootcamp.

In the end, the most crucial factor that will help you become a professional data scientist is your dedication and focus on achieving your goals. You will not learn anything if you lack the drive required, no matter how much money you spend on the course. So, before you dive into the world of data, please think ten times if this is the right path for you.

Abid Ali Awan (@1abidaliawan) is a certified data scientist professional who loves building machine learning models. Currently, he is focusing on content creation and writing technical blogs on machine learning and data science technologies. Abid holds a Master's degree in Technology Management and a bachelor's degree in Telecommunication Engineering. His vision is to build an AI product using a graph neural network for students struggling with mental illness.

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Read This Before You Take Any Free Data Science Course - KDnuggets

The New York University Silver School of Social Work (SSSW) invites applications for an endowed open rank professorship in data science and prevention. The endowed professor will play a leadership role in the Schools newly established Constance and Martin Silver Center on Data Science and Social Equity. The appointment will begin on September 1, 2024.

The endowed professorship and Center have been created by a visionary gift to harness the emerging power of big data and related data sciences for transformational social impact. The overall mission of the Center is to help Silver and the social work profession at-large develop data- and evidence-informed interventions to equitably tackle some of societys most pressing problems. A copy of the press release that provides more information regarding the gift is here.

NYU is a leader in data science, machine learning and artificial intelligence, and related areas, with the Center for Data Science, Courant Institute, Tandon School of Engineering, and Steinhardt School (PRIISM Center). Extensive supercomputer resources are available through NYUs High Performance Computing research infrastructure. The Silver School possesses deep expertise in a variety of areas including poverty, racism, policy analysis, prevention science, substance use and misuse, integrated health, mental health interventions and services, criminal justice, homelessness, child welfare, aging, and evidence-based practice. The leveraging of the tools of data science, in alignment with existing expertise at the Silver School, represents a unique opportunity to elevate social works visibility in this field.

SSSW is an international leader in social work research and education and offers outstanding support for scholarship. Located in Greenwich Village on Washington Square Park and in one of the worlds great urban research universities, the SSSW is one of the 14 schools and colleges of NYU, the largest private university in the USA, with students from all 50 states and more than 100 nations. The diversity of the academic community and the incomparable resources of New York City enrich the academic programs and campus life of NYU and the SSSW.

In compliance with NYCs Pay Transparency Act, the annual base salary range for this position varies according to rank, with Assistant Professor salaries ranging from $75,000 to $115,000, Associate Professor salaries ranging from $100,000 to $150,000, and Full Professor salaries ranging from $175,000 to $275,000. New York University considers factors such as (but not limited to) scope and responsibilities of the position, candidates work experience, education/training, key skills, internal peer equity, as well as market and organizational considerations when extending an offer.

Qualifications

A doctoral degree is required, along with a track record of scholarship in applying data science methodology to understand and forge advances in preventing and addressing social inequities. In addition to conducting research, the successful candidate may teach in the Schools academic programs (BSW, MSW, PhD, and/or DSW), and be fully engaged in professional service at the SSSW and New York University (NYU). Applicants should exhibit a commitment to social and economic justice and possess an ability to facilitate conversations concerning privilege, oppression, and intersecting social identities.

Application Instructions

Review of applications will begin on a rolling basis. We invite candidates to apply via Interfolio and upload a letter of application, curriculum vitae, and an equity, diversity, and inclusion statement.

Applicants should apply using this link:

http://apply.interfolio.com/138951

Candidates should also upload a research statement, a teaching statement, and two representative publications as Additional Documents for their application to be considered complete. We request submission of materials at applicants earliest convenience and would welcome confidential discussions with prospective candidates.

Equal Employment Opportunity Statement

For people in the EU, click here for information on your privacy rights under GDPR:http://www.nyu.edu/it/gdpr

NYU is an Equal Opportunity Employer and is committed to a policy of equal treatment and opportunity in every aspect of its recruitment and hiring process without regard to age, alienage, caregiver status, childbirth, citizenship status, color, creed, disability, domestic violence victim status, ethnicity, familial status, gender and/or gender identity or expression, marital status, military status, national origin, parental status, partnership status, predisposing genetic characteristics, pregnancy, race, religion, reproductive health decision making, sex, sexual orientation, unemployment status, veteran status, or any other legally protected basis. Women, racial and ethnic minorities, persons of minority sexual orientation or gender identity, individuals with disabilities, and veterans are encouraged to apply for vacant positions at all levels.

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NYU aims to be among the greenest urban campuses in the country and carbon neutral by 2040. Learn more atnyu.edu/sustainability

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CONSTANCE AND MARTIN SILVER ENDOWED PROFESSORSHIP (OPEN RANK) IN DATA SCIENCE AND ... - The Chronicle of Higher Education