There’s never been a better time to become a machine learning engineer. In addition to exciting job opportunities in the field of artificial intelligence and the chance to have a meaningful impact on sectors such as security, healthcare, education, finance, and entertainment, machine learning engineers are in high demand. ML engineers experienced 344% job growth from 2015-2018, and the Bureau of Labor Statistics expects the profession to continue experiencing high demand throughout the next decade.
As a highly technical profession, getting started in machine learning engineering takes work. Fortunately, many online courses and bootcamps have been launched in recent years to help newcomers and seasoned programmers learn the core skills needed to make it as ML engineers. Below we’ve curated some of the best paid and free courses on the subject — whether you’re a data scientist or analyst looking to advance your career, a complete newcomer hoping to get into ML engineering, or a professional wanting a career change, there’s a course to help you get started.
What’s the Best Machine Learning Engineering Course?
The best machine learning engineering course is the one that meets your needs. For example, if you’re a complete beginner who is trying to learn the fundamentals of machine learning and polish your skills in mathematics and statistics, then the best ML engineering course will offer a program that focuses on the basics. But if you’re a seasoned data science professional looking to master machine learning engineering, develop ML algorithms that result in accurate predictions, and work with smart robots, then an advanced program that fills in knowledge gaps would be ideal for you.
The Best Paid Machine Learning Courses
Paid programs are typically more comprehensive and rigorous compared to their free counterparts. Many paid courses also offer student support, career counseling, and, in some cases, mentorship from an industry expert.
Machine Learning Career Track (Springboard)
Springboard’s comprehensive Machine Learning Career Track gets graduates workforce-ready by covering foundational topics such as machine learning algorithms, neural networks, natural language processing, and computer vision, while also offering hands-on experience with deploying ML systems to production.
What We Like
Springboard’s self-paced bootcamp spans the basics of machine learning engineering through to advanced machine learning concepts. Learners develop an understanding of real-world ML engineering applications, work on portfolio-worthy capstone projects, receive one-on-one mentorship from an industry expert, and have access to career coaches and counselors who are invested in the success of every graduate.
Details (Cost, Curriculum, Support, etc.)
Springboard’s Machine Learning Career Track is a six-month commitment of 15-20 hours per week. Tuition is $10,900 for those who pay upfront.
The course is one of the most comprehensive of its kind — students build and deploy large-scale machine learning projects, gain experience with tools such as TensorFlow, develop an understanding of logistic regression and linear regression, and gain experience with database mining.
Machine Learning A-Z™: Hands-On Python & R in Data Science (Udemy, Kirill Eremenko)
Udemy’s machine learning course is a massive open online course (MOOC), which means anyone can enroll and learn at their own pace. With 320 lectures spanning topics such as data pre-processing, reinforcement learning, simple linear regression, convolutional neural networks, and hierarchical clustering, the course is intended for both beginners and data analysts looking to fill in skills gaps.
Get To Know Other Data Science Students
Esme Gaisford
Senior Quantitative Data Analyst at Pandora
Leoman Momoh
Senior Data Engineer at Enterprise Products
Diana Xie
Machine Learning Engineer at IQVIA
What We Like
The only prerequisite for students is having a high school level knowledge of math, and the MOOC nature of Udemy means it has a low barrier to entry. The course is also available in Spanish, German, Italian, and Portuguese.
Details (Cost, Curriculum, Support, etc.)
Udemy’s course consists of 44.5 hours of video lectures, although students can expect to spend additional time on coding exercises. Students can pay a monthly fee of $29.99 to access Udemy’s courses, or buy the machine learning program as a one-off for $84.99. Like most MOOCs, Udemy offers minimal support to students, with no individual mentorship or career counseling services.
Data Science and Machine Learning Bootcamp With R (Udemy, Jose Portilla)
Udemy’s machine learning bootcamp offers students an introduction to how R can be used in data science and ML engineering, and the different ways it can be handled in SQL files and web scraping.
What We Like
With such a narrow focus on R, this course is suited to anyone who wants to learn the basics of a programming language or who simply has an R knowledge gap that they want to fill.
Details (Cost, Curriculum, Support, etc.)
Udemy’s R course consists of 17.5 hours of on-demand videos. Students can pay a monthly fee of $29.99 to access Udemy’s courses, or buy the machine learning program as a one-off for $84.99. Like most MOOCs, Udemy offers minimal support to students, with no individual mentorship or career counseling services.
