When Big Data, Artificial intelligence and digitalisation are the order of the day, data science and machine learning figure as two significant domains which are rapidly changing the corporate landscape. They are, in fact, complementary in nature.
What Is Data Science?
Data science is the incorporation of multiple disciplines including data analytics, software engineering, data engineering, machine learning, analysis, business analytics and more. It includes retrieval, collection, ingestion and transformation of large amounts of data, collectively known as Big Data. Data science is responsible for bringing structure to big data, searching compelling patterns and finally advising decision-makers to bring in the changes effectively to suit business needs.
What Is Machine Learning?
Machine learning is a tool used by data science. Machine learning is an application of artificial intelligence that provides systems with the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning focuses on the development of computer programs that can access data and use it to learn for themselves. The primary aim is to allow computers to learn automatically without human intervention or assistance and adjust actions accordingly. It makes use of algorithms which are categorised as supervised and unsupervised.
Essentially, machine learning software comprises statistical analysis and predictive analysis that are used to spot patterns and catch hidden insights based on perceived data. When a site recommends “You might also like” products, machine learning is at work.
To be a successful data scientist knowledge of machine learning, understanding multiple analytical functions, hands-on experience in SQL database coding and a strong knowledge of Python, SAS, R etc. is a must. A data scientist would also be required to use distributed computing framework like HADOOP and data storytelling.
Expertise in computer fundamentals, data modelling and data evaluation skills, knowledge of probability and statistics and in-depth knowledge of programming skills are the required skills for the domain of machine learning. Python and R are the most popular programming languages for machine learning.
Responsibilities Of A Data Scientist
To understand the differences between the two it is essential to understand the roles and responsibilities of each. The responsibilities of a data scientist entail:
- Data cleansing and processing
- Identifying new business questions that can add value to the organisation
- Developing new analytical methods and machine learning models
- Correlate disparate data sets
- Conduct causality experiments
- Data storytelling and visualisation
Responsibilities Of A Machine Learning Engineer
- Designing and developing machine learning and deep learning systems
- Running machine learning tests and experiments
- Implementing appropriate machine learning algorithms
- Perform statistical analysis
- Extend existing machine learning libraries and frameworks
Ideally, a machine learning course would focus on creating systems to utilize and learn from large sets of data. Topics of study must include predictive algorithms, natural language processing and statistical pattern recognition. The course should enable one to master machine learning concepts and techniques including supervised and unsupervised learning, mathematical and heuristic aspects, hands-on modelling to develop algorithms.
For data science, the options include undergraduate/post-graduate courses. Certifications on various aspects of data science are also available.
In Demand Domains
Data science and machine learning are in great demand in the industry as of now. A combination of the right skill sets and real-world experience can result in a very strong career.