Introduction
Data science and Machine learning are growing markedly. Companies are on the lookout for professionals who can screen through these domains. There has been a lot of conversations and debates among experts contemplating Data Science vs Machine Learning. With the exponential growth of data science and machine learning in the leading organizations, a research study by IBM states that in 2020-2021 the job markets for U.S. data will rise from 350,000 to 27,00,000. This is major progress towards data science and machine learning.
Scroll down to get a thorough understanding of data science and machine learning and a comparison of data science vs machine learning.
What is Data Science?
In simple words, data science is the method of using data to find solutions or predict a problem statement’s result.
The study of data is known as data science. It generally comprises generating techniques of recording, reporting, stocking, and evaluating data effectively extra useful input to make receptive decisions. Data science aims to achieve ideas and knowledge from any data, both formed and unformed.
Data science is being used across different industries:
- With the advancements in prognostic modelling, data scientists can help predict the outcomes of disease given in the historical data of the patients.
- With data science, banks can manage their resources efficiently and make wise decisions.
- Through fraud detection in the transport sector, data science is used in the automation of self-driving cars.
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What is meant by machine learning?
Machine learning can be defined as the practice of using complex algorithms to collect data, analyze, and make predictions for a particular topic. Like how humans learn from their past experiences, machines also understand, give reasoning, and act upon after learning from the past data. Machine learning is the essence of many futuristic, technical developments.
Machine learning examples: Tesla’s self-driving car, Apple Siri, and Sophia AI robot, etc.,
Machine learning is the subdivision of data science. It mainly focuses on the design of systems. Predictive analysis and statistical analysis are used to notice trends and capture input based on extracted data.
Are data science and machine learning the same?
No, data science and machine learning are not the same. Data science deals with big data, and machine learning derives actionable reasoning from the data set collected.
What is the difference between data science and machine learning?
The underlying difference between data science and machine learning is what daunts the debate on the topic of data science vs machine learning.
There are certain features when it comes to the discussion of data science vs machine learning, and these key features distinguish data science and machine learning.
Machine learning is disciplined under data science and imparts and empowers machines to think and act themselves. Data science is a discipline that utilizes the combination of mathematical, statistical, and computational tools to acquire, process, and analyze big data.
The key features are as follows:
Data science
- Presents and notifies outcomes
- Analytical skills to develop business requirement.
- Insights are taken from historical data
- It helps optimize the business
- Analyze results
- Classification and regression
- Supervised and unsupervised algorithms
- Focuses on algorithms and statistics
Machine learning
- Feature importance
- Universal selection
- Incorporation of models into UI/warehouse/table
- Planning
- Measuring
- Automation
- Emphases on programming and software engineering
Data science vs Machine learning
The insights of data science vs machine learning are important for all data science professionals.
Machine Learning comes under the Data Science field, as Data Science is vast for numerous disciplines.
Data science dives deep at a fine level of data to unearth and comprehends sophisticated behaviours and tendencies. It can highlight unseen ideas, which can help organizations to make quicker business determinations.
In machine learning, you teach machines by strengthening the machines by data and allowing machines to memorize on their own without any human interference. The process of learning in machine learning starts with data compliance, such as direct understanding and teaching, to look for structures in data and make better judgments in the future.
Skills to become a better professional in the domains of data science and machine learning.
Data Science
- Use big data tools such as Pig, Hive, Hadoop
- Understand SQL database
- Programming languages like Python and R
- Data visualization
- Data mining
- Data cleaning
Machine learning
- Text representation skills
- Data modelling and evaluation
- Fundamentals of computer science
- Natural language processing
- Data architecture design
- Application of algorithms
Conclusion
As the world is moving towards the life of technology and companies offering jobs with the descriptions in this domain, it is required to have well-versed knowledge of data science and machine learning. There are some common skills in both fields. This article must have given some basic and necessary awareness of data science vs machine learning.
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References
https://www.jigsawacademy.com/blogs/data-science/data-science-vs-machine-learning/amp/#
