Data science and machine learning (ML) are two entirely different concepts but within the same field of technology. They both use data for creating innovative products, services, and infrastructural systems. They both offer careers that are in demand and offer lucrative salaries. One way of expressing the similarities and differences between data science vs machine learning is by using the example of squares and rectangles. Both have four sides and are made using straight lines. But, rectangles are not squares and vice versa.
Data science and machine learning have their own entities. Moreover, they are both being adopted rapidly by almost every industry. Additionally, pursuing a career in either field can deliver high returns.
So, let’s get a more scientific and detailed view of the difference between data science vs machine learning.
Data science is a field that involves data research and analysis. It determines the systems, processes, and tools needed to translate data into valuable business insights. To become a data scientist, you must have
- Expertise in statistics
- Data visualization abilities
- Coding skills in Python(R)
- Data evaluation
Skills Needed for a Career in Data Science
To make a career in data science, such as becoming a data scientist, you must have a more-than-average grasp of programming and data analytics skills. You must also have the following:
- Strong understanding of programming languages Python, R, SAS, and others.
- Expertise in working with vast volumes of structured and unstructured data
- Ability to process and analyze data for business requirements
- Understanding of math, statistics, and probability
- Familiarity with machine learning algorithms and models
- Excellent communication skills and ability to work in a team
As a data scientist, you may have to formulate and prove or even refute theories. That is why a solid academic background is a must if you want to pursue this profession. This will help you develop a systematic and methodical approach to problems.
Good knowledge of math and statistics, as well as data mining and processing techniques, can add value to your profile.
Career Opportunities in Data Science
Besides the apparent career as a data scientist, many other job opportunities are available for you in this field. They include data scientists, analysts, engineers, architects, and business intelligence analysts.
Machine Learning (ML)
Machine learning is devoted to building methods that leverage the power of data to boost performance or make business predictions.
ML is a part of the Artificial Intelligence process. It helps create human-like abilities in machines using algorithms and statistical models. So, to become a machine learning expert, you must have:
- Excellent programing skills in Python, Java, and SQL
- Have a good understanding of statistics and probability
- Skills in prototyping
- An understanding of data modeling
ML professionals use algorithms to extract data and predict future trends. Software systems are programmed with models that engineers decode by conducting statistical analysis. So, this helps them understand patterns in the data.
An example of an ML application is how social channels gather visitor data. Based on past customer behavior, it predicts user interests and desires and recommends relevant products and services.
Machine learning is primarily applied in data science but is valuable in other fields too.
Data scientists often integrate machine learning into their projects to collect detailed information more accurately. With MI, they can do it faster.
Skills Needed for a Career in ML
To become a successful machine learning engineer, you must have the following skills:
- Proficiency in computer science
- Knowledge about data structures, algorithms, and architecture
- A strong understanding of statistics and probability
- A deep understanding of software engineering and systems design
- Programming knowledge, such as Python, R, and more
- Ability to handle data modeling and analysis
Careers Opportunities in ML
Several options are available if you want a career in machine learning and artificial intelligence.
- Machine learning engineer
- AI engineer
- Cloud engineer
- Computational linguist
- Human-centered AI systems designer
Applied mathematics is a vital skill for a machine learning engineer. Standard models and approaches do not work for complex projects. So, you must develop out-of-the-box models and solutions. Likewise, knowledge of theoretical math and statistics will make you more efficient. As some engineering elements are also involved, programming skills are also important. While Python is the most commonly used programming language in ML, other languages are also gaining popularity and usage.
You must understand that machine learning is a vast field. That’s why you must clearly know what you’re going to specialize in.