AI Applications for Growth (Kellogg Executive Education)
Developed by Northwestern University, this executive program is intended for business leaders who want to understand how machine learning and artificial intelligence can give organizations a competitive edge. Unlike some of the other courses on this list that teach students how to do the job of a machine learning engineer, Kellogg’s offering helps decision makers apply AI to their companies.
What We Like
The Kellogg course approaches machine learning and AI from a business perspective and helps equip company leaders, managers, and mid-career professionals with the knowledge, case studies, and vocabulary to incorporate ML engineering into their organizations.
Details (Cost, Curriculum, Support, etc.)
This program takes two months with a 4-6 hour a week time commitment. Classes are delivered over recorded video lectures, and students can go at their own pace within each weekly module. Classes span topics such as AI and customer experience management, AI in autonomous vehicles, and strategy and platforms. The course costs $2,600.
Machine Learning: From Data to Decisions (MIT)
Similar to Northwestern’s executive course, this machine learning program from MIT is designed for organizational leaders, managers, data scientists, technical leads, and consultants who want to better understand how they can use data and machine learning to gain a competitive edge.
What We Like
The course requires no technical prerequisites. It equips students with an understanding of the building blocks of machine learning, the tools that can be used to make predictions such as neural networks and classification, and how to make critical decisions under uncertainty. The course is also offered in Italian, French, Spanish, and Portuguese.
Details (Cost, Curriculum, Support, etc.)
The program runs for eight weeks and consists of eight lesson modules that span understanding data, making predictions through regression, classification, and neural networks, and decision making foundations. It costs $2,300.
Fundamentals of Google AI for Web Based Machine Learning (Google)
This Google-developed course, taught on the EdX platform, touches on both high-level AI concepts and gives students a taste of building their own ML algorithms. Divided into two parts, the first part of the course covers everything from real-world applications of AI to the relationship between data and ML engineering. The second part covers TensorFlow.js.
What We Like
This self-paced course gives students the opportunity to learn directly from Google AI experts. While it doesn’t go as deep or span as many topics as other ML engineering courses, it offers a strong introductory overview to AI and how it can be applied.
Details (Cost, Curriculum, Support, etc.)
Google AI’s course takes two months to complete with a 3-4 hour a week time commitment. The curriculum is limited compared to other ML engineering courses, but offers a valuable overview of the applications of AI and TensorFlow.js. Like most MOOCs, EdX courses offer minimal support to students, with no individual mentorship or career counseling services. The course costs $398.
Deep Learning Specialization (deeplearning.ai)
Developed by the co-founder of Coursera, Andrew Ng, and offered through the Coursera platform, the Deep Learning Specialization is an ML engineering course that requires students to have experience with Python (heavily used in the data science field), linear algebra, and machine learning concepts.
What We Like
For experienced data professionals who want to study advanced ML engineering concepts, this course cuts to the chase. The program spans artificial neural networks, deep learning, mathematical optimization, transformers, recurrent neural networks, and more.
Details (Cost, Curriculum, Support, etc.)
The program takes around five months to complete with an 8 hour a week commitment. Although the course can be read and viewed for free, it costs $49/month to gain full access to the program. Like most MOOCs, Coursera courses offer minimal support to students, with no individual mentorship or career counseling services.
The Best Free Machine Learning Courses
Free online courses are not usually as comprehensive or well supported as paid courses. That said, they offer a low-commitment approach to dipping your toes into machine learning engineering and often provide valuable lessons on programming and AI basics.
Machine Learning in Python (Springboard)
Springboard’s free Machine Learning in Python course is quick, concise, and designed to give those who already have a background in tech (such as knowledge of a programming language) a rigorous and practical understanding of Python machine learning.
What We Like
This program is best suited for tech professionals who want to know how ML engineering can change the way they work. The course is completely self-paced, features hands-on tutorials on coding using real-world data sets, covers the use of tools such as scikit-learn, and gives students a broad overview of common techniques of the field and how they can be implemented.
Details (Cost, Curriculum, Support, etc.)
Those who have a strong background in tech can expect to complete this free course in around 25 hours, or 2-4 weeks. The curriculum spans supervised learning and unsupervised learning, computational science, building deep neural networks, and using tools such as Pandas and NumPy.
Machine Learning (Stanford, Andrew Ng)
This Coursera program offers an accessible introduction to machine learning basics such as data mining, statistical pattern recognition, and logistic regression. The course is offered as a MOOC, which means students learn independently from pre-recorded lectures and readings.
What We Like
Students praise professor Andrew Ng for his ability to explain the mathematical concepts involved in different areas of machine learning. The course is broad and gives students a taste of the various elements that make up machine learning engineering, such as linear algebra, dimensionality reduction, support vector machines, recommender systems, bias/variance theory, and kernels and neural networks.
