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Working as a data scientist is the dream of many IT professionals these days. It is no secret that data science is a skyrocketing field attracting young professionals and inspiring many to switch careers to data science. On one front are young professionals who study their courses in colleges to pursue their dream of becoming data scientists and on the other are professionals seeking to enrol in short courses with computing, business analytics, and applied science skills that they already have to switch careers. But are these short courses and data science certifications worth the time and money? Lets find out.

Earning a data science certification amid demand for skilled data scientists is a good data science career move. Heres why:

Enrolling in a professional data science certificate offers a valuable opportunity to validate your expertise. These programs, often backed by respected reputed educational institutions, require rigorous examinations to assess your grasp of essential concepts, tools, and methods. Acquiring certification serves as tangible proof of your proficiency, elevating your credibility and setting you apart in a competitive job market.

A certification program offers chances to connect with peers, instructors, and industry experts. Building a network within the data science community can lead to valuable insights, job prospects, and collaborations. It is always good to have access to exclusive forums and alumni groups, that nurture a supportive network and enhance your learning and career development.

In the expansive realm of Data Science, there are various specializations, including data engineering, machine learning, data visualization, and more. To match your interests and career aspirations, select a certification program aligned with your desired specialization. By obtaining certification in a specific Data Science area, you demonstrate your dedication to becoming a specialist in that field, positioning yourself as a highly desirable professional in your chosen niche.

In todays data-driven world, organizations actively hunt for skilled data scientists capable of leveraging data for strategic advantage. Possessing a Data Science certification significantly boosts your career prospects and unlocks a plethora of job opportunities. Whether youre entering the Data Science field or aiming for career progression, certification becomes a pivotal asset in securing your desired position.

The high demand for professionals with comprehensive data science skills translates into attractive salaries. Obtaining a data science certification can significantly boost your income compared to non-certified peers. Additionally, with organizations increasingly embracing data-driven approaches, certified data scientists can expect enhanced job security and career stability.

Not every certification can be an ideal choice for a successful data science career. It is important to consider multiple factors before registering for one.

The U.S. Bureau of Labor Statistics anticipates a significant 27.9 percent surge in the demand for professionals with data science expertise by 2026. Obtaining a certification will not only enhance your skills but also equip you with the in-demand skills of the present moment.

You can visit the websites of the aforementioned certifications and check their curriculum, exam fees, and the duration required to complete the program. This will help you choose the certification that best suits your career requirements and your organizational goals.

Data science is a highly sought-after profession that can empower you to make critical business decisions. These certifications on the list will enable you to become a data science expert, as they are comprehensive programs that include a wide range of topics.

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Are data science certifications the gateway to competitive pay? - DataScienceCentral.com - Data Science Central

For Justin Xu, a third-year mechanical engineering concentrator at the Harvard John A. Paulson School of Engineering and Applied Sciences (SEAS), spending the summer working at the Harvard-China Project on Energy, Economy and Environment (known as the Harvard-China Project) gave him an opportunity to work with data on a large scale by studying the effect of climate change on global drought patterns at Hong Kong Baptist University. The project allowed him to explore a research area hed never before pursued while learning new, broadly applicable computational skills.

At Harvard, the extent that Id looked at climate science was a General Education course I took on natural disasters, Xu said. Admittedly, going in I didnt have that much experience in climate work, but what was more rewarding and valuable was the chance to work with that scale of data.

Based at SEAS and founded in 1993, the Harvard-China Project works with partner institutions in China to study and design effective policy to address the global challenges of climate change, air quality, energy systems, and economic development.The Harvard-China Project includes researchers from SEAS and a number of other schools, including the Harvard Faculty of Arts and Sciences, Harvard T.H. Chan School of Public Health, Harvard Graduate School of Design, and Harvard John F. Kennedy School of Government.

Xu, who is pursuing a secondary in government, is part of the Harvard Model United Nations, and last summer was a legislative intern in the U.S. Senate. Both the research and political elements of the Harvard-China Project drew him to the program.

For SEAS students, I know Im not alone in having interests both inside and outside of engineering, Xu said. During the school year, SEAS students have a very intense and focused course schedule. During the summer, this program is a great opportunity to explore outside of purely technical realms.

Xu split his time in Hong Kong between research in the lab and remote computation and frequently met with graduate and postdoctoral students working in the lab.

Working with grad students was great, and I think Harvard prepared me well for the rigorous academic culture, he said. For engineering students specifically, its a really rewarding experience because its different. You get to interact with people across disciplines, which is always a great experience.

Xu grew up in Delaware and hadnt visited his extended family in China in more than a decade. This was his first time living in Hong Kong.

Hong Kong is such a dense city, and living here provided an entirely new perspective on how city life works, he said. I grew up in a suburb, but even if you grew up in Manhattan, youd probably say the same thing about Hong Kong.

Xu went into the summer with some computational skills acquired through computer science and applied math courses hed already taken. His experience in the Harvard-China Project showed how useful those skills can be, even for students concentrating in other disciplines.

Even if the courses SEAS students take dont seem directly relevant, the general process of thinking about a challenge always is, he said. This type of engineering work is so broadly applicable across the world. It opened my mind to how interdisciplinary research areas in engineering, applied mathematics and applied sciences can be. Having an experience working with large batches of data is something you can do in mechanical engineering, climate science, and everything in between.

