Every purchase, every online presence of every human is piled into an enormous amount of data over time. If organized and analyzed well, this data can serve as the key to solving problems and leading the market. This process of uncovering, organizing, and extracting actionable insights from this big data is called data science. Sounds easy, doesn’t it?
Well, this is exactly where the widely hated mathematics comes in. Mathematics, scientific methods, machine learning, and AI are the tools required to organize, process, manipulate data, and extract actionable insights from the pile of data collected over time.
Thus, the data scientists were able to improve the human experiences and lead the markets through projects such as google search autocomplete, text autocorrect, face recognition in an image, and many more successful projects
The fundamental steps in a data science project are:
- Data preparation – involves data cleansing, aggregating, and manipulating to produce a data set ready to be analyzed
- Advanced data analytics – at this stage we develop and apply algorithms to find patterns in the dataset and extract business insights from predictions
- Visualization – applying data visualization tools to communicate the results of the analysis, the extracted patterns, and business insights, to the stakeholders