Details (Cost, Curriculum, Support, etc.)
The course consists of 61 hours of content and takes around 11 weeks to complete. A week is dedicated to each topic, such as linear regression with one variable, and within each week there are videos, readings, and quizzes.
Machine Learning Crash Course (Google)
Google’s crash course in machine learning is designed for those who already have prerequisite knowledge of Python and are comfortable with linear equations, graphs of functions, calculus, histograms, and statistical means. The self-paced program consists of 25 lessons (videos and exercises) that span machine learning concepts such as regularization, feature crosses, and validation sets.
What We Like
Each lesson within the Machine Learning Crash Course includes a video lecture, an additional reading, and an exercise or quiz to help students check their understanding of the topic — exercises often involve real-world data sets to give students hands-on experience.
Details (Cost, Curriculum, Support, etc.)
The crash course takes around 15 hours to complete, with lectures from Google AI experts and exercises based on real-world case studies. As with most free courses, Google doesn’t offer mentorship or support from instructors.
Free Machine Learning Course (fast.ai)
Fast.ai’s machine learning course was designed to be a companion to the book Deep Learning For Coders With Fastai And PyTorch: AI Applications Without A PhD. Built to be accessible to beginners, the course teaches students to train models in computer vision, tabular data, natural language processing, and random forests and gradient boosting; understand how deep learning models work; and the latest deep learning techniques that matter in practice.
What We Like
The accessible nature of it means that even though the course delves into some fairly complex and technical subjects, newcomers can still find a way in. The course was designed so that anyone with high school level math and around a year of programming experience (preferably Python) can follow along.
Details (Cost, Curriculum, Support, etc.)
Fast.ai’s course contains around 20 hours of lessons that students typically complete over seven weeks. Like most MOOCs, the course has no individualized support or mentorship, but students have access to an online forum where they can ask questions of their peers.
Machine Learning Course Considerations
Whether you’re interested in enrolling in a paid course or getting started in something that’s free, the following things are worth keeping in mind.
Online vs in Person
Many people like taking courses online because it offers them flexibility and the ability to go at their own pace. But some struggle without the accountability of traditional classroom learning and face time with instructors. Before you sign up for a machine learning course, it’s important to understand what kind of student you are and what conditions will help you thrive. Are you someone who needs a classroom environment and face-to-face instruction to help you learn? And if you prefer to take a course online, are you better suited to synchronous classes where you have the accountability of real-time lectures and meetings, or do you perform better when left to set your own pace?
Course Provider Reputation
Not all machine learning courses are made the same. Before enrolling — or paying — for a course, look into its success with students. What information does it publish about its graduate and job placement rate? How many students actually complete the course once they begin? And does it offer assurances such as deferred payment or a job guarantee? The existence of a job guarantee typically signals confidence in the program and its ability to help graduates land a relevant job.
Job Support
It’s one thing to learn all the skills needed to do the work of a machine learning engineer; it’s another to get a foot in the door. Look for courses that will support you in both your academic journey and your post-graduation job search. Does a program include industry mentorship or career counseling? Is interview training or portfolio preparation included? Will the program go above and beyond to help give you a competitive edge? These are all things to consider when choosing a course.
Projects and Portfolio
While most courses promise to teach you the skills needed to become a ML engineer, only some will build in capstone projects and assignments that help you put together a competitive portfolio. When deciding between courses, prioritize the program that will give you hands-on experience with projects that both develop and show off your skills.
Companies are no longer just collecting data. They’re seeking to use it to outpace competitors, especially with the rise of AI and advanced analytics techniques. Between organizations and these techniques are the data scientists – the experts who crunch numbers and translate them into actionable strategies. The future, it seems, belongs to those who can decipher the story hidden within the data, making the role of data scientists more important than ever.
In this article, we’ll look at 13 careers in data science, analyzing the roles and responsibilities and how to land that specific job in the best way. Whether you’re more drawn out to the creative side or interested in the strategy planning part of data architecture, there’s a niche for you.
Is Data Science A Good Career?
Yes. Besides being a field that comes with competitive salaries, the demand for data scientists continues to increase as they have an enormous impact on their organizations. It’s an interdisciplinary field that keeps the work varied and interesting.
10 Data Science Careers To Consider
Whether you want to change careers or land your first job in the field, here are 13 of the most lucrative data science careers to consider.
Data Scientist
Data scientists represent the foundation of the data science department. At the core of their role is the ability to analyze and interpret complex digital data, such as usage statistics, sales figures, logistics, or market research – all depending on the field they operate in.
