DASCA Accredited Data Science Programs

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Since 1995, The Data Science Council of America (DASCA) has offered third-party certification to qualified data professionals and education programs. In the years since, DASCA has asserted itself as a premier assessor of employment readiness in the data domain.

DASCA Accredited Data Science ProgramsDue to its independence, the Data Science Council of America tests for data skills in a tool agnostic manner. Testing focuses on general data science capabilities suitable for solving practical data science problems, rather than testing for solutions within a given platform. DASCA's ability to identify top talent through testing streamlines hiring and provides a degree of standardization within the data science community.

Why should you consider a DASCA accredited program?

The recent uptick in demand for employees with data skills contributed to an influx of both new data professionals and preparation programs. In today's data environment, third-party credentials serve a particularly important place, providing a degree of confidence that an applicant is a skilled data scientist, analyst, or engineer. For potential students, DASCA certification provides an additional guarantee that a program is offering a quality curriculum.

Are there online data science programs accredited by DASCA?

Yes. Several DASCA accredited programs offer online education including the University of Virginia and the University of California - Berkeley.

What is the DASCA?

DASCA's proprietary Data Science Book of Knowledge (DASCA-DSBoK) and Essential Knowledge Framework (DASCA-EKF) combine to deliver a high-quality certification platform. The DASCA-DSBoK is the result of decades of research resulting in determination of the most relevant skills to test for data professionals. The DASCA-EKF is the identified corpus of content that data scientists should understand in order to bring maximal value to their organization and achieve DASCA certification.

DASCA Accredited Data Science Degrees

University of California - Berkeley's Top Undergraduate and Graduate Data Science Programs

UC Berkeley boasts one of the top Data Science programs in world and offers undergraduate and graduate degrees.

The Bachelors in the Art of Data Science program's prerequisites include Calculus, Linear Algebra, and Data Structures courses. A fundamental understanding of UC Berkeley's prerequisite course catalog positions one well to be a data scientist in the future. Though not all data scientists work with advanced mathematics regularly, knowledge of the underlying assumptions of statistical models is helpful in interpreting machine learning results. Additionally, the course DATA C8 Foundations of Data Science is required for all Data Science majors. In Foundations of Data Science, students study the foundations of advanced analytics through computational and inferential thinking, as well as practical examples. The opportunity to work with real-world data early in the program is a unique advantage of Berkeley's undergraduate program. The data that practitioners experience in professional work typically requires hours of pre-processing, and working with this data from the beginning allows the student to be better prepared for real projects.

After completing the prerequisite portion of the degree, students select classes from segments of:

  • Computational and Inferential Depth
  • Probability
  • Modeling, Learning, and Decision-Making
  • Human Contexts and Ethics
  • Domain Emphasis
  • UC Berkeley also offers a twenty-seven credit unit online Masters of Information and Data Science (MIDS) degree. The MIDS curriculum caters to experienced data scientists looking to further their education. The program's core curriculum includes topics including research design, machine learning, data engineering, and ethics. While courses are online, students are required at lease one immersion on the UC Berkeley Campus. During the immersion session, students study with their classmates in person and participate in workshops together. The immersion sessions are intended to not only develop data science skills, but also to provide networking opportunities to MIDS students.

    The MIDS is designed to be completed within twenty months, but three paths are offered in order to accommodate working students. The accelerated path allows students to complete the program in twelve months, at a pace of three courses per semester. The standard path can be completed in twenty months and students take classes at a rate of two courses per semester. Lastly, a decelerated path permits students to take one course per semester and complete the degree in 32 months. The program provides ample flexibility for students to participate regardless of their other time commitments.

    MIDS' curriculum includes four required courses - Research Design and Application for Data and Analysis, Statistics for Data Science, Fundamental of Data Engineering, and Applied Machine Learning. An additional course titled Introduction to Data Science Programming is also required for those without experience in object oriented programming. In the course, students are exposed to Python, Jupyter notebooks, and source control with GitHub. Experienced programmers are able to exempt out of the course if they are able to demonstrate sufficient ability.

