Data Science Programs: The Best Guide for Students Looking for a Degree in Data Science

Welcome to DataSciencePrograms.org - we've researched the web to assemble a list of Data Science Programs and including them in our database and on subsequent pages. As of mid-2020, we have gathered over 500 universities with a data science degree at all levels and over 830 different data science programs, with the Master of Data Science degree being the most popular.

Here is how to best use our website: Start with some of the top pages on our site to learn more in-depth information on data science programs and related sub-fields, or view schools with a data science degree, by state. We separate out online vs. campus based programs on each state page.

If you're considering data science career, check out the Data Science Career Guide.

If you know you want a data science degree: Consider reviewing the most comprehensive list of schools with an online data science program. We've reviewed over 130+ different online data science (or related) programs - the most on the web.

🗪 What is Data Science?

Data science is a relatively new realm of data interpretation and manipulation that has found its place within current society. This field may require upcoming professionals to have a strong mindset in computer programs, statistics, probability, and common business-oriented skills such as communication and growth.

Students that might be interested in pursuing degrees in this field may enroll in degree programs that include a subtle blend of all of these topics, as well as courses that outline methods for using data to create helpful plans or programs for businesses or individuals.

The DataSciencePrograms.org team has researched the web, hand-gathering a list of Data Science Programs and including them in our (MySQL) Database. So far, as of early 2020, we have gathered over 500+ universities with a data science degree at any level (Master of Data Science Degree is the most popular). Contact us at schools (at) datascienceprograms.org if you notice any missing programs that we should consider. Below is an aggregated sample of the programs in our database.

Data Science Programs

Data Science is a booming industry - the collection, integration, and utilization of 'Big Data' is a relatively new area of study for those interested in a combination of mathematics, statistics, technology, and business application.

Companies around the globe are using data to make better decisions. Combined with statistical analysis, empirical evidence, artificial intelligence, algorithmic decision-making, predictive modeling, and business sense and intelligence, data-driven decision making is becoming the norm, rather than the outlier.

# Data Science Programs

There are a number of different types of data science degrees available at US colleges, and that number is growing every day. Most people are familiar with data science and statistics, but areas such as business intelligence and data analytics is becoming increasingly more popular. Here are some of the top degrees available in data science:

You might consider a featured online data science degree (some are below that are currently accepting applicants).

Featured Online Data Science Programs

The Origins, Utility, and Future of DataSciencePrograms.org

The issue with Data Science and traditional higher education is that there is no consensus on what should make for the best data science degree. What students should study - at any level - is not agreed upon (at this point) by a centralized accrediting body. Many industries in healthcare (CCNE), business (AACSB), and technology (ABET) have a program-specific accrediting body that ensures accredited universities are implementing the same coursework, classes, and areas of study.

LinkedIn Logo

Data Science is an Emerging Job for the Future According to Research from LinkedIn

According to the 2020 LinkedIn Emerging Jobs report,

  • "... Artificial intelligence and data science roles continue to proliferate across nearly every industry... Data science is booming and starting to replace legacy roles. Unsurprisingly, data science is a field that is seeing continued growth on a tremendous scale, but our data shows data scientists may be augmenting responsibilities traditionally done by statisticians as some industries, like insurance, gear up for the future."

In the 2019 Emerging Jobs Report by LinkedIn's massive amount of data, data science and data engineering are two of the top jobs they expect to emerge in the near future. According to the report:

  • "... Data science is a field that is seeing continued growth on a tremendous scale, but our data shows data scientists may be augmenting responsibilities traditionally done by statisticians as some industries, like insurance, gear up for the future."

- LinkedIn Emerging Jobs Report, 2019 and 2020 (source)

🗈 Research on Data Science Programs

Unfortunately Data Science does not have a singular, centralized database for this site to base its degree program listings on. Every data science program on this website was found by people-driven research.

This feat took hours upon hours of research - and we know we are not done yet. DSP was founded in 2018 and is a new project with a big vision. If you have or know of a university that has a Data Science degree offering at the following levels, email schools (at) datascienceprograms.org and we will review as soon as we can.

Data Science Integrated Into Daily Life

If you are curious about how data science plays a role in the everyday function of society, you should look at some of the most recent technologies.

Predicting Consumer Behavior

Predicting Behavior with DataMovie streaming services and social media outlets use data scientists to help create programming that is suggestive, predictive, or even personalized. Research into the behaviors and searches of consumers can provide information to scientists that is helpful in making technology more convenient and user friendly. Using research and algorithms, data scientists can make current programs more efficient and catered to customers' needs.

Business Analytics

Data Science Degrees for BusinessFrom the business outlook, data scientists can also assist in making relevant business decisions through research of current and past data associated with the business's practices. As an example, data scientists for business can review statistics and data related to business practice to sort through important issues or problems with everyday functioning.

