Back to Blog

Data Science

Top FAQs about our Machine Learning Engineering CT

12 minute read | March 11, 2020

Ready to launch your career?

According to hiring managers, knowing machine learning concepts is important but not enough to get you hired. For this reason, we launched in 2019 our Machine Learning Engineering Career Track (MLE CT). Below we compiled the most common questions being asked by prospects of our MLE CT.  If you are wondering if this program is right for you, check them out here.

FAQ 1: Can I join if I do not have programming experience?

FAQ 2: Is this the right program for me?

FAQ 3: Who should NOT consider an MLE Career Track?

FAQ 4: How is the program different from cheaper online resources?

FAQ 5: How will this program help me achieve my career goals?

FAQ 6: What will I learn, and how will I learn it?

FAQ 7: What is an example of how I will master ML skills in the curriculum? 

FAQ 8: How is this program different from a Master’s in ML or A.I.?

FAQ 9: Who built this course and how much knowledge do they have?

FAQ 1: Can I join the program if I do not have programming experience?

Students with at least o

Our Machine Learning Engineering Career Track is for candidates with a strong background handling an object-oriented programming language (OOP) like Java, Python, C++, PHP, etc. 

Starting with our Data Science Career Track may be a better fit for you if you have less than one year of experience handling an OOP language. 

If you are entirely new to programming, you can start with our four to six weeks of Data Science Prep course. Upon completion, you would be ready to be accepted to our Data Science Career Track Program. 

FAQ 2: Is this the right program for me?

Students with at least one year of experience working in the software engineering industry have the highest likelihood to be successful in our program. Most of the students who finish our program come from having previous experience as data engineers, QA engineers, back-end engineers, software engineers, or other roles in application development.

Outside software engineering disciplines, some of the students who are also likely to succeed in our program are Data Scientists, and Master’s or Ph.D. graduates in computer science, electrical engineering, applied math, or a related field where computer simulation is an ongoing part of the curriculum.

While some candidates who hold advanced degrees with significant data analysis or are self-taught (via online courses, etc.) may meet the minimum requirements, it is unlikely they will be eligible for our Job Guarantee. Since the program was built specifically for software engineering disciplines, coming from an adjacent or unrelated program can create extra challenges for students who don’t have a strong background with proficient knowledge of a modern programming language (i.e., C++, Java, Python). Instead, our Data Science Career Track with a specialization in Machine Learning might be a better choice.

FAQ 3: Who should NOT consider this program? 

If Machine Learning is a side hobby for you or you’re just curious about the A.I. industry, this course might not be the best fit for you.  

The students who succeed in our program come because they see becoming a Machine Learning Engineer as their next step in their career. They take our Career Track to get a new job as Machine Learning Engineers (MLEs) or because they want to switch to a more challenging role within their current company.  

If you are hobbyist or enthusiast in the A.I. space, free-online tutorials and low-cost courses are a better option for you. You can get started with excellent free online resources like our Introduction to Machine Learning in Python or the dozens of video tutorials available online on YouTube. 

This program is for someone who sees the limitations of learning how to build, deploy, and scale Machine Learning models from only watching free online videos or taking low-cost courses. Our program is project-based and requires our students to work on building and deploying their own ML models after they learn the theory presented in the curriculum.

If you are proficient in handling modern programming languages like C++, Java, or Python and you don’t want to go back to school for a 2 years MLE master’s degree, then you could be the right candidate for this program.

If you want to seek a career that relates to A.I., but you are not sure just yet if Machine Learning Engineering is the career you want to pursue long-term, a good alternative to get started is our Data Science Career Track program.

FAQ 4: How is the program different from cheaper online resources?

If you are a hobbyist or enthusiast in Artificial Intelligence, and you are not sure if you want to commit to a career in Machine Learning Engineering, then free-online tutorials and low-cost AI courses are definitely a better option for you! There are dozens of excellent video tutorials on Youtube and other websites to learn the fundamentals of Machine Learning. In fact, we use many of them to explain key concepts throughout our curriculum. 

However, if you’re serious about becoming a Machine Learning Engineer, you probably see the limitations of free online tutorials and low-cost courses to learn the engineering side of Machine Learning.  

Learning the fundamentals of Machine Learning is one hurdle, but applying those learnings to write your own ML algorithms or to deploy and scale a machine learning model in production is a whole different challenge. Springboard’s Career Track tackles both of these aspects. 

The program follows a hands-on, real-life project-based learning methodology where you work on applying the knowledge gained from the theory by completing project milestones. As you complete these projects, you learn the skills required to write ML algorithms, deploy real AI models, and, most importantly, work towards building your own machine learning portfolio as the program progresses. 

