Data Science consists of six major topics. These are:
- Linear Algebra
- Machine learning
- Data Visualisation
- Data Mining
Through a data science course, one can have a better understanding of these topics. These topics are discussed further in detail through the course of this article.
Statistics is the mathematical branch of business which includes the processes of collecting, classifying, analysing and interpreting the numbers to draw an understanding of them and thus, draw a conclusion.
Statistics is implemented in various ways in the field of data science. These are:
- Experimental Design: The answers to various questions are found through means of experimentation including samples size, control groups, and so on.
- Frequent Statistics: The user is allowed to define the value of the importance of the result of data.
- Modelling: Having statistical knowledge is important for the further success of a data scientist, even though it does not see daily use in their lives. Old statistical models are being slowly replaced with the new models.
- Linear Algebra: Linear algebra is a section of mathematics which involves the process of linear mapping between vector spaces. It sees use in data science in the following ways:
1. Machine learning: When working with data that is dimensionally high and involves matrices, linear algebra comes in very handy. It’s component analysis, and regression techniques see the most use along with eigenvalues principals.
Coding is a very important part of data science and being able to code well is extremely important for any data scientist. Having a background in computer science is thus a large advantage, however, if one does not have such a background then these skills can easily be picked up through a data science course.
Automating tasks not only saves time and effort but also helps make the process of debugging, understanding and maintaining codes simpler. The practical skills involved in programming are as follows:
- Being comfortable with data development. Usually, people with a software development background find it easier to work on commercial projects at a higher scale.
- Having experience in the database area, such as knowledge of modern databases like NoSQL and cloud as well as on older databases like SQL, is important to any employer.
- Teamwork and collaboration are important as most work as a data scientist would be tone in groups. Thus communication with teammates and holding strong relationships would help keep productivity at a maximum.
Important practices here involve:
- Avoiding the use of hard values
- Documentation and commenting continuously
- Refactor the code
Machine learning is important in data science and has shown use in a large number of groundbreaking technologies like self-driving cars, drones, image classification, speech recognition, so on and so forth. This field is expanding every minute and expanding very quickly. Thus the knowledge of machine learning and its implication would be necessary for any good data scientist.
The process involving the exploration of data and extraction of vital information is called Data Mining. A data science course makes the understanding of such a topic much clearer. The commonly used slang in data mining are listed below.
- Data wrangling/Data munging
- Data Cleaning
- Data scraping
Even though the term may seem self-explanatory, there is more to it than what we see. Data visualisation is the process of communication of data and its results through pictorial or graphical representation. The goal of it is to communicate the findings of the data in the simplest way for understanding.
Thus a data science course would further equip aspiring data scientists with all the tools in the toolkit necessary for optimal success in their career.