As a result of this research, these professionals can implement programs or solutions that can make everyday business more fluid and profitable for their organization.

Other Industries

Data science is not limited to just business and IT. Having the skills to analyze data and provide solutions can be useful in any area that uses information to make the customer's experience better. Healthcare, statistics, advertising, and even sports are some fields that can benefit from the expertise of learners within this field.

Read more below to see what our educational experts have found regarding degree programs in data science.

Are There Online Data Science Degrees?

The short answer is yes. One of the best options about the area of data science is that many programs are offered entirely online. A large majority of upcoming college students are considering online degree options, due to the convenience and flexibility of this option. For students, online data science programs can allow for more independence while working through courses, which could increase awareness and self-motivation for future careers.

Students might find that the time it takes to complete online degree programs in data science depends on several different factors. Students that have enrolled in online programs may be able to attend their courses more frequently, since they can report to their class at their own convenience. Combining the responsibility of work and education can also have a direct impact on how quickly students complete their program online.

Why Data Science?

Data science can touch on many different specialized fields, but can also be very broad in its application. If you have an interest in statistics or data analysis, this is definitely a career field to consider.

Degree programs that focus on data science can be found under many different names. Students that are interested in the IT field may enroll in programs such as Data Analysis or Information and Data Science to help encourage growth within this field. Students interested in business can also enroll in programs that are specific to their needs, such as Business Intelligence, Finance, or Marketing.

Data science has been a trending career choice for professionals that are looking for a new industry, but it can be a confusing field to explore. When looking up opportunities, there are various positions such as "machine learning engineer" and "business analyst," and even a general "data scientist" that requires years of experience using programs such as SQL and R. Here are some frequently asked questions from those that are curious of what they can do with a data science job or gain higher education to break into the field.

What courses do you usually take for a Masters in Data Science?

At the Master's degree level, courses will shift from foundational computer science topics to directly working with big data. There should already be experience or familiarity with high-level mathematics, statistics, and decision methods and modeling. Students should also have a basic understanding of how to use popular programming software, but many institutions will provide this and other education if necessary.

Some of the courses may be available at different educational levels, but will focus on different specific topics. For example, programming courses within a Bachelor's degree will emphasize the basics of how to write code and run programs in a developmental environment. At the Master's degree level, there will be topics on how to create specific programs using library packages like NumPy and Scikit-learn.

Data Wrangling and Mining

The process of extracting important data from unstructured and structured sources, preparing it for analysis, and ensuring that the information meets quality control is all part of data wrangling. Data mining is a common term when talking about extraction, and it can mean collecting and transforming this information into a way that can benefit the organization.

Mining can be an automatic process that is able to sort information and detect patterns, but it may also be referred to the analysis process where experts will clean the data and determine patterns and find valuable information. Cleaning data means eliminating incomplete information and removing outliers that can skew data in a different way.

Architecture courses will also be in this category, which is the foundation of where the cleaning and analysis process is at. Students will learn different platforms and applications that they may utilize in the future. It is important for an organization to provide ease of accessibility for their information instead of having it separated in different areas.

Data Visualization

One of the most important aspects of data science is being able to find what is the most important in a slew of information that is available and being able to tell a story about these findings. Professionals that do not have exposure to the terminology of programming and database warehouses will not be capable of identifying this important information.

There are different types of data visualization that is both learned and that can be pursued as a profession. One is more geared toward other professionals in information technology that can use these visualizations to interpret their own conclusions, such as business analysts. Data visualization for higher-level employees or a group of people may require to prove a point, which means these stories should be persuasive, but follow ethical and legal rules.

Machine Learning

Perhaps the largest benefit to society's reliance on big data is the capability to automatically discover trends and organize information. As more user input is being collected, such as searching for information online, making a purchase with a credit card, and uploading images on a cloud-based server, companies are gaining valuable information to improve the goods and services they offer to consumers.

Having this capability will expand into the improvement of recommendation systems when streaming content and self-driving vehicles. Along with its great benefits, people have become more aware of data privacy with prior ethical breaches from organizations and government agencies. Courses will also explore the importance of valuing this privacy and also how to protect sensitive information from unauthorized access and data breaches.

Python and R Programming

Regardless of what type of profession is being pursued in data science, there should be at least some familiarity with computer programming when working with information. Two of the most popular programs in the field are Python and R. Both are free, open source, and object oriented, making it easily accessible for students and they can get a better grasp at the concept of programming when compared to other languages.

Python is the widely-used program that many companies will require for professionals thanks to its capability of installing many different libraries, especially those that focus on scientific analysis. R is much more statistical-based and can provide more features for data visualization.