    After completion of the foundation courses, students select nine credit hours of advanced courses. Available courses include Experiments and Causal Inference, Deep Learning, Machine Learning at Scale, and Natural Language Processing with Deep Learning. These courses provide students with the opportunity to apply their knowledge in deeper dives. For example, enrollees in the Machine Learning at Scale course analyze big data using Apache Hadoop and Spark, which is among the most in demand skills in modern data science.

    Lastly, all students complete a capstone courses which tests their core skills and understanding of advanced analytics. The Synthetic Capstone project is completed as a team of three or four students, and is evaluated on their ability to deliver and a communicate professional-grade project.

    Featured Online Data Science Programs

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    Stanford University's Multiple Data Science Programs

    Stanford offers several data science-related degrees including an undergraduate Statistics path with a minor in data science, a Masters program, and Data Science Scholars which are current PhD students at Stanford using data science in their research.

    The Data Science minor was developed for social science and humanities majors seeking to deepen their analytical skillsets. The program is 22 credit hours consists of statistics and calculus, programming, and exposure to data mining.

    The MS in Data Science at Stanford is a 45 credit hour program composed of 15 units of mathematics and statistics, 3 units of experimentation courses, 6 units minimum of scientific computing, 6 units of machine learning methods and applications, 3 units of practical work, and elective data science courses for the remainder of the program.

    The Masters program's focus on mathematics and statistics is great for those seeking a deeper understanding of today's machine learning algorithms. While most programs require a handful of mathematics and statistics courses in their curriculum, Stanford's inclusion of 15 credit hours worth provides students with the opportunity to dig deep into the foundations of data science.

    Duke University's Data Science, MIDS, and AI Degrees

    Durham, North Carolina's Duke University is among the top degrees in the world for artificial intelligence. Duke currently offers both undergraduate and graduate-level Data Science programs. At an undergraduate level, an interdepartmental major is available in two paths. The Data Science Concentration is within the Computer Science BS major, and emphasizes the practical side of solving data science problems. The program is intended for those interested in studying higher-order data science from a computer science perspective. The IDM program is a collaborative effort between the departments of Computer Science and Statistical Science featuring two paths - The IDM in Sta+CS on Data Science which emphasizes statistical data analysis, and the IDM in Math+CS on Data Science focuses on the practical uses and mathematics machine learning and data science.

    Duke also offers a masters program, called the Duke Master in Interdisciplinary Data Science, or MIDS. The curriculum for the MIDS program is 42 credit hours, including 4 full-semester electives. Core classes include Data Science ethics, Introduction to Natural Language Processing, and Practicing Machine Learning. Additionally, students have the opportunity to choose between dozens of electives ranging from time series courses to healthcare and social sciences. Students complete a capstone project under the guidance of faculty and staff with relevant experience in the research area. Students work to solve real-life problems for external organizations (private firms, government, etc.) and present their findings along with a white paper regarding implications.

    University of Pennsylvania's Data Science Masters

    The University of Pennsylvania's MSE in Data Science is intended to be completed within a year-and-a-half and two years, and designed to prepare graduates for today's demanding job market. Courses are divided into three categories - Foundations, Core Requirements, and Technical and Depth Area Electives.

    Foundations is composed of a course in programming languages and techniques, and another in linear algebra. Successful completion of these courses establish a solid foundation to build upon in later courses. Linear algebra powers many of the models used in modern machine learning as datasets are a form of matrix. By requiring a linear algebra course early in the program, the University of Pennsylvania provides students with the means to fully understand the computational underpinnings of machine learning. Additionally, students are required to take an introductory programming or software development course. If similar courses were taken before, degree candidates are permitted to exempt the corresponding graduate level course.

    Next, students are required to take three Core Requirement courses - Statistics for Data Science, Big Data Analytics, and can choose between a handful of machine learning and data mining courses for the third. The remaining fifteen credits are Technical and Depth Area Electives, where students choose five courses from practical application or methodology courses. Available electives include Forecasting and Time-Series Analysis, Biomedical Image Analysis, and Deep Learning for Data Science.

