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From students to large organizations, all are after the latest techniques in the field of information technology. Be it small or large businesses – all of these thrive on their data assets. For this, ML and data science are the most searched technological terms in the 21st century. Both terms fall in the same domain but have specific meanings and applications. Provided below is a comparison of data science vs. ML.
The difference table:
|Framework||Data Science||Machine Learning|
|Area||A broad term for multiple disciplines||An area within data science|
|Field about systems and processes to extract data from unstructured and structured data||Field of study that provides computers the ability to learn without explicit programming|
|Example||Amazon, Netflix, the healthcare sector||Siri, Alexa, ML technology used by Facebook to recognize faces and languages|
|Job roles||Data scientist
Natural Language Processing (NLP) scientist
Meaning and example
Data science is defined by Dr. Thomas Miller of Northwestern University as “a combination of modeling, business management, and information technology”. It is an interdisciplinary area that brings together different scientific methods, processes, people, and systems to extract insights from data in different forms. This data can be structured with texts and numbers grouped or unstructured with separately recorded individual values. Data scientists excel in converting data into vital business matters. The airline, healthcare, digital marketing, and entertainment sectors commonly use data science.
Machine learning on the other hand is about different algorithms that computers use to learn from available data rather than being specifically programmed to interpret it. For instance: -Virtual assistants like Alexa or Siri use machine learning algorithms to convert our spoken request to text and then evaluate it to understand the exact action to be carried out.
Algorithms are not the mere focus of data science rather are about data processing, combining scientific methods, math, and even statistics to uncover and explain in-depth business insights whereas machine learning is a wide area within data science with different methods.
For aspirants of data science, some of the lucrative job roles include:
• Data Scientist:
Different data trends are investigated by a data scientist to evaluate the effect on a business. For individuals seeking a career as data scientists, the Data Science Council of America (DASCA) offers Senior Data Scientist (SDS) and Principle Data Scientist (PDS) certifications among the best data scientist certifications to enhance your career.
• Data Analyst:
Determining industry trends by evaluating data is what a data analyst does. In addition, he or she assists in the development of an organization’s business plans.
• Data Engineer:
Data or computer engineers are referred to as organizations’ backbone in charge to manage, create and design a massive database. They maintain an adequate flow of data and design data pipelines. Cloudera Certified Professional (CCP) is the most preferred data engineer certification for employees having in-depth and high-level mastery in data engineering.
For machine learning aspirants, some of the lucrative job roles include:
• ML engineer:
ML engineers are responsible for utilizing algorithms to improve machine learning applications and systems. They are to improve and mold self-learning, effective ML applications through fine-tuning and statistical evaluation based on test results.
• Natural Language Processing (NLP) scientist:
NLP scientists engineer and design software and computers that can convert spoken words to other languages and learn human language speech habits.
• Software developer/engineer
Software developers/engineers are primarily responsible to create intelligent computers and machine learning algorithms.
While there are no specific qualifications to become a data scientist, there are certain key skills you might require to become one.
Most data scientists have a master’s degree or a Ph.D in computer science, statistics and mathematics. Basic knowledge of Hadoop can be beneficial for candidates seeking careers in this field.
Certifications: For graduates and professionals seeking to earn credentials can opt for ML with Python, ML by Stanford University Online, and ML at Udacity certifications. These are among top machine learning certifications available online.