Data science knowledge and skills are some of the most valuable credentials for the modern workforce. Companies and individuals can sign up to take DS courses developed by top universities, technology companies, and boot camps through an online platform. From specializations in Python, SQL, and machine learning (ML) to general introductions to these topics, the range of courses offers something for any student or IT professional.
The best data science courses are available online through course-hosting platforms and provider-platform hybrid vendors.
The latter include courses from Udacity, Datacamp, and Udemy, where certificate candidates take the course through the certifying organization’s website. Course-hosting platforms like Coursera, edX, and LinkedIn Learning provide notable schools, companies, and individuals a space to build courses and certify candidates.
For both types, the leading revenue model for certification providers is a monthly subscription offering access to the course as needed. Alternatively, a handful of the top DS courses are available for a one-time fee with unlimited access to the class on a longer-term schedule.
Learn for Free: Many of these courses are auditable for free for candidates looking to avoid the price tag associated with a certificate. To earn the certification, candidates can pay at a later date and complete the needed coursework for completion. Alternatively, most course providers also offer financial assistance to interested students.
Platform: Coursera
Individuals with a foundational knowledge of data science can deepen their analysis skills with the Applied Data Science with Python Specialization course from the University of Michigan on Coursera.
Learners explore the python programming language through 5 courses:
Interested students should expect a pace of 7 hours per week for five months. They’ll pick up skills like visualizing data integrity, using natural language toolkits (NLTK), and conducting inferential statistical analyses.
Platform: LinkedIn
LinkedIn Learning offers the Become a Data Scientist certification through 12 courses that clock in at just over 20 hours for a general introduction to the data scientist career path.
Each course is anywhere between the foundations of statistics at 30 minutes up to a 4-hour course exploring terminology and enhancing data fluency. The average course length is just over an hour, with topics covering the everyday life of a data scientist, potential career paths, data visualization, big data, and data governance.
Learn more with: How to become a data scientist: A cheat sheet | TechRepublic
Platform: Udacity
Ten years after its launch, Udacity has a robust portfolio of courses for IT professionals and students, including the nanodegree program, Become a Data Scientist.
Though it shares the same name as the LinkedIn certificate above, Udacity’s nanodegree is not for beginners. Interested learners should have an existing knowledge of the DS topics, including skills in programming (Python and SQL), statistics, data wrangling, and machine learning. With the prerequisites met, candidates dive into software and data engineering, experiment designs, and finish with an open-ended DS project reflecting skills learned.
The Udacity nanodegree is available for a one-time fee, offering the most flexibility to complete the program. Candidates spending 10 hours a week expect to finish in approximately four months.
Platform: Coursera
Learners new to data science can get the proper foundation with the Data Science Specialization from one of the top research institutions, John Hopkins University.
In 10 courses, learners look at the entire data science pipeline to understand concepts like R programming, analysis, statistical inference, and regression models. With a suggested pace of 7 hours per week, candidates can expect to finish courses and the capstone project in 11 months. This length may be intimidating to some starters, but candidates committed to learning data will no doubt benefit from the world’s leading biostatistics instructors.
Note: Through a Coursera monthly subscription, candidates can expect to pay $429 over 11 months or save and gain access to the platform’s library of over 7,000 courses with an annual subscription to Coursera Plus for $399.
Platform: edX
The University of California San Diego’s MicroMasters Program in Data Science offers students four graduate-level courses through the top online course provider, edX.
At 10 hours a week, candidates can expect to finish the program in 10 months for a one-time fee. Along the way, learners will cover the mathematical and applied areas of data science. Skills gained by the course’s end include cleaning data, using ML models and Apache Spark, and making reliable statistical inferences. Program courses are:
Note: While there is no overall deadline to complete the program, candidates have 24 months to select UCSanDiegoX course sessions after purchase. When completed, the MicroMasters account for 30% of the full Master’s degree in DS.
Read more about Apache Spark with Best Data Warehouse Tools & Solutions.
Platform: edX
Through edX, Harvard University offers a Professional Certificate in Data Science, giving interested learners the knowledge base and skills to address modern data analysis challenges.
The HarvardX Data Science program’s nine courses include the breadth of topics for DS starters, covering:
Students become familiar with critical data science tools like Linux, GitHub, and RStudio to experiment with real-world applications.
The certificate program is self-paced with a suggested 2-3 hours per week of work; however, candidates can expect to complete the program in 17 months at this rate.
Platform: Coursera
For beginners looking to kickstart their data science, why not learn from an IT juggernaut with the IBM Data Science Professional Certificate on Coursera?
Through 9 courses, candidates entirely new to data science and programming can learn about open source tools and libraries, databases, visualization, statistical analysis, and machine learning algorithms. Learners get hands-on experience with IBM Cloud using the latest DS tools. At 4 hours a week, course takers can expect to finish the program in 11 months.
With applied learning an emphasis throughout the certificate program, candidates will become familiar with:
Note: In 11 months, the IBM program runs $429 with a monthly subscription to Coursera. Alternatively, an annual subscription will offer access to more courses and costs $399.
