The internet’s big bang proliferation, along with digitization, has resulted in the prominence of technology trends such as Big Data, artificial intelligence, IoT, and cloud computing, with data serving as the nucleus that drives these strong advancements. According to research, 99% of the world’s data has been created in the previous ten years, with estimates implying that 328.77 million terabytes of data are created per day.
Because of the data explosion, there has been a growth in data-driven jobs all around the world. Individuals in these businesses must evaluate vast volumes of data using cutting-edge technologies and algorithms in order to identify hidden opportunities, get insightful knowledge, and make better business decisions that result in increased profitability and contented consumers.
Furthermore, data science is ranked as one of the top three jobs in the world by Glassdoor, with employees earning a national average salary close to $110,000 per year, according to the U.S. Bureau of Labor Statistics (BLS). It is not surprising that more professionals want to work as data scientists in the IT industry.
So, if you are wondering whether a job as a data scientist is rewarding, look no further. The best way to seize a good opportunity and stand out from a talented pool of other competitors is to obtain a data science certification. Although obtaining one can be a daunting task, having one in your name validates and stands a testament to your data science talents and supports you in demonstrating your data science talents to your company.
To establish strategies for gathering and processing data for analysis, data scientists use a variety of scientific methodologies, processes, and algorithms. This process is known as data science. It includes handling and creating data models using a variety of mathematical, statistical, programming, artificial intelligence, and machine learning techniques, as well as incorporating those models into functionality.
To discover the relevant trends or patterns in the data, data scientists deal with vast amounts of raw and structured data utilizing a variety of advanced analytical techniques such as predictive modeling processes and designing their own machine learning algorithms.
A data science certification by itself cannot ensure your success in the field because it takes a combination of sincere effort, experience, aptitude, and abilities to succeed as a data scientist specialist. A data science certification, however, can be a great method to advance in your career, increase your earning potential, and have more job prospects. It also imparts knowledge about new techniques and technological advancements while serving as a testimonial to your professional savvy and knowledge base. As a result, holding a data science certification allows you to secure the spotlight in your job interviews with recruiters and help you stand out from the crowd.
Additionally, by successfully completing a certification program, you can get highly sought-after technical skills such as statistical analysis and computing, Big Data approaches, data analysis, data visualization, advanced mathematics, and programming. By enrolling in a reliable certification program, you will get actual real-world experience while working under the tutelage of industry professionals, allowing you to circumvent a lack of experience or a formal degree in exchange for skill and technical competence.
Let us look explore the most prominent data science certifications available to help you succeed in your career:
The Certified Analytics Professional (CAP) certification is recognized as the pinnacle of data science and is vendor- and technology-neutral. The CAP certification attests to the professional’s experience in seven disciplines of data analytics as well as your capacity to extract valuable insights from challenging data, which is important for people thinking about a career in data science and analytics. Having a CAP demonstrates your superior analytical talent and competence. To earn the CAP certification, you must pass the Associate Certified Analytics (aCAP) Professional test.
According to the eligibility requirements for the CAP certification exam, you need to have three years of work experience in a related field if you have a master’s degree in that field, five years if you have a bachelor’s degree in that field, and seven years if you have any other degree that is unrelated to analytics.
Professionals having four or more years of experience in research and analytics and a bachelor’s or master’s degree in data science or any other field that is closely related are eligible for the coveted SDS certifications from the Data Science Council of America (DASCA). The SDS certification is broken into five tracks based on expertise level. Data Science Essentials, Data Science Applications in Business, The Functional Framework of Data Science, Building Pan-Organization Support, Big Data Analytics Essentials, and Advanced Data Science topics like Stochastic Modeling, Data Structures, and Machine Learning are just a few of the fundamental and advanced concepts that are covered in the exam. The certification is supported by 30 core professional knowledge subjects and seven fundamental knowledge dimensions. Candidates must demonstrate advanced knowledge of Statistical Analysis techniques and tools, analytics platforms such as SPSS/SAS, R, object-oriented programming such as C++, and Core JAVA, as well as working knowledge of the Linux and Unix environments.
One of the top-rated data science certifications for senior professionals provided by DASCA is the Principal Data Scientist certification, which is both a qualification-credential and a professional designation. There are four pathways available in this program, and each one calls for at least 10 years of professional data science expertise. The certification emphasizes extensive subject analysis and innovative technologies
Data Scientists’ technical mastery, knowledge, and expertise are validated by the Open CDS, a global certification based on independent experience. Possessing an Open CDS certification shows that you have the perseverance and skill set necessary to produce effective data analytics for advancing the performance of your organization’s operations. Candidates for the certification must prove their expertise and familiarity with a set of conformance requirements through written applications and peer interviews; they are not required to take written examinations or go through training courses in order to be eligible for the exam.
As you advance through the CDS certification process, you will earn three certifications.
Certified Data Scientist, Level 1
Master Certified Data Scientist, Level 2.
Distinguished Certified Data Scientist, Level 3.
Your ability to implement and run machine learning workloads on the Azure cloud platform utilizing data science and machine learning is strongly emphasized by the Microsoft Azure Data Scientist Associate certification. A component of the Microsoft Azure family, this certification. Being an entry-level certification, it covers topics including machine learning (ML), artificial intelligence (AI) solutions, natural language processing (NLP), computer vision (CV), and predictive analytics. You need to be skilled at performing a range of related tasks, such as creating and managing virtual networks, deploying and managing resources, managing identities and governance, installing and managing storage, and more.
Multiple SAS data qualifications are combined into the SAS Certified Data Scientist credential. Pair the SAS Data Curation certificate with either the Advanced Analytics Professional or the AI & Machine Learning Professional credential to finish the coursework and become eligible for the SAS Data Science credential. The qualifications include information on machine learning, programming expertise, predictive modeling, data science principles, data management, analysis, and manipulation. It is intended to be a practical certification for data scientists who use data visualization tools, as well as external analysis tools like Hive/HiveQL, Hadoop, PIG/PIGLATIN, and machine learning models, to extract meaningful insights from enormous amounts of data and then apply that knowledge to real-world issues.
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