{"id":6121,"date":"2022-01-16T18:14:00","date_gmt":"2022-01-16T12:44:00","guid":{"rendered":"https:\/\/www.mygreatlearning.com\/blog\/different-aspects-of-data-science-and-related-fields-such-as-machine-learning\/"},"modified":"2024-09-03T14:59:06","modified_gmt":"2024-09-03T09:29:06","slug":"different-aspects-of-data-science-and-related-fields-such-as-machine-learning","status":"publish","type":"post","link":"https:\/\/www.mygreatlearning.com\/blog\/different-aspects-of-data-science-and-related-fields-such-as-machine-learning\/","title":{"rendered":"Different aspects of Data Science and related fields such as Machine Learning"},"content":{"rendered":"\n

The future is already here and data science courses<\/a> are already leading the way. Hence it is no surprise that data science is creating millions of jobs worldwide. Tech giants like Facebook, Google, IBM are spending millions of dollars in research and development of different aspects of Data Science like Machine Learning and Artificial Intelligence. It is also one of the most sought-after job on job searching websites like Linkedin, Glassdoor and Monster. And if you are wondering what skills a data scientist requires, then read on.<\/p>\n\n\n\n


To begin with, let\u2019s talk about what is Data Science<\/strong>?<\/p>\n\n\n\n

\"dse\"\/<\/figure>\n\n\n\n


As the name suggests, Data science deals with \u2018data\u2019<\/a>- large amounts of data. This data is grouped, classified and structured and then useful insights are drawn from it that help the development of businesses. Reading this data though in theory may sound simple, it\u2019s actually not. That\u2019s where the \u2018science\u2019 part comes into the picture. In order to read the data, many tools and algorithms have to be used to visualise, structure and then read and derive insights.
Data science is used as a rather broader generic term these days when people use the word Data science they don\u2019t mean the textbook definition of Data Science but rather all the different fields that come under Data Science, like, Data Analytics, Data Science and Analytics<\/a>, Machine Learning and Artificial Intelligence.
Each field is unique in its own way and perform their own tasks and functions.<\/p>\n\n\n\n

Find out the difference between data science, AI and ML.<\/a><\/em><\/p>\n\n\n\n

Data science flow-chart<\/strong> <\/h2>\n\n\n\n

This chart shows the flow in Data science, right from obtaining the data to predicting the insights, along with all the skills and tools required for that particular stage of the flow-chart.<\/p>\n\n\n\n

    \n
  1. Data collection<\/li>\n\n\n\n
  2. Data wrangling<\/li>\n\n\n\n
  3. Data exploration<\/li>\n\n\n\n
  4. Data modelling<\/li>\n\n\n\n
  5. Report<\/li>\n<\/ol>\n\n\n\n

    Step 1:<\/strong>
    Obtaining the Data
    <\/strong>This is obviously one of the first and foremost steps. First you need to identify what kind of data you want to analyse, and then you need to export this to an exel or csv file. The next step would be to make this data easily readable. Basically, it should be labelled and structured the right way so that it is easy to analyse.
    Skills and tools required<\/p>\n\n\n\n