Probability and Statistics

Basic college-level courses in mathematics and statistics are generally a requirement to get into a Master of Data Science program. Once at this level, statistical courses will mostly consist of theory and advanced skills that can be obtained. Students will discover what research methods are best in their particular situation and the differences between them.

For example, quantitative and qualitative analysis may sound similar, but are completely different in concept. Quantitative research is working directly with numerical information, or data that can be transformed into digits and can be easily organized. Qualitative research is finding the reasoning behind the information that is obtained, such as why customers purchased more of one product than a similar product.

Bachelors Degrees in Data Science

Data Science Degree ProgramsIf you are new to the college scene, the bachelors level is probably where you need to start your journey into data science. You may find that there are numerous data science-related majors and minors available in the college world for you to choose from. This type of degree can be standalone, or partnered with other topics such as computer science, information technology, and business. Depending on what interests you, you may find a blend of degree programs that is more suitable for the type of career you are searching for.

During this bachelors program, you may take courses that can help sharpen your skills with data analysis. Courses such as Java Programming, Computer Architecture, and Fundamentals of Data Structure are common at this level, since you may be required to know more about computer information systems operate and their popular programs. If you choose a program that is related to business, you may also see courses that are relevant to the business environment. Most students can complete their bachelors program in around 4 years.

Masters in Data Science Degrees

If you already have an existing bachelors degree, you might be more interested in pursuing careers in data science throughout the acquisition of a Masters degree in Data Science. These programs typically provide more focus into the actual application of data science, which can help students become more familiar with the methods and analytical properties associated with careers at this level.

The Masters in Data Science is the most popular program in the area that is offered online. Consider taking some time to read the ultimate review guide to online Masters in Data Science programs. In our guide there are over 130 schools reviewed!

Most students can complete this masters program in around 2 years on average. Throughout this program, students may take courses such as Collaborative Problem Solving, Advanced Statistics, and Reproducible Data Analysis. Students may also have the opportunity to engage in individual research projects that can test their abilities in data collection and analysis.

Doctorate Degrees in Data Science

At the highest degree level, data science students can provide research-oriented information and expertise to businesses in need. The goal of the Ph.D. program in Data Science is to create scientists that can transform large quantities of data into useful information for organizations in need. Throughout this program, students may have the opportunity to dive deeper into the analytic aspects of data science, and possibly even take part in an intensive practicum throughout the duration of their degree program.

The courses that students are likely to see throughout the Ph.D. program can include Probability and Statistics for Data Science, Inference and Representation, and Machine Learning. If you already have a masters degree, this program can be completed in as little as 2 years. Those coming from the bachelors level may require 4 years of additional time to complete this program. At the Ph.D. level, students may be required to maintain a 3.0 or similar GPA and pass an accumulative examination prior to receiving their degree.

Data Science Program Accreditation

Students that wish to pursue data science degree programs might be curious about their options for reliability and relevance. There is currently not a single organization that provides accreditation for data science programs specifically. However, the Accreditation Board for Engineering and Technology (ABET) provides accreditation for educational institutions that offer degree programs related to computer technology, analysis, and engineering.

AACSB for Business Analytics

AACSB MBA Programs in Data ScienceSome minors in data science can also be found within business degree programs, which should hold accreditation through the Association to Advance Collegiate Schools of Business (AACSB). Regardless of which path you choose with your data science degree, you should ensure that your program has received the right type of accreditation prior to enrolling. Programs that promote data science specifically may be accredited by either regional or national accreditation – which can have an impact on transfer-ability and reliability.

Students that are planning to enroll in a data science degree program should ensure that the program has received regional or national accreditation prior to enrolling. There are also some programs at higher degree levels that are accredited by the Higher Learning Commission.

An accredited degree program can help to teach you the right topics, provide you with the right skills, and possibly prepare you for highly influential careers in the field of business and technology. Most universities provide accreditation information on the degree program’s page, while others may require a more in-depth look into the university’s accreditation information.

Types of Data Science Degrees

Data Science Degree

Data Science degree programs can be applicable to many different career fields. This program includes topics that are relevant to computer science, statistics, business analysis, and even specialized fields such as healthcare. The purpose of this degree program is to help students use data to create information and helpful solutions for the businesses in which they work. While in this program, students may learn more about the methods required to analyze data sets in order to gather information for reports or programs.

Business Analytics Degree

The competition and desire for growth present in business is fed by the use of analytics. This degree program is increasingly popular for upcoming business professionals and MBA earners. This type of program may require students to look closer at the data and analysis that goes into creating helping programs, enticing more customers, and driving in more profit for businesses. This program can be found in many different forms, including those partnered with MBA and Information Technology degree programs.