They combine their computer science, statistics, and mathematics expertise to process and model data, then interpret the outcomes to create actionable plans for companies.
General Requirements
A data scientist’s career starts with a solid mathematical foundation, whether it’s interpreting the results of an A/B test or optimizing a marketing campaign. Data scientists should have programming expertise (primarily in Python and R) and strong data manipulation skills.
Although a university degree is not always required beyond their on-the-job experience, data scientists need a bunch of data science courses and certifications that demonstrate their expertise and willingness to learn.
Average Salary
The average salary of a data scientist in the US is $156,363 per year.
Data Analyst
A data analyst explores the nitty-gritty of data to uncover patterns, trends, and insights that are not always immediately apparent. They collect, process, and perform statistical analysis on large datasets and translate numbers and data to inform business decisions.
A typical day in their life can involve using tools like Excel or SQL and more advanced reporting tools like Power BI or Tableau to create dashboards and reports or visualize data for stakeholders. With that in mind, they have a unique skill set that allows them to act as a bridge between an organization’s technical and business sides.
General Requirements
To become a data analyst, you should have basic programming skills and proficiency in several data analysis tools. A lot of data analysts turn to specialized courses or data science bootcamps to acquire these skills.
For example, Coursera offers courses like Google’s Data Analytics Professional Certificate or IBM’s Data Analyst Professional Certificate, which are well-regarded in the industry. A bachelor’s degree in fields like computer science, statistics, or economics is standard, but many data analysts also come from diverse backgrounds like business, finance, or even social sciences.
Average Salary
The average base salary of a data analyst is $76,892 per year.
Business Analyst
Business analysts often have an essential role in an organization, driving change and improvement. That’s because their main role is to understand business challenges and needs and translate them into solutions through data analysis, process improvement, or resource allocation.
A typical day as a business analyst involves conducting market analysis, assessing business processes, or developing strategies to address areas of improvement. They use a variety of tools and methodologies, like SWOT analysis, to evaluate business models and their integration with technology.
General Requirements
Business analysts often have related degrees, such as BAs in Business Administration, Computer Science, or IT. Some roles might require or favor a master’s degree, especially in more complex industries or corporate environments.
Employers also value a business analyst’s knowledge of project management principles like Agile or Scrum and the ability to think critically and make well-informed decisions.
Average Salary
A business analyst can earn an average of $84,435 per year.
Database Administrator
The role of a database administrator is multifaceted. Their responsibilities include managing an organization’s database servers and application tools.
A DBA manages, backs up, and secures the data, making sure the database is available to all the necessary users and is performing correctly. They are also responsible for setting up user accounts and regulating access to the database. DBAs need to stay updated with the latest trends in database management and seek ways to improve database performance and capacity. As such, they collaborate closely with IT and database programmers.
General Requirements
Becoming a database administrator typically requires a solid educational foundation, such as a BA degree in data science-related fields. Nonetheless, it’s not all about the degree because real-world skills matter a lot. Aspiring database administrators should learn database languages, with SQL being the key player. They should also get their hands dirty with popular database systems like Oracle and Microsoft SQL Server.
Average Salary
Database administrators earn an average salary of $77,391 annually.
Data Engineer
Successful data engineers construct and maintain the infrastructure that allows the data to flow seamlessly. Besides understanding data ecosystems on the day-to-day, they build and oversee the pipelines that gather data from various sources so as to make data more accessible for those who need to analyze it (e.g., data analysts).
General Requirements
Data engineering is a role that demands not just technical expertise in tools like SQL, Python, and Hadoop but also a creative problem-solving approach to tackle the complex challenges of managing massive amounts of data efficiently.
Usually, employers look for credentials like university degrees or advanced data science courses and bootcamps.
Average Salary
Data engineers earn a whooping average salary of $125,180 per year.
Database Architect
A database architect’s main responsibility involves designing the entire blueprint of a data management system, much like an architect who sketches the plan for a building. They lay down the groundwork for an efficient and scalable data infrastructure.
Their day-to-day work is a fascinating mix of big-picture thinking and intricate detail management. They decide how to store, consume, integrate, and manage data by different business systems.
General Requirements
If you’re aiming to excel as a database architect but don’t necessarily want to pursue a degree, you could start honing your technical skills. Become proficient in database systems like MySQL or Oracle, and learn data modeling tools like ERwin. Don’t forget programming languages – SQL, Python, or Java.
If you want to take it one step further, pursue a credential like the Certified Data Management Professional (CDMP) or the Data Science Bootcamp by Springboard.