    The University of Pennsylvania's wide variety of electives is beneficial to degree candidates, as options ranging from medical to robotics and marketing are available. The opportunity to be exposed to multiple fields lets students explore their interests.

    New York University’s Undergraduate and Graduate Data Science Programs

    NYU is home to undergraduate, graduate, and PhD Data Science programs. New York University’s data science related university-wide initiative is facilitated by its Center for Data Science, which began in 2013. The center’s primary focus is to provide the necessary skills and resources to students and researchers interested in big data analytics.

    The undergraduate curriculum consists of sixteen points of mathematics, 16 points of computer science courses, and 20 points of data science courses within the NYU Center for Data Science. The mathematics component is structured in-line with other top programs, containing calculus, linear algebra, and statistics. The computer science segment is composed of class exposing students to machine learning, data management, and general computer science. The final segment of classes within the NYU Center for Data Science are meant to provide students with a comprehensive, holistic view of the field. For example, in Causal Inference students learn the best practices to design experiments that are statistically-robust and avoid common pitfalls in experiment design. In Intro to Data Science, students address problems from social science, humanities, and science settings using advanced analytical techniques.

    NYU’s Masters of Science in Data Science is a 36-credit hour degree program that consists of five mandatory courses – Introduction to Data Science, Probability and Statistics for Data Science, Machine Learning, Big Data, Capstone Project and Presentation, and another elective from a subset containing courses in deep learning, natural language processing, and optimization. The program’s requirement of a big data course is a comparative advantage, as an experience with big data platforms is highly-demanded in the current labor market. DS-GA 1004 Big Data covers fundamental tools of big data including MapReduce, Hadoop’s Distributed File System, and Spark. Following completion of the Big Data course, students will understand the underlying concepts that sit beneath petabytes of data online. Following completion of the mandatory coursework, students select remaining courses from electives including Probabilistic Time Series Analysis, Machine Learning for Healthcare, and Responsible Data Science. Electives are not available in every semester, rotating every year. The program is designed the be completed within two years at a pace of three classes per semester. Prior to graduation, students complete a capstone project that tests their knowledge to date and provides an opportunity to research real world problems under the guidance of professionals. Teams of students are matched with researchers within various departments of the university and contribute to their research.

    New York University’s PhD in Data Science is a 72-credit hour composed of 18 hours of required courses, 39 credit hours of electives, a teaching requirement, qualifying exams, and a dissertation. The required courses and elective selection are similar to the graduate program, differing predominantly in volume. The current teaching requirement for PhD students mandates that students are to have taught or served as a section leader for at least one course by the end of their fourth year of study. The program contains two sets of qualifying exams – the Comprehensive Exam and the Depth Qualifying Exam (DQE). The Comprehensive Exam covers content from DS-GA 1003 Machine Learning and DS-GA 1004 Big Data, and ensures that the candidate is adequately knowledgeable. The Depth Qualifying Exam planning begins before the end of the third semester, and students reach out to research advisors to determine a research agreement. The student’s DQE committee determines a syllabus, and provide it to the student no less than two months before the test date. Candidates are then tested on their knowledge of the research content, ensuring an adequate fit. By no later than May 15 of a candidates third year, a student’s PhD thesis top proposal must have been approved. In addition to the traditional PhD, NYU offers a medical school track for interested students. The medical school track expands course selection to include medically-related electives and guidance from experts in the desired research area.

    University of Virginia’s Data Science Minor and Online Masters Degree

    The University of Virginia offers an undergraduate Data Science minor, a Masters degree available both traditionally (in-person) and online, and professional non-degree programs.

    The undergraduate minor requirements are met through the completion of five approved courses, two of which must be Data Science courses. Students are expected to complete classwork in five segments, including foundational programming, analytics, database systems, data design, and applied data science. The minor curriculum is designed to expose students to not only the analytical skills required to perform advanced analytics, but also communications and the ethical considerations of the field.