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Data science on a global scale - Harvard School of Engineering and Applied Sciences

The calculator has replaced the slide rule. Latin is rarely offered in high school. Sentence diagramming has disappeared from most English classes.

Academic disciplines continually evolve to reflect the latest culture and technology. Why, then, are recent attempts to tinker with the high school math canon eliciting such a backlash? Students deserve a chance to learn up-to-date topics that reflect how mathematics is being used in many fields and industries.

Case in point: the debate over including data science courses as high school math options. Data science courses teach the use of statistical concepts and computer programming to investigate contemporary problems using real-world data sets.

The courses have been gaining in popularity, particularly with high school math teachers. They say the more relevant content offers a highly engaging entry point to STEM, especially for students who have been turned off by traditional math courses.

Others say that the courses are in fact detours away from STEM.

The high school teachers remain unconvinced. Its just been a pleasure to have an absence of hearing, How am I going to use this? or Why do I need to learn this? Lee Spivey, a math teacher from Merced County, told members of the California State Board of Education at their July meeting, before they voted to make California the 17th state to add data science to its curriculum.

This course transformed my teaching practices and transformed the lives of many students.Special education, English learners and calculus students worked side by side, Joy Straub, who taught a data science course in Oceanside for six years, told the board. Students who had a dislike for math suddenly were transformed into math lovers . . . skilled in statistical analysis, computer programming and critical thinking. I saw many students who never would have taken an AP math course take AP Statistics.

Despite the enthusiasm from teachers, some university STEM professors in California objected. Their vehement criticism focused on the fact that data science courses were proposed in the states math framework as alternatives to Algebra II. Faculty from both of the states public university systems went on record opposing the idea that students could take data science or statistics courses to meet university eligibility requirements instead of Algebra II. (They seemingly didnt realize that a 10-year-old policy already permitted students to take data science or statistics in lieu of Algebra II though that route is rarely utilized, at least among applicants to the University of California.)

Related: COLUMN: How can we improve math education in America? Help us count the ways

Algebra II, which covers topics such as exponential and logarithmic functions, is a typical university admission requirement. Twenty states consider Algebra II a high school graduation requirement, but about half of those allow for exceptions or alternative courses, according to a 2019 report, the most recent available.

Algebra II is traditionally considered a stepping-stone to calculus, which remains the key to the STEM kingdom. Many believe that bypassing the course risks prematurely closing off doors to STEM.

Critics, however, complain that the course is jammed with topics that are hard to justify as essential. How often do we use conic sections or synthetic division? Even content that is more important take exponential growth and the very concept of a function is often weighed down by tedious classroom teaching and rote learning.

At the same time, statistical reasoning and data fluency are becoming indispensable in the 21st century, regardless of profession. Digital technologies are changing everything from fitness training to personal investing. But many students are missing out on this essential learning because so many teachers feel ill-equipped to teach these topics, simply run out of time or bow to the perceived preferences of colleges.

Interestingly, both sides of the debate cite the importance of expanding access to STEM fields. The standoff reflects differing perspectives about how math is learned, including a tension between content coverage and conceptual understanding.

Algebra II defenders emphasize that the topics are foundational for STEM fields.

However, many students who take Algebra II dont learn much of the content. And even if students gain proficiency in Algebra II procedural skills, it doesnt necessarily improve their performance in subsequent college math courses. In college, two-thirds of high school calculus students retake calculus or take a prerequisite course.

Proponents of data science courses say not only is data competency essential to everyones future (and to STEM fields themselves) but that the greater relevance the courses provide can actually keep students interested and invested in STEM including in algebra.

Of course, good content and comprehension are both key to math learning. Ultimately, empirical research is needed to validate how well various paths prepare students for college and STEM success.

That is, states must analyze actual longitudinal data on student progress through different sequences to solve this math dilemma. Surely, both data science and algebra will have some role in the future likely with some archaic Algebra II content dropped, as proposed by the National Council of Teachers of Mathematics.

Though press coverage including of Californias recently approved math framework has emphasized the extremes of the debate, much work happening around the country exists in the more ambiguous middle.

Numerous efforts are underway to update Algebra II. Georgias modernized Algebra II course, for instance, incorporates data science concepts. The University of Texas Charles A. Dana Center also provides a model for such a course.

Related: TEACHER VOICE: Calculus is a roadblock for too many students; lets teach statistics instead

Other efforts focus on ensuring that data science courses teach some algebraic concepts. CourseKatas founders promote using data science courses to teach some basics of Algebra II. So does Bootstrap, a curriculum development project based at Brown University.

Even in California, where friction over how to fit data science into the mathematical canon has been especially public, most students who take the courses also take Algebra II. So do at least 99.8 percent of applicants to the UC system which may rise to 100 percent, if some faculty have their way in blocking statistics and data science courses from replacing Algebra II.

Such a decision might preserve coverage of traditional math content. But it would dodge the question of how to ensure that the next generation of students has the statistical and data fluency the 21st century demands. The California teachers are right: We cant defend teaching techniques like synthetic division when students finish high school unable to use data to understand the world around them.

Pamela Burdman is executive director of Just Equations, a California-based policy institute focused on the role of mathematics in education equity.

This story about data science courses was produced by The Hechinger Report, a nonprofit, independent news organization focused on inequality and innovation in education. Sign up for Hechingers newsletter.

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