Average Salary
Data architecture is a very lucrative career. A database architect can earn an average of $165,383 per year.
Machine Learning Engineer
A machine learning engineer experiments with various machine learning models and algorithms, fine-tuning them for specific tasks like image recognition, natural language processing, or predictive analytics. Machine learning engineers also collaborate closely with data scientists and analysts to understand the requirements and limitations of data and translate these insights into solutions.
General Requirements
As a rule of thumb, machine learning engineers must be proficient in programming languages like Python or Java, and be familiar with machine learning frameworks like TensorFlow or PyTorch. To successfully pursue this career, you can either choose to undergo a degree or enroll in courses and follow a self-study approach.
Average Salary
Depending heavily on the company’s size, machine learning engineers can earn between $125K and $187K per year, one of the highest-paying AI careers.
Quantitative Analyst
Qualitative analysts are essential for financial institutions, where they apply mathematical and statistical methods to analyze financial markets and assess risks. They are the brains behind complex models that predict market trends, evaluate investment strategies, and assist in making informed financial decisions.
They often deal with derivatives pricing, algorithmic trading, and risk management strategies, requiring a deep understanding of both finance and mathematics.
General Requirements
This data science role demands strong analytical skills, proficiency in mathematics and statistics, and a good grasp of financial theory. It always helps if you come from a finance-related background.
Average Salary
A quantitative analyst earns an average of $173,307 per year.
Data Mining Specialist
A data mining specialist uses their statistics and machine learning expertise to reveal patterns and insights that can solve problems. They swift through huge amounts of data, applying algorithms and data mining techniques to identify correlations and anomalies. In addition to these, data mining specialists are also essential for organizations to predict future trends and behaviors.
General Requirements
If you want to land a career in data mining, you should possess a degree or have a solid background in computer science, statistics, or a related field.
Average Salary
Data mining specialists earn $109,023 per year.
Data Visualisation Engineer
Data visualisation engineers specialize in transforming data into visually appealing graphical representations, much like a data storyteller. A big part of their day involves working with data analysts and business teams to understand the data’s context.
General Requirements
Data visualization engineers need a strong foundation in data analysis and be proficient in programming languages often used in data visualization, such as JavaScript, Python, or R. A valuable addition to their already-existing experience is a bit of expertise in design principles to allow them to create visualizations.
Average Salary
The average annual pay of a data visualization engineer is $103,031.
Resources To Find Data Science Jobs
The key to finding a good data science job is knowing where to look without procrastinating. To make sure you leverage the right platforms, read on.
Job Boards
When hunting for data science jobs, both niche job boards and general ones can be treasure troves of opportunity.
Niche boards are created specifically for data science and related fields, offering listings that cut through the noise of broader job markets. Meanwhile, general job boards can have hidden gems and opportunities.
Online Communities
Spend time on platforms like Slack, Discord, GitHub, or IndieHackers, as they are a space to share knowledge, collaborate on projects, and find job openings posted by community members.
Network And LinkedIn
Don’t forget about socials like LinkedIn or Twitter. The LinkedIn Jobs section, in particular, is a useful resource, offering a wide range of opportunities and the ability to directly reach out to hiring managers or apply for positions. Just make sure not to apply through the “Easy Apply” options, as you’ll be competing with thousands of applicants who bring nothing unique to the table.
FAQs about Data Science Careers
We answer your most frequently asked questions.
Do I Need A Degree For Data Science?
A degree is not a set-in-stone requirement to become a data scientist. It’s true many data scientists hold a BA’s or MA’s degree, but these just provide foundational knowledge. It’s up to you to pursue further education through courses or bootcamps or work on projects that enhance your expertise. What matters most is your ability to demonstrate proficiency in data science concepts and tools.
Does Data Science Need Coding?
Yes. Coding is essential for data manipulation and analysis, especially knowledge of programming languages like Python and R.
Is Data Science A Lot Of Math?
It depends on the career you want to pursue. Data science involves quite a lot of math, particularly in areas like statistics, probability, and linear algebra.
What Skills Do You Need To Land an Entry-Level Data Science Position?
To land an entry-level job in data science, you should be proficient in several areas. As mentioned above, knowledge of programming languages is essential, and you should also have a good understanding of statistical analysis and machine learning. Soft skills are equally valuable, so make sure you’re acing problem-solving, critical thinking, and effective communication.
Since you’re here…Are you interested in this career track? Investigate with our free guide to what a data professional actually does. When you’re ready to build a CV that will make hiring managers melt, join our Data Science Bootcamp which will help you land a job or your tuition back!