    UVA’s Masters program is 32 credit hours and is designed to be completed in less than a year. Beginning in the summer, students complete 9 hours of coursework which cover foundational topics including programming, exploratory data analysis, and linear models. The fall semester is composed of 12 credit hours worth of statistics, data science ethics, and R programming. Additionally, students begin to work on their capstone projects in the fall semester, working in collaboration with their group members and advisors. Teams are made up of between two and four members, and allow students to work a full-cycle project. In the final spring semester, students take a deep learning course, 2 upper-level electives, and complete their capstone projects.

    University of Florida’s DASCA Accredited Bachelor of Data Science

    The University of Florida offers a DASCA-accredited Bachelors of Science in Data Science. The program requires a minimum of 62 credit hours in data science and related coursework, covering mathematics, programming, and data science algorithms. The program’s data science requirements are 17 credit hours of mathematics (calculus, linear algebra, and computational), 18 hours of statistics, a minimum of 12 credits of computer science coursework, a 3 credit hour ethics course, and 9 hours of humanities or social sciences.

    The University of Florida’s Data Science program is within the Statistics department, and billed as a means to learn both the statistical methods in tandem with the computer science components of data science. A considerable portion of the program’s statistics coursework is completed in R, providing student’s with an opportunity to practice machine learning while learning statistical concepts.

    University of Arizona Masters in Data Science

    The Masters of Data Science at the University of Arizona is a 30-unit program that intends to prepare students for the today’s data challenges as well as tomorrow’s. The program is available both online and in-person. Within the first few months of the program, students develop a Master’s Plan of Study with a faculty advisor. The Plan of Study identifies courses intended to be transferred (if any), courses already completed at the University applicable to the degree, and lastly the additional coursework to complete the degree program.

    The proposed timeline for the degree is roughly a year and half, beginning in the fall and finishing in the spring of the second year. In the first semester, students are expected to complete courses in ethical issues and data mining. A unique feature of the University of Arizona’s degree program is the exposure to an ethics class in the first semester, whereas many have a similar class later in the degree as an elective. The following spring, students attend courses in data analysis and visualization, and choose another 6 credit hours of electives. Options for electives include Artificial Intelligence, Applied Natural Language Processing, and Fundamentals of Optimization.

    The next fall and spring allow for a total of 12 additional credit hours of electives, but also for the senior capstone course. The capstone project is an opportunity for students to display the skills acquired during the program as well as push the student’s technical skills to their limit. The project is approved by faculty advisor, and advisory assistance is available to students throughout the duration of the project.

    Johns Hopkins University Masters of Data Science

    Johns Hopkins University offers a Masters of Data Science intended to train students on data science tools and the foundational mathematics and computer science power them. Students begin with an Introduction to Data Science course before selecting a course in four core areas: statistics, machine learning, optimization, and computing, following this sequence with an additional four class. Options for additional courses include computational medicine, mathematical finance, and natural language processing. The program culminates in the Capstone Experience in Data Science, an advised research project. The capstone project deliverables include a written paper displaying data science skills acquired during the masters program to apply those skills to additional applications. The paper is the presented as a research poster at the end of the semester.

    University of Michigan’s Statistics Degrees

    The University of Michigan has a long history of top university for both statistics and computer science students, culminating in a similarly strong data science programs. The university offers both undergraduate and graduate programs, with undergraduate students having the opportunity to pursue their degree from either the College of Engineering or the College of Literature, Science, and Arts. The LSA degree program is 120 course hours compared with the 128 course hour Engineering degree. The programs differ in their ease of being paired with a dual major. The LSA degree has more overlap with other programs within the Literature, Science, and Arts school, and the same is true for engineering co-majors for paths within the engineering school.

    The two undergraduate degrees begin identically – with 19 credit program cores consisting of probability and statistics work, an introduction to programming, and data structures courses. Students of both programs then complete a 3 course elective series of machine learning, data management, and data science applications. Students must then complete 8 credits of what are considered advanced technical electives. These pre-approved electives include courses in robotics, data mining, and parallel computing.

    The programs diverge in the later components regarding electives in that the engineering path requires more technical electives, and the LSE path requires a capstone whereas it is optional for the engineering degree.

