Data Science vs. Machine Learning

Machine learning, on the other hand, refers to a group of techniques used by data scientists that allow computers to learn from data. These techniques produce results that perform well without programming explicit rules.
Data science and machine learning are both very popular buzzwords today. These two terms are often thrown around together but should not be mistaken for synonyms. Although data science includes machine learning, it is a vast field with many different tools.
SPONSORED SCHOOLS
Syracuse University
info
Master of Science in Applied Data Science
Syracuse University’s online Master of Science in Data Science can be completed in as few as 18 months.
Complete in as little as 18 months
No GRE scores required to apply
University of California, Berkeley
info
Master of Information and Data Science
Earn your Master’s in Data Science online from UC Berkeley in as few as 12 months.
Complete in as few as 12 months
No GRE required
Southern Methodist University
info
Master of Science in Data Science
Earn your MS in Data Science at SMU, where you can specialize in Machine Learning or Business Analytics, and complete in as few as 20 months.
No GRE required.
Complete in as little as 20 months.
University of Texas at Austin
info
Master of Data Science
In today’s data-driven world, there’s an increased need for data scientists in every industry. Accelerate your career in this rapidly growing field by completing a rigorous master’s degree program that’s 100% online, affordable, and flexible to fit your life. With an MSDS from the top-ranked University of Texas at Austin, you’ll gain in-demand skills in data visualization, data mining, data analysis, machine learning, and more.
Data Science Workflow
The proliferation of smartphones and digitization of so many parts of daily life have created massive amounts of data. At the same time, the continuation of Moore’s Law, the idea that computing would dramatically increase in power and decrease in relative cost over time, has made cheap computing power widely available. Data science exists as the link between these two innovations. By combining these components, data scientists can derive more insight from data than ever before.
The practice of data science requires a unique combination of skills and experience. A skilled data scientist is fluent in programming languages like R and Python, has knowledge of statistical methods, an understanding of database architecture and the experience to apply these skills to real-world problems. A masters in data science may build upon existing knowledge to ensure that you are best prepared for a long career in this ever-growing field.
The Limitations of Data Science
Though it may sound obvious, data science relies on data. The massive growth of data science was spurred by the availability of massive datasets and cheap computing power. Only with these incredible resources is data science effective. Small datasets, messy data, and incorrect data can waste a lot of time, creating models that produce meaningless or misleading results. If the data doesn’t capture the actual cause of variation, data science will fail.
Careers in Data Science
Data science is needed wherever there is big data. As more and more industries begin to collect data on customers and products, the need for data scientists will continue to grow. To start on the path towards a career in data science, consider these skills to land a data science job.
What is machine learning?
Machine learning creates a useful model or program by autonomously testing many solutions against the available data and finding the best fit for the problem. This means machine learning can be great for solving problems that are extremely labor intensive for humans. It can inform decisions and make predictions about complex topics in an efficient and reliable way.
These strengths make machine learning useful in a huge number of different industries. The possibilities for machine learning are vast. This technology has the potential to save lives and solve important problems in healthcare, computer security and more.
The Inherent Limitations of Machine Learning
Though machine learning may seem like a magic bullet to answer any question, it is not all-powerful.
Machine learning algorithms are better than ever at creating useful results with minimal intervention. However, we may still need engineers and programmers to constrain and optimize these algorithms to make them work on new problems.
There are also plenty of problems that machine learning isn’t particularly good at solving. If a traditional program or equation can solve a problem, adding machine learning might complicate the process instead of simplifying it.
Importance of Machine Learning
Machine learning is being applied in many industries. Cutting costs by letting a machine learning algorithm make decisions can be a lucrative solution to many problems.
Applying these techniques in industries like lending, hiring and medicine raise some major ethical concerns. Since these algorithms are trained on data created by humans, they incorporate social biases into their results.
Machine learning is one of the many tools in the belt of a data scientist. In order to make machine learning work, you need a skilled data scientist who can organize data and apply the proper tools to fully make use of the numbers.
SPONSORED SCHOOL
University of London
Online BSc Data Science and Business Analytics
The online BSc Data Science and Business Analytics from the University of London, with academic direction from LSE, enables students to build essential technical and critical thinking skills and prepare for careers in data science, analytics and other growing fields – while they work, without relocating.
infoSPONSORED
Data Scientist vs Machine Learning Engineer
Ever consider the growth of machine learning and data science to be the reasoning behind the best and popular job attributions that are given to these fields? It’s important to understand that as the technology and data fields grow, careers may very well. Technology careers often intersect, but the difference between a machine learning engineer and data scientist is important to distinguish. Here’s a list of common skills for data scientists and machine learning engineers:
Skills Needed for Data Scientists
Statistics
Data mining and cleaning
Data visualization
Unstructured data management techniques
Programming languages such as R and Python
Understand SQL databases
Use big data tools like Hadoop, Hive and Pig
Skills Needed for Machine Learning Engineers
Computer science fundamentals
Statistical modeling
Data evaluation and modeling
Understanding and application of algorithms
Natural language processing
Data architecture design
Text representation techniques
Data science is a broad, interdisciplinary field that harnesses the widespread amounts of data and processing power available to gain insights. One of the most exciting technologies in modern data science is machine learning. Machine learning allows computers to autonomously learn from the wealth of data that is available.
The applications of these technologies are vast, but not unlimited. Though data science is powerful, it only works if you have highly skilled employees and quality data. To get involved in data science, take a look at some data science masters programs.