If you have been studying programming and math, you might be thinking about going into data science or machine learning as a career path. At Pathrise, we work with job-seekers every day and one of our biggest recommendations comes at the very beginning of the process. Candidates need to narrow down their search so they can find the right position for their skills, education, experience, and goals.
To help you decide if machine learning or data science is a good fit for you, we have outlined the similarities and differences as well as included information about the skills, tools, and salaries for each role.
- What is data science?
- What is machine learning?
- Similarities and differences between data science vs machine learning
- Skills: Data science vs machine learning
- Tools: Data science vs machine learning
- Salaries: Data science vs machine learning
What is Data Science?
In tech, the field of data science is skyrocketing. Companies are hiring more and more data scientists, analysts, and engineers to collect, process, analyze, and visualize data sets with the ultimate goal of making business decisions using algorithms and programming languages like SQL and Python. Beyond needing the technical hard skills, data scientists also need to create decks and present their findings to stakeholders and team members. Learn more about what data scientists do in our article.
What is Machine Learning?
Machine learning is similar to data science as a field, but it requires more knowledge of programming. Machine learning engineers train computer systems using models to accurately predict and/or classify outcomes. In this case, the computers are analyzing the data, determining patterns, and suggesting conclusions. Machine learning is commonly used in artificial intelligence as well when working with extremely large groups of data. Companies like Facebook, Amazon, and Netflix often use machine learning algorithms to suggest friends, products, and content based on your behaviors on their platforms.
Similarities and differences between data science vs machine learning
Data science and machine learning are both highly technical fields that are currently in demand.
Similarities
- Candidates looking to be successful in machine learning or data science need to have a strong background in math and programming.
- Data scientists and machine learning engineers should be analytical and enjoy problem-solving.
- There is a fair amount of overlap in the fields – data scientists can use machine learning to manage large data sets.
Differences
- Data scientists are generally doing more manual collecting, analyzing, and visualizing the data to come to conclusions.
- Machine learning engineers are responsible for training models and they often need to spend more time programming and researching.
- Data scientists are often required to create decks and present their findings, sometimes to team members that are non-technical.
Skills: Data science vs machine learning
Recruiters and hiring managers are looking to make sure that new employees will provide impact and be an asset for the team. Oftentimes they are looking for specific skills, so in order to land a great job and be successful in these roles, data scientists and machine learning engineers should have the following skills.
Data science skills
- Probability
- Statistics
- Linear algebra
- Multivariable calculus
- Collecting, sorting, and analyzing data
- Data visualization
- Ability to create presentations and explain findings to technical and non-technical team members and stakeholders
- Programming (specifically in R, Python, and SQL)
Learn more about data science skills needed to land a great job [will link] in our article.
Machine learning skills
- Computer science fundamentals
- Programming knowledge
- High level of math education, including probability and statistics
- Statistical modeling
- Data architecture design
- Deep understanding and application of algorithms
- Outside the box thinker
Tools: Data science vs machine learning
Both of these fields are highly technical and require knowledge of a variety of different tools. Since machine learning can often fit under the broader data science umbrella, some of the tools used in these roles are the same. By having these tools on your resume, you can let recruiters and hiring managers know that you have the right knowledge for the role.
Data science tools
- Programming and database querying languages like SQL, R, Java, Python, C++, JSON
- Statistics and data analytics tools like Microsoft Excel, MATLAB, TensorFlow, Tableau, KNIME, SaS (Statistical Analysis System), TensorBoard, Python pandas
- Data gathering, processing, and visualization tools like Hadoop, Geospatial, Apache Spark, Redis, MongoDB
Learn more about data science tools needed to land a great job [will link] in our article.
Machine learning tools
- Programming and database querying languages like Python, C++, R, SQL, JSON
- Machine learning technologies and frameworks like NumPy, Random Forests, KerasNaive Bayes, scikit-learn, DataRobot, PyTorch, RapidMiner, BigML, NLTK
- Data analytics and visualization tools like pandas, matplotlib, Jupyter notebook, Tableau
Salaries: Data science vs machine learning
Since data science and machine learning are both highly technical fields, which typically require at least an undergraduate education and often graduate school as well, you can expect the average salaries for the roles to be on the higher end of the spectrum.
Average salary for data science
According to Glassdoor, the average salary for data scientists is $113k (without geographic restrictions). Data analysts, which typically report to data scientists and require less extensive backgrounds, have an average salary of $67k. On AngelList, the average salary for a data scientist is $94k. This is likely a more well-rounded salary average because it includes startups. Learn how to increase your data science salary above the average through negotiation. If you are looking for even more guidance on your job search, check out our career guide: How to get a data science job.
Average salary for machine learning
If you look on Glassdoor, the average compensation for machine learning engineers is $114k. The low end of the spectrum is around $78k and the highest reported is $150k. Similarly, on PayScale, the average salary for machine learning engineers is $111k. Large tech companies, like Adobe, Microsoft, and Apple are frequently hiring machine learning engineers and paying them the highest paying salaries.
If you want more support on your job search, Pathrise is a career accelerator that works with students and professionals 1-on-1 so they can land their dream job in tech. With our tips and guidance, fellows in our program see 3x as many responses to their applications, interview scores that double, and a 5-20% increase in salary.
If you want to work with our mentors 1-on-1 to get help with any aspect of your job search, join Pathrise.
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