    Data Science Master’s Program is a partnership between the Statistics program and Biostatstics departments and the Computer Science, Engineering, and School of Information. The collaboration provides students with a high-quality education and a comprehensive training in data science. The goals of the MS in Data Science program is to prepare students to apply statistical and computational means to datasets to solve problems involving distributed big data.

    Students must take required courses in discrete mathematics, programming, and data structures, and chose two additional statistics and probability courses. Following completion of the mandatory coursework, degree candidates complete a series of introductory machine learning classes and three electives, one from each category of Principles of Data Science, Data Analysis, and Computation. Capstone options include Principles and Practices in Effective Statistical Consulting, Directed Study, and Big Data Analysis.

    Standardized Curriculum for DASCA Accredited Data Science Degrees

    DASCA-accredited degree programs share a number of features in common.

    Real world data courses

    First, DASCA-accredited programs provide opportunities to work with ‘real-world’ data. The emphasis on experiencing the data science pipeline from raw data to end result allows students to gain comprehensive knowledge of the field. Oftentimes data scientists must build or clean their own datasets prior to modeling, and DASCA certified programs prepare students for those tasks.

    Machine learning and algorithms

    Next, certified programs approach machine learning as something to be understood instead of viewed as ‘blackbox’ algorithms. Curricula includes a healthy dose of statistics, linear algebra, and calculus. While many data scientists do not routinely need to program algorithms from scratch, understanding a programmable model allows data scientists to create unique solutions and to communicate with stakeholders.

    Prioritizes big data skills

    DASCA-accredited programs also prioritize big data skills, preparing students for both today and the future of analytics. As datasets continue to grow, data practitioners are increasingly required to work with big data. Familiarity with Apache’s Hadoop and Spark, for example as many of these programs cover, position students well to meet the needs of organizations for years to come.

    An example of a DASCA-accredited graduate program might resemble:

    First Semester

    • Introduction to Statistics
    • Data Programming in Python
    • Database Management
    • Optimization and Linear Algebra

    Second Semester

    • Machine Learning
    • Big Data Analytics
    • Data Science Elective
    • Data Science Elective

    Second Semester

    • Senior Capstone
    • Deep Learning
    • Data Science Elective
    • Data Science Elective

    Additional Certifications through DASCA

    DASCA offers certifications for Big Data Engineering (Associate and Senior), Big Data Analytics (Associate and Senior), and Data Scientists (Senior and Principal).

    What is the DASCA mission and goals?

    DASCA is active in 183 countries, and seeks to elevate the quality of analysts, engineers, and teacher via proprietary education frameworks and certifications. For Big Data professionals, DASCA provides third-party credentialing and through DASCA-EKF prepares them for the demands of today and tomorrow. Organizations and recruiters benefit from a shortened hiring timeline and a reduction in onboarding costs due to the confidence that a DASCA certification provides. By selecting a DASCA credentialed candidate, organizations can be confident that they are on-boarding a qualified data scientist, analyst, or engineer. For educators, DASCA assists in developing high-quality educational content that meets the demand of today’s employers. Additionally, certification asserts that a program is meeting the high standard required for modern data science.

    QualiFLY Q1 vs Q2, what is the difference?

    DASCA’s QualiFLY program eases the minimum requirements and shortens the certification timeline for graduates of qualified programs. QualiFLY is particularly advantageous for recent graduates seeking to add credentials, allowing the applicant to bypass the ‘years worked’ requirement. Additionally QualiFLY candidates are able to save up to 15% on DASCA certification-related fees.

    QualiFLY has two classification categories – Q1 and Q2.

    Q1 institutions have previously completed the recognition process and are fully accredited DASCA institutions. Q2 institutions are undergoing the DASCA-recognition process, and upon certification move into the Q1 tier. Q2 is effectively a means to communicate academic programs undergoing certification.

    In conclusion, DASCA provides students, employers, and educators with a standardized education and certification platform intended to improve the quality of data science around the world. Certification is available in 183 countries, and through QualiFLY increasingly more institutions are providing opportunities for students to experience the benefits of DASCA. For more information, please visit DASCA.