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Platform: Coursera
Not just a world-renowned brand, Google has a quarter century of experience attracting the brightest minds in data science – making it hard not to consider the Google Data Analytics Certificate on Coursera.
Google aims to prepare graduates for entry-level work in IT support, UX design, digital marketing, and data analytics. Friendly to beginning learners, the eight courses include the foundations of data, data-driven decision-making, data preparation and processing, visualization, and analysis with R programming. At 10 hours per week, candidates can expect to finish the program in 6 months.
Note: For six months, candidates can complete the course for $240 with a monthly subscription to Coursera. The entire 240 hours break down into 180 hours of instruction and 60 hours for assessments, labs, additional studying, and a capstone project.
Platform: Coursera
Interested beginners can join a group of 5 million learners with Stanford University’s Machine Learning Specialization led by deep learning expert and Coursera co-founder Andrew Ng.
Ng originally developed the program in 2012 and continues to update the curriculum to meet an evolving field, focusing on crucial ML concepts, tools, and the dominant programming language for AI applications, Python.
Unlike other specialized DS courses, Stanford’s ML specialization offers an in-depth look at deep learning without needing prior math knowledge or coding background. At 9 hours a week, candidates can expect to finish the program in 3 months.
Read more: Top 50 Companies Hiring for Data Science Roles | Datamation
Platform: Udemy
Another specialization in machine learning, Udemy’s Python for Data Science and Machine Learning Bootcamp, is just over 25 hours, split into 27 topics and 165 lectures.
Ideal for experienced developers or beginners with some programming experience, the Udemy bootcamp covers using tools like Spark, NumPy, Pandas, Matplotlib, Seaborn, and SciKit in real-world DS applications.
Other topics covered include interactive visualizations, linear regression, decision trees, and natural language processing to give learners a robust understanding of ML within data science. Students can gain a bundle of ML skills for under $100, sometimes costing a couple of thousand dollars.
Note: While the total length of lectures is 25 hours, this does not account for additional study and time spent working through applications offered in lessons.
Also read: Data Analytics vs DS: What’s the Difference? | CIO Insight
Data science studies volumes of data and the tools and methods used to store, manipulate, and analyze such data. Data scientists utilize data analysis, statistics, and, increasingly – computer programming – to understand and develop insights into volumes of structured and unstructured data.
Data science courses are popular for students and professionals looking to learn about and work with DS practices. The most popular routes are public-facing and online, which means students can earn their credentials or boost skills at their own pace without setting a schedule with a class and professor or enrolling in a traditional university.
Read more: Best DS Books in 2022 | CIO Insight
The spectrum of data science courses offers general introductions to highly specialized classes and MicroMasters covering several topics. General introductions often look at the full range of DS topics, while specializations dive into:
A newer field of study, data science emerged in an era where online education is increasingly available. Meanwhile, traditional college experiences for DS are limited to top research universities and quite expensive. Several platforms arose in the last decade to host and deliver courses to interested students and professionals.
Today, the top online course providers include a range of newer educational institutions known for software development boot camps, premier technology companies, and course platforms.
The sections below look at some of the most popular online platforms that host and facilitate courses for students, followed by the prominent companies offering credentials for proprietary and industry DS skills.
Also read: Best DS Certifications | Enterprise Networking Planet
The flexibility of online courses gives students plenty of freedom to study, practice, and move through lessons. Rolling courses with pre-built lessons and projects offer students the flexibility to complete each course when possible or demanded by course deadlines.
While the online route offers more flexibility than in-person coursework, candidates should still be wary of time limitations for one-time fee programs and monthly subscriptions for extended programs. Without a plan of action, where those program deadlines can be more friend than foe to finishing efficiently, learners can lose out on the opportunity to complete the program or waste money.
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Students or professionals looking for a more hands-on experience may appreciate the full-time alternative to taking individual courses or certificate programs with an accredited graduate degree. Data science is a newer academic discipline, but universities are eager to explore the field and produce the next generation of data scientists.
Notable DS graduate programs include:
As with most IT-adjacent fields of study, there are a lot of job opportunities and demand for professionals with data science skills. That demand isn’t going anywhere, making any credential, whether general or specialized, an asset on resumes.
So, where to begin? For new learners, start cheap by auditing one of the handful of DS courses for beginners. Students explore a range of topics related to DS and develop a general proficiency valuable to most professional staff. By auditing the courses, candidates can also choose when to pay for earning the degree and completing any necessary coursework. These introductions can inform a choice of specialized certifications for students looking to move further in data science.
Keep in mind the range of time commitments and choose an accomplishable program. Early blockers and overwhelmed feelings are regular for a complex topic, which makes choosing a one-time fee program a hefty decision. If considering one of these programs, ensure a plan of action to study, work, and finish the certificate in a reasonable amount of time.
There are plenty of specializations for budding data scientists hoping to develop their skill set. Depending on the DS applications, candidates might benefit the most by boosting their IT prowess using proprietary tools and systems with vendor-specific certifications.
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