Information and Data Science Degree (MIS)

Information and Data Science degree programs combines aspects of both information technology and data science into their curricula. These programs can help students gain a better understanding of the computer software, programs, and methods that are present in today’s society, as well as the use of data analysis to companies and consumers alike. These programs can possibly lead to careers involving computer program creation, improvement, and implementation.

Information Management Degree

Specialists that have earned a degree in Information Management may have many diverse skills that are helpful in different settings. This degree program may provide students with knowledge of information usability, business administration, knowledge maintenance, and information system design and improvement. The degree program in this area can help to create effective leadership professionals within the IT and business fields as well. This is a great option for students that wish to use their data science skills within a technological environment.

Data Analytics Degree

Data Analytics Degree Programs
The use of Data Analytics can go well beyond just business and information technology. Having the skills necessary to provide helpful analytical data can be applicable in a very wide array of settings. Many organizations in society seek out students of data analytics to help make decisions that are helpful to the people in which they serve, such as through education, engineering, and healthcare. This degree program is great for people that want a wider reach than standard data science degree programs.

Business Intelligence Degree (BI)

One of the most important components in business is decision making. This process requires professionals to have strong skills in analytics, data interpretation, and business practices. This degree program can provide students with skills that can help them become a vital piece of an organization. Students may take courses that relate to data influence, financial awareness, and ethics throughout their experience in this program.

Applied Statistics Degree

Applied Statistics can help students indulge more into the numerical aspect of data science throughout their career. This program may include topics such as predictive analysis, biostatistics, and data mining. Students that take part in this program may have the option to pursue careers in business, healthcare, or information technology in their future – making this a great opportunity for those that want diverse career choices after graduation.

Finance Degree

The field of Finance is constantly looking for professionals that have analytical and data mining skills for their positions. With a degree in finance, you could possibly take courses that can help you better understand the fundamentals of appropriate financial practices for business, healthcare, and even IT. The courses that you could take in this program may include those that outline accounting, consumer spending, and tax reporting.

Geospatial Science Degree (GIS)

Geospatial Science programs are great for students that want to have a well-rounded expertise in areas such as society, ecology, the environment, and geography. Experts within this field can contribute to what society knows about these different areas and how the information found can be used to better the human experience. Some fields that are influenced by professionals in this field can include engineering, geography, and sociology. Course work for this program may include topics such as topography, physics, information technology, and surveying.

Data Science Q&A

There are many questions when it comes to an emerging field like data science. We’ve highlighted some of the top questions and have provided answers to each, below.

Which online course is best for data science?

A major benefit to studying a data science discipline online is the natural requirement of working with computers and networks in a professional setting. It is nearly impossible to enter into any data science career without being proficient in computers, working with various programming languages, and being able to query information from a database.

Numerous courses in a typical data science educational program will be perfect for a virtual setting.

Top Data Science Courses

Course Description
Programming courses Programming in Python and R require hands-on experience with a computer in order to install various packages that run code and create data visualization. In many courses, there will be a need to access information online, such as querying specific data from a cloud server.
Open Source software For data scientists building programs that are able to extract and manipulate information, it is important to store these in a repository, such as GitHub or SourceForge. Courses that explore these concepts will already require the student to create and access an online account.
Online courses All technical activity in data science can easily be explored in an online course, and in many cases, they are available in separate online courses from various institutions and the vendors that create these hardware and software tools. Any individual that is looking for certification in a specific area will likely have access to online course material prior to taking the examination, which is also completed online.
Enterprise software courses In an enterprise setting, there will be constant communication with other employees in a virtual environment. Completing higher education online will already give graduates the option of working in this capacity and holding the necessary soft skills to thrive in their profession.

Studying online vs on campus: what is the difference?

While a number of institutions offer a full data science education online, it may not be the ideal answer to all students. Learning in a more controlled environment and having direct contact with faculty and students may be a preferred method of education. Those that are easily distracted in a home environment or do not have the technical capability to succeed away from campus should enroll in at least a hybrid format.

Some of the less technical courses in data science may not have too many advantages in an online setting. For example, when a course explores theory behind algorithms that are used in machine learning or organizations that have adopted data science techniques into their business model, there is not much coding or other specific uses for a computer.

For education in working with large enterprise systems or protecting servers from data breaches, university laboratories often have great resources located on campus for a more simulated experience. They can offer a controlled environment where a student can learn penetration testing, which is a simulated attack on a network that can be analyzed for vulnerability concerns.

At the doctorate level, these programs are frequently longer to complete and require dissertation defense in front of faculty members. Even if the institution offers an ability to pursue the degree online, there are typically on-campus residencies that are required. In many cases, students looking at this level of education should live in close proximity to the school or at least be able to meet residency requirements of an online program.

Where can I study data science?

