Data Science and Machine learning basics
There is a lot of interest in Data science and Machine learning, Let’s get a quick grasp on what these terms mean and what we need to learn to be a part of this profession.
Machine learning:
- Is about coding, and doing mathematics on data.
- Applying algorithms to data to get productive outcomes.
- Finding patterns that can be exploited for business outcomes.
- Real-time applications like chatbots, image recognition, and product choice suggestions based on customer behavior, are irrelevant to specific domain expertise.
Data Science:
- Is a combination of Coding, Statistics, and Domain expertise.
- a similar job like research lab scientist may be true to an extent, but not completely,
- If you are sanitizing the data for analysis by writing a program you are doing the data science job, usually, python or R language is used along with SQL may be (an example if you want to pipe the data to a database)
- If you are managing the storage of large amounts of data, you are doing the data-science job.
- Deriving product offerings from the data after applying machine learning and algorithms, this in term monetizes the data.
- Data-science is a very diverse field, with many different tools, and many different teams, like coding, statistics, design, presentation, etc…
- at the end of the day, each data science project is teamwork of coders, statisticians, engineers, business, and management.
