Difference between Data Science and Machine Learning

Data science is the study of data cleaning, creating data models and then analyzing them to find insights from the data collected. Machine Learning is a branch of Artificial Intelligence and a sub field of Data science that allows computers to learn from data.

What is Data Science?

As the name implies, Data science deals with data. It is the study of huge amounts of data of an organization. Data science uses statistical methods, machine learning algorithms and some analytical techniques and apply them on data so that they can develop some useful insights from that data which is used for the growth of an organization.

Data scientists are the people who are excelled in performing these techniques on raw data and helping the organizations to take better decisions. Many organizations such as Netflix, Amazon etc., use these data science techniques to understand the user interests and collecting trends so that necessary changes can be made to improve their services.

Data is studied in the following ways using Data science:

• Descriptive analysis − To gain insights using visualizations

• Diagnostic analysis − Detailed data examination

• Predictive analysis − Predicting future patterns based on historical data

• Prescriptive analysis − Analyses all the outcomes and recommends the best

It is an interdisciplinary field which involves the collection of raw data, cleaning the data, visualizing the data, analyzing the data, applying statistical and machine learning algorithms on it, and then developing some insights which helps business organizations to take decisions. This in turn increases the profit of that organization.

Skills Required to Become a Data Scientist

• Statistics, Calculus and Linear Algebra

• Data cleaning and Data Mining

• Data Visualization

• Programming languages such as python, R, SAS, Scala etc.,

• Databases SQL, MongoDB etc.,

• Data tools like Hadoop, Tensor flow, Pig, Hive etc.,

• Machine learning

One can use the concepts of Data Science to discover new patterns, create new products, perform real-time optimization, etc.

What is Machine Learning?

Machine learning is a branch of Artificial Intelligence (AI) and computer science that allows computers to learn and make their own decisions to solve problems without being explicitly programmed. It applies statistical tools on data to extract patterns and rules so that they can predict the future outcomes.

Machine learning is used to make decisions without any human intervention. It creates different kinds of solutions for the same existing data and selects the best fit among them. It makes sure that this solution can also be used for all other data sets. The main goal of machine learning is to enable computers to learn on their own and make decisions with minimal human involvement.

Using historical data, machine learning algorithms create mathematical models which make decisions without any explicit programming.

Image recognition, Speech recognition, email filtering, Facebook auto-tagging etc., are all examples in which Machine learning is being used.

Skills required to become an ML engineer

• Computer science Fundamentals

• Applied mathematics and statistics

• Python

• Data evaluation and modelling

• Machine learning algorithms

• Neural networks

• Natural language processing

• Communication skills

Difference between Data Science and Machine Learning

The following table highlights the major differences between Data Science and Machine Learning −

Data Science	Machine Learning
Data science is the deep study of data to extract valuable insights from it.	Machine learning is a branch of Artificial intelligence which allows computers to take decisions
It is used to identify the hidden patterns in the given data which can be used to take data driven decisions by the organizations that can benefit them	Machine learning enables computers to create an efficient solution for a problem without the involvement of humans
Steps to create this module include data extraction, cleaning, visualizing, analyzing, modelling and then taking decisions	In context of data science, the steps of creating this module includes all the steps of Data science and then applying mathematical and statistical analysis and machine learning algorithms to create a best solution
It works on raw, structured or unstructured data	It mostly works on structured data
A data scientist should have skills to use tools such as Hadoop, Hive etc., statistics and programming languages such as Python and R	The skills required for a machine learning engineer are computer science fundamentals, programming skills in python and R, applied mathematics, statistics, etc.
Data science allows you to create insights and patterns from data dealing with real-world complexities	Machine learning allows us to predict the results for new data based on existing data using algorithms
Data science is not a branch of Artificial Intelligence	Machine learning is a branch of Artificial intelligence
It involves data processing along with usage of algorithms and statistics	It is solely depend on algorithms and machine learning
It involves data cleaning, data visualization, data mining, etc.	Unsupervised, Reinforced and supervised are the three kinds of Machine learning
Example: Netflix uses Data science technology	Example: Facebook uses Machine learning technology

Conclusion

Data science is a multidisciplinary field that applies various technologies on huge amounts of data to understand data and take necessary decisions. Machine learning is a study which gives computers the power to learn and take decisions on their own based on the existing data.