With the growing addition of data science programs at various education levels online, there is no limit on where students can pursue a degree. Many popular universities offer a full degree at the graduate level online with no requirements for campus visits to convenience students that are also continuing as a working professional. Undergraduate programs are less popular due to the amount of credits needed, but are still available at various institutions and community colleges across the country.

1. Traditional Institutions

For individuals that value the physical experience, a data science education can be earned in all 50 states throughout the country. Many popular institutions have added a Data Science discipline over the years with added concentrations for students to specialize their education. If a favorite school does not have a specific Data Science discipline, there may be an emphasis in a related field, such as Computer Science, Engineering, Business Analytics, and Applied Statistics.

As an example, the state of California has numerous educational opportunities for a Master’s degree in various disciplines. The University of Pacific in Stockton has a Master of Science (MS) in Data Science. The University of California in Irvine has a Master of Science in Business Analytics.

Some institutions will have multiple disciplines available where students select what type of profession to pursue. At the University of San Francisco, a MS in Health Informatics is geared toward students looking at a career improving the quality of health care and offering new treatment plans with better patient information. At the University of Southern California, a MS in Business Analytics will give students the skills to analyze valuable data and explain these findings in non-technical terms to other departments.

Data science itself may be an added concentration to different focus area of study. Students can select a Master of Business Administration to receive theory and leadership skills for a higher position at an organization, and a concentration in Data Science will provide added exposure to collecting, maintaining, and analyzing big data.

2. Program Certification

When looking at various institutions, it is important to review if the school or program has been verified by an accreditation association. For example, the Higher Learning Commission certifies educational experiences across much of the North and Midwest regions of the United States, and the Southern Association of Colleges and Schools reviews institutions in the Southeast and Southwest-based states.

Having regional certification is often necessary for the potential of transferring credit if the student is considering changing schools to pursue their education. This can also verify to the student that the education they are receiving will be optimal for their new career in data science.

3. Online Bootcamps and Academies

In lieu of studying data science at a university, there are a growing number of online-only resources available. Resources such as Coursera, Udacity, and Datacamp are becoming popular to customize a student’s education or to become an expert in a certain category. Individuals that are only looking for an education in learning Python or machine learning can pursue that specific need at a cheaper cost.

These programs are frequently referred to academies or bootcamps, and some are sponsored by or have faculty members that are affiliated with a certain institution. Similar to taking an online program at a university, there is flexibility when it comes to completing courses. Bootcamps are structured around a rigorous fast pace for individuals to learn various topics in data science in a quick time frame.

Academies can also provide a quick education to receive a certification through a vendor. For example, Coursera has the IBM Data Science Professional Certificate with education in Python, SQL, and data visualization. It is free to enroll in order to try it out, but requires a monthly payment to complete all the courses and receive the certification.

Does data science require coding?

Learning a programming language is not necessarily required for a data science position. For example, most business analysts have a duty to make the information they obtain clearer to other professionals they work with. They are essentially the communication path between the information technology department and other departments of an organization.

Why Coding is Not Necessary for Everyone

Having programming experience may help in some cases for business analysts. They can check algorithms or analysis methods to determine their accuracy when creating data visualization and reports. However, it is more important for them to have a full understanding of data they are working with and offer strong communication skills with other workers.

As data science becomes more popular, there are already many algorithms and programs written for professionals that have minimal experience in coding. This means they can adapt a particular program by changing out a few key details that makes it work in their system and get the information they need. Assuming this work is credited toward the right professionals in the field, such as forking a project in GitHub, they do not have to worry about attempted plagiarism.

Software is being adapted for professionals that do not need to have a large skill set in coding. Tableau, a popular data visualization software, continues to implement a drag-and-drop system when organizing information instead of coding. Machine learning will also make this process easier, simply asking for what the user wants to know and automatically adapting that through coding without manual input.

Ultimately, the need for coding depends on what position is being pursued in the field of data science. As a general rule of thumb, it is recommended that the professional has, at the very least, familiarity with programming languages such as Python, R, and SQL. These languages create algorithms that organize data, query that information from a database, and create visualizations to better interpret information.

Why Coding Skills Are Important

For the best career prospects, individuals should gain experience in some type of coding. Working with data sets in Python and R under various developmental environments will require professionals to gather information from a variety of sources and clean their data tables with parsing out specific information that they need for analysis. They may not need to build a program that gathers data from scratch, but manipulating these findings will often require additional lines of code.

Having programming skills are mandatory when looking at higher, more advanced positions in data science. Creating the architecture for working with big data or becoming a machine learning engineer will require the professional to input and review code from other workers. Getting into a better profession is not only more rewarding, but it can also pay significantly higher than a traditional business analyst.

