Data science and Machine learning |Brief Introduction

The machine learns from data

Data science and machine learning are popular fields in the business and IT industries over the decade. Getting a basic knowledge in these fields will be essential to establish or to change your career in those industries. This article gives you the idea for kickstart.

What is Data Science?

Photo by Carlos Muza on Unsplash

Wikipedia says,

Data science is a multi-disciplinary field that uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from structured and unstructured data.

Data is everywhere. We are using thousands of data every day. The Internet is the main source of data where we are spending more time.

Eg:- When you search something in a search engine, the accurate results are obtained through data science.

Here the search engine first finds the words you entered in the search bar(called keywords). Then, go through the data of almost every website. Finally, it will find the specific page where your results found.

For such purposes, the search engine is fed with data of many websites in various forms such as posts, pages, images, etc. to obtain a particular algorithm.

Artificial intelligence vs Data Science vs Machine learning

If you have a misconception about artificial intelligence and data science, the above diagram clarifies the relationship.

Artificial intelligence is a vast field covers many subfields. Data science is entirely a different field. The major part of data science is involving in artificial intelligence and its sub-fields.

Deep learning is currently trending subfield in machine learning.

What is Machine learning?

Machine and human brain

Wikipedia says,

Machine learning (ML) is the scientific study of algorithms and statistical models that computer systems use in order to perform a specific task effectively without using explicit instructions, relying on patterns and inference instead.

Machine learning is the modern path of data science. Data science in tradition uses statistical theorems only. Nowadays, the usage of statistical concepts and machine learning is inevitable to obtain good results.

In Computer science, Machine learning is a kind of programming.

The programming task is huge when trying to solve a complicated problem. Some time thousands of lines of code are needed. In such cases, if we have a considerable amount of data, we can use that data and find an algorithm to solve the problem, using machine learning.

It could be explained simply as follows,

Machine manipulates the data points(single data) again and again to find insights of data, i.e learn through data is called Machine learning.

In data science, machine learning is used for programming the data with statistical theories to find a suitable model (machine learning model) for solving real-world problems.

Photo by Agence Olloweb on Unsplash

Some machine learning models

  1. Regression
  2. Classification
  3. Clustering
  4. Decision Trees
  5. Neural Networks

Individually the in-depth article is coming soon.

Some applications of data science and machine learning.

  1. Search engines — to obtain accurate results.
  2. Recommender systems — to increase sales.
  3. Fraud detection — to ensure the security of card payments
  4. Weather predictions — to obtain good weather values from historical data.
  5. Image recognition — to find the images autonomously using their features (Face unlock).
  6. Voice recognition — to differentiate voices of different people.
  7. Natural language processing — to work with human orders (personal assistant ).

Why Data Science?

Photo by Dylan Gillis on Unsplash

Title of an article published on the Harvard Business School website.

Data Scientist: The Sexiest Job of the 21st Century

Important features of data science,

  1. Need in almost every industry.

The statement of Harvard Business School simply explains the importance of data science.

Many industries having piles of unused data. They are used mainly for annual reports and revenue calculations only. After that, the piles are trashed to create room for the next piles.

Nowadays those industries got known the importance of data science. Then they’ve started to hire data science experts with them. Such that every industry needs data science.

2. Handsome Salary

According to Glassdoor,

https://www.glassdoor.com/List/Best-Jobs-in-America-LST_KQ0,20.htm

The data scientist is the best job in the US due to its salary, openings and job satisfaction.

Approximately average data scientist’s salary in the US is about 100 bucks.

3. Evolution

The evolution of data science is incredible. Within a decade, various data science jobs are evolved. Some of them are,

  • Data Analyst
  • Data Engineer
  • Machine Learning Engineer
  • Big data analyst
  • Big data architect

Big data is the modern term used for denoting an extremely large amount of data. ( Minimum size of 100 Terabytes according to IDC)

Comparison of data science with other job titles on the basis of salary.

Source: O’Reilly Salary Data Science Salary Report, 2016

Reference: https://datasciencedegree.wisconsin.edu/data-science/data-scientist-salary/

More than 45% of salary share is held by data science.

Conclusion

Data science is working with data to solve problems. Machine learning is a practical approach to data science. The application of data science is inevitable in many industries. The need for data science is increasing day by day. Data science is still evolving. The salary of data science is admirable. So that gaining knowledge in data science is always appreciable. Start learning today.

I'm an AI enthusiast who focuses on research and developement of business solutions using data science and machine learning.