Comparing a Career Track vs. an online course is like comparing apples and oranges…

The way our students learn our Deep Learning module is a great example to showcase this. This module teaches the principles of Deep Neural Networks along with engineering frameworks like Keras, TensorFlow, and PyTorch. One of the videos we use in our platform to teach the fundamentals of Deep Neural Networks with PyTorch is the video below created by Stefan Otte and available for free on Youtube.

This video is one of the best – if not the best – video to learn the fundamentals of Deep Neural Networks with PyTorch. Does that mean that after watching a one hour and a half video, you can now build and deploy your own Neural Network model into production using PyTorch? Unlikely. 

The most critical part of learning the concepts presented in the curriculum is to be able to apply them to real-life problems and challenges. You can only achieve this by working on real-life projects and getting guidance from a Machine Learning Engineering expert. 

Let’s expand on that previous point. 

Learning the basics of how to build a Neural Network with PyTorch could be an important part of Machine Learning. You can learn this from excellent resources at a much lower cost than our program or even for free (i.e., youtube). 

But what’s critical to be a successful Machine Learning Engineer is to decide based on your resources available (or your company’s), how a problem is solved most effectively. 

When you transition to a Machine Learning Engineering job, your main challenge will not be if you understand the fundamentals of deep learning. Your main challenge will be to decide the tradeoffs of using one model vs. another to find the most effective solution to a problem – both in terms of model performance and cost. 

If building a Neural Network using Pytorch is the right approach to solve the problem an organization is facing, the next step for you as a Machine Learning Engineer is to build, deploy, or scale the specific ML model correctly.

These are the types of skills you will master from our program compared to the skills you gain from other traditional courses.

To summarize

With Springboard’s Machine Learning Engineering Career Track:

  1. You learn the fundamentals of Machine Learning from the most reputable content available online; and
  2. You work on applying that knowledge to real-world problems by working on the different project milestones required to complete the course; and
  3. You get 1:1 mentorship from a Machine Learning Engineering expert who gives you weekly guidance so you can complete the projects of the program. 

Finally, to be awarded our Springboard Machine Learning Engineering Career Track certificate, you will need to combine the skills gained from the curriculum to deliver an ML or DL capstone project. The project requires you to build, deploy, and scale a real model on a specific topic or area you are passionate about.

Get To Know Other Data Science Students

Ginny Zhu

Ginny Zhu

Data Science Intern at Novartis

Read Story

Sunil Ayyappan

Sunil Ayyappan

Senior Technical Program Manager (AI) at LinkedIn

Read Story

Rane Najera-Wynne

Rane Najera-Wynne

Data Steward/data Analyst at BRIDGE

Read Story

FAQ 5: How will this program help me achieve my career goals?

If your goal is to study Machine Learning Engineering to take your career to the next level, then this program will help you achieve that goal. 

The top 3 reasons our students report for taking our program are:

  1. They want to switch to an A.I. or Machine Learning Engineering role in their current company.
  2. They want to build and deploy real Machine Learning Engineering models in their current career or venture.
  3. They want to advance in their career with a new job in the Machine Learning Engineering space. 

The curriculum follows a hands-on, project-based learning methodology where the primary goal is to support professionals with building their Machine Learning portfolio through the completion of multiple projects. Every student gets paired with an industry expert who guides them throughout the curriculum with weekly video check-ins.

How are your goals aligned with Springboard’s goals?

Springboard’s programs are rapidly growing because they follow an outcomes-oriented education model where the final student goal is always aligned with the Career Track they take. 

For instance, compared to a traditional Master’s degree, our Career Tracks offer a job guarantee

Our job guarantee commitment ensures that eligible students get 100% of their tuition reimbursed if they cannot find a job after completing the program. That way, students can commit to our program with additional focus and confidence in Springboard. If our program doesn’t get our students a job, they simply don’t pay. 

FAQ 6: What will I learn, and how will I learn it?

What will you learn in our program?

You’ll learn the foundations of machine learning and deep learning —

and how to implement them at scale. The first half of the course focuses on building and scaling a working prototype (either in ML or DL) while the second half focuses on deploying your prototype to production. Download our Syllabus to get a more detailed breakdown of the subjects covered. 

How will you learn in our program?

One of the most critical aspects of the Machine Learning Engineering program is the way in which you’ll learn the concepts introduced in the curriculum. The curriculum is rigorous and intensely technical, teaching you the foundations of machine learning and deep learning. These resources, often organized and provided to you from different places across the internet, provide you in a logical and organized manner with the theory and fundamentals you need to learn to be a successful Machine Learning Engineer. 

However, that’s just one part of the program. 

The Machine Learning Engineering Career Track follows a hands-on, project-based learning methodology. Meaning you will work on applying the knowledge gained from the theory by completing project milestones.

As you work towards completing the projects of the program, you will gain the skills required to write your own algorithms, learn how to deploy and scale A.I. models, and, most importantly, you will work on building your own machine learning portfolio. 