As a bottom line, having the ability to create programs on various applications will give that individual a better selling point when applying for jobs. Being comfortable in any environment and holding a multifaceted skill set will be more attractive to companies that are relying more on technology to gain a competitive advantage against others or to improve the goods and/or services they offer to clients and customers.

Are online data science degrees worth it?

Prospective students must consider a number of factors when researching and reviewing online data science programs. Some individuals may have preferences on how to receive their education, such as learning within a more controlled environment at a university and in classrooms. They may be more productive following specific schedules and avoid any potential distraction from other obligations when studying.

Benefits for Online Education

Online learning provides much more flexibility for students, but there is more responsibility for them to study on their own. Especially at the graduate level, online programs are geared for students that are multitasking with their professional career or other life obligations. There is less commitment to meet for class lectures, and in some cases, they are not required at all.

Two formats that generally come when pursuing an online program is synchronous and asynchronous. The synchronous setting will require students to meet for lectures at specific times and/or activities that must be completed, such as participating in class discussion or message boards. In addition to activities, all assignments, quizzes, and examinations will be completed in a structured format.

Asynchronous work means that the student has flexibility to view lectures and complete assignments at their own pace. When studying at an institution, it is very common to have the ability to access course lectures and other material at any time, but students must still follow deadlines for assignments and examinations.

Other potential benefits for online education is extra customizability for the curriculum. The ability to choose the amount of credits provides automatic enrollment as a full-time or part-time student. In programs that do not require students to follow the same structure as the on-campus format, they may be able to complete the degree in a quicker pace, especially if there are transfer credits applicable.

Institutions offering online programs also open up availability to more potential students. While some programs may require students to live in a specific region, many data science opportunities will be available to everyone across the United States with no requirements to visit the campus. Those enrolled will have the same virtual resources students at the campus have, and they graduate with the same diploma.

What to Consider for Online Education

One of the biggest problems students face when pursuing an online program is the ability to complete all courses and gain the degree. It is very important to set a schedule and have the same amount of dedication toward the program as taking it at the institution. Procrastination with the freedom to complete course content at any time can lead to worse results and dismissal from the program.

There are also technical limitations in rural areas across the United States. Many institutions are located in areas that already have high-speed internet access and all sorts of resources for students to succeed when preparing for their career. Rural communities may lack this availability, so it can be tougher for students to access large on-demand video streams and work with massive data sets that require faster connection speed.

Some resources are also only available on the college campus. For example, there may be specific workshops that can enhance skills in communication and leadership that are not available in a virtual setting. Students on campus may be able to experience real-world scenarios when dealing with various data science tasks, such as identifying cyber security issues or working with information technology systems that are unavailable in their location.

Hybrid Education

In some cases, students may have the opportunity for a hybrid format of online and on-campus learning. This means that some courses can be taken from the convenience of a student’s home and others can be pursued on campus for a more concentrated educational experience. Programs that have identical course availability and are similar in both formats will usually have this option available.

There are many reasons that students may want to pursue this type of educational format. One is the ability to complete the program in a quicker time frame, taking some courses on campus during regular fall and spring semesters and online during the summer term. Another similar format is where students meet on a limited basis during the course (weekends, monthly, etc.) and completing the majority of the work online.

Some universities will not have this opportunity available even if they have similar programs with both formats being an opportunity. Students should review the program webpage or contact the institution to see if they can take courses both on campus and online.

What are some of the best data science programs?

When prospective students are reviewing different institutions and their data science programs, it is very important to determine its authenticity. There are a number of opportunists that will create a misleading educational service in order to take advantage of the growing field of data science. Always make sure that the program has some type of accreditation attached that verifies faculty members and the resources available for students.

Regional accreditation is the best form of authentic review for educational programs in data science, and in some cases, institutions will only provide transfer credit form other regionally-accredited schools. Examples include the Middle States Commission on Higher Education, which reviews states and regions such as New York, Pennsylvania, Maryland, and Washington DC, and the Northwest Commission on Colleges and Universities, which observes the states of Alaska, Idaho, Montana, Nevada, Oregon, Utah, and Washington.

Here are some institutions with popular data science programs in terms of enrollment and have been regionally accredited by one of these established agencies.

Colorado State University

Two different concentrations – Data Science or Statistical Science – can be pursued when taking the Master of Science in Applied Statistics through the College of Natural Sciences. This degree can be pursued either online or on-campus at the Fort Collins area. In order to get into the program, students should have a Bachelor’s degree with coursework completed in multivariable calculus and linear algebra.

Students with full-time enrollment can complete the program in as little as one year, and up to 31 credits are needed to gain the degree. All Computer Info Systems-type courses are completed online regardless of format chosen. Examples of courses taken within the program include Statistical Consulting, Qualitative Reasoning, Applied Bayesian Statistics, and Applied Data Mining and Analytics in Business.

