Unveiling the Heart of Data Science in 21st century
The terms “Data science” and the “heart of data science” have become very common in the 21st century but only few understand the meaning and its importance.
Data science is that new branch of study dealing on the numerous volume of data available in the society today using the modern data collection techniques. From creation, collection, organization and possibly publication of data to other undefined functions surrounding data handlings.
Someone referred to as a data scientist can be said to data creator through coding of programs using statistical knowledge. Developing strategies for the analyzing of data, building models with data through the help of programming languages, and general working abilities with modern software applications such as Python and R.
The heart of data science revolves around;
- Data analysis
- Programming languages
- Machine learning
- Data visualization
- IDE
- Math
- Deploy
- Web Scraping
Let us discuss each of the above points briefly for better understanding of the real meanings of the heart of data science.
Data analysis
This has to do with feature engineering, data wrangling and EDA. The ability to analyze available data and figure out the necessary information and meaning from it.
Programming languages
There are numerous programming languages available today use for diverse purposes for instance, Python programming language, JavaScript, java, r, C, C++, etc. data science surrounds the ability to understand, code, and operate effectively with these programming languages.
Machine learning
Machine learning deals with the classification, regression, reinforcement learning, deep learning, dimensionally reduction, and clustering of data. It is a modern skill that surrounds the understanding of modern software operations and their applications functionalities.
Data visualization
Data visualization involves the use of tableau, power BI, Matplotlib, GG plot, seaborn, etc. to visualize data to gain proper understanding and easy explanation to others.
IDE
This is another heart of data science involving the use of Pycharm, Jupyter, colaboratory, spyder, and R-studio to operate, evaluate, and analyze data. A modern approach to data handling that is associated with data science.
Math
Data science also involves the use of mathematics approach such as linear algebra, statistics, differential calculus, etc. to analyze, create, correct, and/or do anything with data.
Deploy
Data science also involves the use of the available online services such as AWS, Azure, etc. in the handling the necessary operations around data and its managements.
Web Scraping
The term ‘web scraping’ means looking at the existing data handling practice on the web, obtaining the practice to improve the new one. An approach aimed at ensuring continuous improvements in data science as the time progresses.
Having known these few list of data science areas, let us know what you think and let us know the areas we missed in this topic.
