I think before you visit my website you already tried to google data science and of course, you got a huge amount of confusing information.
But let me tell you data science is not a complicated field. It is a set of methodologies dealing with many forms of data, that are available today, such as email, click, credit card swipe, and tweet, all these forms of data can describe the present or predict the future.
Data can describe our current state like food consumption, energy production, etc. This process can be done with a dashboard or alerts, streamlining time-intensive reporting processes. It can help you detect uncommon events like detecting bank fraud.
If we have data on what has happened earlier, we can increase efficiency by automatically catching a new event that is unexpected or strange. Data can also analyze the causes of empirical events and behaviors, for instance, your activity on Facebook or Twitter.
Rather than determining correlations between short numbers of circumstances, data science strategies help us understand complex systems with many probable causes. Data can predict future possibilities, such as forecasting weather. We can use new methods to take multifarious causes into account and predict potential results. Further, we can estimate the probability of our prediction mathematically to clarify our level of uncertainty.
So now we know what data science is. The next question is why is it so widespread. The answer is VERY CLEAR, we’re gathering data more than before.
All of that data is automatically entered into a computer, and integrated with the data from hundreds of dealerships into one big database.
For instance, when you buy a car, it’s easy to use the email address that you provided when you bought that car to connect your car purchase data with your data from social media or web browsing. Suddenly, we have a very complete picture of everyone who bought a car in the last year like their ages, their likes, and dislikes, and their friends and family. This extra data can be used to indicate what price you can pay for your car, what other purchases you’re likely to make, or how best to sell your insurance for that new car. Data is everywhere, and it is incredibly valuable information for businesses, organizations, and governments.
The data science workflow
In data science, we commonly have four steps to any task. First, we gather data from many sources, such as surveys, web traffic results, geo-tagged social media posts, and financial transactions. Once collected, we store that data in a safe and accessible way.
Data is in its raw form, so the next step is to prepare data. This includes “cleaning data”, for example finding missing or duplicate values, and converting data into a more organized format.
After that we explore and visualize the cleaned data, this could involve building dashboards to track how the data changes over time or performing comparisons between two sets of data.
In the final step we run experiments and predictions on the data, this could involve building a system that indicates climate changes or performing a test to find which web page gains more customers to plan your SEO strategy.
Now you know why data science is important and the first four steps in the data science workflow, you can explore more courses to start your data science learning.