Purdue University

The Master of Science in Business Analytics and Information Science prepares students to solve business problems with data-driven analysis and machine learning capability. 36 credit hours are needed to complete the program, and courses are divided up into core technology, business foundation, restricted electives in information management and analytics, and free electives to customize the program.

This program is ideal for students that are looking for business courses in addition to data science. In addition to the standard curriculum, students can add the SAS Business Analytics certification to become proficient with the vendor’s software capability. Completion of a Bachelor’s degree with a 3.0 GPA or higher and Graduate Management Admission Test (GMAT) scores are recommended for the application process.

University of California-Berkeley

An online Master of Information and Data Science can be earned through the School of Information and it is accredited by the Western Association of Schools and Colleges. Topics that students will cover included machine learning techniques and the ethical issues behind data science in criminal justice, health care, politics, and more. 27 credit hours are needed to complete the program, which can fully be completed online.

There are various pathways that students can choose from to complete the program. An accelerated path is geared toward full-time enrollment with an opportunity to finish the entire curriculum in just one year. The standard path and decelerated path allow a longer time frame to complete the degree, which is ideal for working professionals that will be studying at the same time.

University of Syracuse

The Master of Science in Applied Data Science is an online program through the School of Information Studies. Students will learn how to solve complex analytical problems in order to improve any business they pursue. Specifically, they may develop strategies completely based on information they collect and analyze, work with higher-level employees to adapt these new findings, and fully understand ethical and legal issues when working with sensitive data.

36 total credit hours are needed to complete the degree, which half of the curriculum will have core courses that all students take, and 12 to 15 electives to customize the program. There is a set of analytical courses that emphasize how to manipulate accounting, marketing, and financial information. A final student portfolio should showcase what they have learned throughout the program and is reviewed by the faculty prior to graduation.

Should I learn software like R or Python?

Data science has two programming languages that are most popular within its field: Python and R. Python is a general purpose programming language that has been around for over 30 years. It is designed to be easily read with an uncluttered format and has a number of different libraries that can be implemented.

Python

Python is object-oriented, meaning that numbers, strings, classes, and more can be defined with variables. Importing libraries such as NumPy, Pandas, and Scikit-learn add further opportunities with Python. These are the most commonly used libraries in data science as they analyze data, convert it into different visualization charts, and utilize machine learning capability.

There is capability to program in a traditional shell, but more advanced projects are easier to complete in an integrated development environment (IDE). Another benefit with Python is this offers customizability in that area as well. Some examples of popular IDEs are Eclipse, Visual Studio Code, and Spyder, and many of these options are free with their own unique advantages.

Eclipse is popular for programmers that are already familiar with other languages, such as C++, and using the Pydev plugin allows this IDE to be compatible with Python. Visual Studio Code is a Microsoft product that has many extensions available, but struggles to handle files with a large amount of code. Spyder has a number of handy development tools built in to make the programming process easier, and it is one of the popular IDEs in data science.

R

Simply known as R, this programming language specializes in statistical features. As one would expect, there are additional features in analysis and visualization techniques that Python cannot accomplish. This is why data scientists will frequently have the skills to use both programming languages in their profession.

Similar to Python, R is object-oriented and has many libraries that can be attached to it. In fact, there are thousands of packages available to satisfy many types of analysis criteria. Reviewing programming data in an R is not as clean as Python, and there are differences in the vocabulary and operations used that can be confusing for beginners.

RStudio is the most popular IDE for R programming, and is available in two different formats. The first is a desktop version that is completely run through the application, and a server version can be accessed through a browser. Using the latter version gives a flexibility benefit of working on programming tasks with a different device and multiple users can access the information at one time through the server.

Using Both Programs

Both programs are open source and have their advantages in the data science field. Python is the more widely used language due to its relatively easy learning curve and customizability. R is the program of choice if there are additional needs in statistical analysis and data visualization techniques.

For professionals that are going to use both languages, a recommended open-source distribution platform is Anaconda. This is not an IDE, but a collection of programs that feature the Spyder IDE for Python and numerous other tools that are handy for programmers. Instead of installing libraries and packages separately, Anaconda conveniently provides these options in a single installation process.

Is data science hard?

Data science is a challenging field to become a professional at, and it can be extremely intimidating for those that do not have much experience in computer science and information technology. There are numerous resources that are available, and it can be easy to get lost in the various programs and terminology that is needed to thrive in a career.

However, this discipline can be rewarding in many different ways. Professionals are able to find information that helps improve the company’s understanding of how to create a product or better serve their clients in a particular location. It can also be educational to observe different trends and finding out just how much information can actually be extracted from various websites.