To ensure our students successfully gain these skills, they are paired with an expert in the industry who will guide them with 1:1 weekly video meetings on the progress of the curriculum.

To complete the program and master the curriculum, you will be required to submit a capstone project. Using the knowledge, tools, and techniques that you learned in the program, you will build a real Machine Learning or Deep Learning application. The capstone project follows a 10-step guided process throughout the curriculum with guidance from your mentor.

Once your capstone is submitted and approved by our board, you will finalize the academic part of the program with the completion of your very own Machine Learning portfolio.

FAQ 7: What is an example of how I will master ML skills in the curriculum? 

Our Module 6, A “Deep” Dive into Deep Learning, is a great example. This unit teaches you the principles of Deep Neural Networks, common Neural Network configurations like RNNs, CNNs, MLPs, LSTMs, and engineering frameworks like Keras, TensorFlow, and PyTorch. 

While you can find many of the concepts you’ll learn in this module online, the most critical aspect of mastering them is to be able to apply them in real-life scenarios. All of our modules follow a hands-on, project-based learning methodology, and with the help of your mentor, you will complete the projects that come on each module.

Completing the projects ensures you gain the skills required to write, deploy, and scale your own AI models. This learning methodology also allows you to tailor the curriculum towards areas in Machine Learning where you are most interested in.

FAQ 8: How is this program different from a Master’s in ML or A.I.?

Some candidates erroneously compare our program to low-cost online courses and free video tutorials. In the previous FAQ, we break down why this is not a valid comparison for candidates who are serious about a career in Machine Learning Engineering. 

However, a master’s degree may offer a similar education model as our Machine Learning Engineering (MLE) Career Track. Here is a break down of how we compare. 

Key similitudes:

  • Program Depth. A master’s in Machine Learning and our MLE Career Track are comprehensive academic programs that provide you the tools and knowledge to transition to a career in Machine Learning Engineering. 
  • Curriculum Strength. Our curriculum was built (and is continuously updated) by Springboard’s MLE subject matter expert with the support of Springboard’s MLE board of advisors (Read more about them in FAQ 9). This process is similar to how a college dean leads the launch of a new Master’s program with the support of professors who teach the subject.
  • Project-based learning. The program is taught following a hands-on project-based methodology. For example, our students learn various uses of Spark ML by working on customizing ML pipelines to build their own algorithms and compete with state-of-art algorithms.

Key differences:

  • A job guarantee. Probably our most significant difference from a university. Eligible Springboard students are guaranteed a job after they complete our program, or 100% of their tuition is reimbursed.  
  • Lower cost. The total tuition to earn a graduate degree in the U.S can range from $30,000 to $120,000 (both online and on-campus). The tuition of our program is $7,940.
  • Mentorship from Experts. As a Master’s student, you get office hours support from TAs and professors. Our students, however, get unlimited 1:1 mentorship support from Machine Learning Engineering experts already working in the industry. 
  • Focus on the skills employers are looking for. Most master programs focus on the research side of Machine Learning and could require more in-depth classes on calculus and statistics. Our curriculum will cover these areas just enough, so you can focus on getting experience in projects where you write your own algorithms or learn how to deploy and scale AI models.

FAQ 9: Who built this course and how much knowledge do they have?

Our course was created by Springboard’s Machine Learning Engineering experts. Our Lead Subject Matter Expert (SME) is the head of the program curriculum and works in conjunction with Springboard’s Machine Learning Engineering board. Here is some information about their background:

Sébastien Arnaud – Lead SME

Sebastien has over 17 years of experience working in Data Science, Software Engineering, and Machine Learning Engineering. He reached the Master’s level on in 2014 for his past competitive entries, in particular for his real-time competitive solution for the “Job Salary Prediction” using Lucene similarities and Genetic Programming, which ranked him in the top 150 machine learning professionals in March 2013. 

Sebastian is responsible for the structure of the Machine Learning Engineering Career Track, the philosophy of the program, and the engineering units. 

Check out his track record here

Eddie (ChengYu) Lin – SME

Eddie is part of Springboard’s Machine Learning Engineering board. He has over five years of experience in Machine Learning Engineering and is responsible for the ML model units. 

Check out his track record here

Dipanjan (DJ) Sarkar – SME

DJ is part of Springboard’s Machine Learning Engineering board. He is also a Springboard mentor for Machine Learning Engineering students. DJ is a top-rated writer for Towards Data Science and is mostly responsible for creating our Machine Learning Engineering projects. 

Check out his track record here.

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!

About Juan-David Rodriguez

Originally from Colombia, Juan-David is passionate about marketing, Machine Learning, and emerging financial technologies. With a background in industrial engineering, marketing and finance, Juan-David has spent the past 8 years drawing insights from data to strategize and drive growth to technology-driven organizations.