Data Science is an umbrella term, covering data science technologies as a discipline as well as applications that use these technologies. The main focus of Data Science is the gathering and analysis of data. This is a natural extension of data mining which is a technique used in Data Mining for building an understanding of data by collecting and analyzing data.
This is not a new field. Data scientists have been practising Data Mining for years and have been forming Data Science communities. This is an extension of what they do and also an activity in a broader domain of Analytics.
If you are interested in it for fun or even if you want to make money out of it, then this article on how to become a data scientist can be really helpful for you.
In my opinion, data science is concerned with the acquisition, storage, and analysis of huge amounts of data. You need to have a very good hold on statistics, probability, and machine learning. You will also be required to write reports and presentations based on the analysis of data.
As a data scientist, you will be required to collect data from different sources like the internet and social media. If you are working for a company then they would pay you handsomely if your work meets their requirements.
If you wish to make it big in this field, then you need to have a keen interest in data analytics and statistics. You should also be willing to work for long hours for the sake of your career.
Since the job requires a lot of hard work, you need to have the willpower and patience to succeed. It is not easy but it pays well.
You should be able to predict future results based on historical data and create models using Machine Learning and AI.
Although the Data Science role serves all the above activities, only the modeling stage is always associated with data scientists in all organizations.
What are Data Science technologies?
As per Wikipedia, data science includes a variety of scientific disciplines such as statistics, machine learning, data mining, data visualization, digital twins, digital humanities, expert systems, artificial intelligence, econometrics, cognitive science, computational finance, geomatics, and data science.
Most of the popular data science technologies such as Python, R, Hadoop, and SQL have been around for a while.
Machine Learning and Artificial Intelligence, can be used for a lot of things. Data Science is just one of the use cases. From helping to detect cancer in predicting the outcome of a market, machine learning and AI can do that.
If you have a good hypothesis, then the next step is to collect and analyze data. You might think about how you could get access to more or better data for your experiments if necessary.
But wait for a second! We only have a starting point – and if we want to test our hypothesis, we need to get some more data. It may seem unnecessary to do more experiments since we have already collected the data we need for our research, but science does not work that way.
After this stage, we do some statistics on the collected data in order to draw conclusions from it.
Once we have the data and know how to analyse it, then we can draw conclusions from our results. This is normally done by creating a visualisation of some kind (like a graph or table) that shows the relationship between variables. Data Scientists must be careful to make sure they interpret their findings correctly.
When you have collected the data for your experiment and drawn the conclusions from them, you are done. Now you just need to submit your study to a scientific journal, and the research process is complete.