Data Mining

Without question, one of the hardest aspects of data science is the preparation and cleaning of information. There will be many situations where the professional run into data sets that are incomplete and must determine how that will impact their results. Finding that particular data may require tweaking the algorithm they use that extracts data from their source, or they may have to pick a different source altogether for further information needed.

In order to extract specific information and make it usable in the transition to data visualization, this data must be cleaned. Cleaning data requires the programming knowledge of structuring charts and tables, creating forks with Lambda functions, and removing any unnecessary formatting, punctuation marks, and anything related to make it into a usable value. Outliers must also be detected and removed to keep the information from being skewed in a specific direction.

Preparation of data and ensuring that programs are written accurately will usually take the professional’s bulk of time, which limits their ability to analyze data efficiently. This becomes a greater challenge when they have to create an algorithm that extracts data from scratch. Luckily, with so many programs available online, professionals have the ability to take those same programs and adapt them to their needs.

Never-Ending Flow of Information

One of the pitfalls of big data is there is simply too much information coming in for organizations to keep up with all of it. Almost everything can be tracked, organized, and analyzed, from what is being posted on social media websites to all the queries of information being searched in Google. Combine that with the need to prepare and clean data, and it can be extremely overwhelming.

The evolution of machine learning will help in the process of organizing information, which can also be difficult to venture into. There are different types of ML, such as supervised learning that is able to make predictions after seeing the same thing over and over again. Unsupervised learning making educated guesses on what a particular person would like to watch next on a streaming service or purchase online at Amazon.

Ethical Concerns

One aspect of data science that may not get much attention, but adds to the difficulty of the profession, is ensuring that the process remains ethical and legal. Data privacy continues to be a growing concern for a number of consumers that do not want sensitive information shared among organizations. Any information gathered from unauthorized access is illegal.

Algorithms also should not be built to take advantage of individuals and must be kept in check to avoid potential bias. For example, programs that are automatically geared toward accepting or declining loans may give a false positive to certain applicant that may or may not be qualified. This is why programs should constantly be reviewed and updated to determine if accurate results are being developed.

Which online data science certification is best?

Certification is the quickest and most convenient pathway for students to receive data science education. These are typically more focused toward a specific position or gaining competence with particular software and hardware in the field. A wide variety of certification is available through specific vendors, such as Microsoft, Google, IBM, Cisco, and more.

While certification requires less of a time commitment and can generally be completed within a year, completion rates are low for this pathway. Students are responsible for maintaining their own studies and preparing themselves for the certification exam at the conclusion of the program. These examinations will often accept just one attempt, and students will need to repay and/or wait to take another exam.

All certifications will have different educational requirements and prerequisites for their students. The curriculum should be reviewed to determine if more fundamental courses in data science are needed prior to enrollment. Some vendors may include foundational topics prior to specific education into using their hardware and software in the data science field.

Here are some vendor certifications that are very popular within the data science profession. For those looking for advanced certification may consider the Data Science MicroMasters Certification, which is distributed by UC-San Diego and acts as an online Master’s degree with nine courses to complete. Courses include fundamentals in machine learning and Python, using Python for probability and statistics, and using Spark for big data analysis.

IBM Data Science Certification

One of the most popular vendors to gain data science certification is through IBM, which provides an entry point into a career in data analytics and machine learning. Students will gain the proper abilities to query information in a database and access this information through a variety of different tools and applications. There will be specific skills gained in using Python and the libraries of Numpy and Pandas.

This certification generally takes 10 months to complete for students that have an ability to work on the program for four hours each week. There are no prerequisites and set times for courses, so this provides maximum flexibility for those looking to jump into data science education immediately. It is helpful for students to be familiar with computer science, calculus, and linear algebra.

SAS Certified Data Scientist

This is an online program that provides various computer science topics over a five-exam period along with the 18 total courses needed prior to gaining the certification. Students will be able to complete real-world scenarios and case studies to further their academic education. Other certifications include Advanced Analytics Professional and Big Data Professional, which are both required to hold prior to pursuing the Data Scientist certification.

Another related certification is the Data Curation Professional through SAS, which offers four training course and a voucher for the certification exam. This is ideal for individuals that are looking to gain experience in gathering data from various sources and preparing this information on software such as Apache Hadoop. They will gain familiarity working in the Hadoop environment and other data management tools from SAS.

Google Cloud Professional Data Engineer

Another pathway to gain certification is by becoming an expert data engineer through Google Cloud. In addition to being one of the high-paying opportunities in the field, having this designation on a professional resume will make the candidate stick out and they will gain networking opportunities through Google’s certification directory.

The examination itself does not require prerequisites, but the applicant should have at least three years of experience in data engineering and at least one year of working on the Google Cloud platform. This can be completed online assuming the applicant meets testing requirements or at a test center that is proctored at that location. Google provides some training material and practice exams for preparation.