There are some major concentrations and concepts that create data science. Every concept comes with its unique definition and role. Some of the most relevant topics under data science include statistics, Programming, linear algebra, data visualization, data mining, and machine learning.
Statistics
Statistics fall under mathematics and it deals with the collection, analysis, classification, as well as interpretation of facts to draw inferences based on quantifiable likelihood. Statistics is significant in the sense that it helps in the interpretation of data and coming up with the best results. Statistics covers areas such as experimental design, frequent statistics, and modeling. The importance in data science depends on the nature of the tests carried out.
Linear Algebra
This is another branch of mathematics like statistics. However, linear algebra has to do with linear mappings and vector spaces. There are different ways in which algebra can be prominent and significant in data science and they include machine learning, modeling, and optimization.
Programming
When you want a career in data science, it is essential that you learn the code. This is why you notice most data scientist have got a background in computer science. However, even if you do not have any programming experience, you can still learn. Programming is very essential for data scientists. In data science however, writing programs help you save a lot of valuable time and it also becomes easier to understand, debug and maintain. Programming involves development, database management, and collaboration.
Machine Learning
Machine learning falls under artificial intelligence. This is where intelligent machines are created. Machine learning has become an important part of data science and it has a lot of significance. With machine learning, we learn how computers can self-program so that it is not necessary to come up with explicit instructions.
Data Mining
Data mining is a process where data is explored so that the most important information can be extracted. Data mining is yet another wide topic that has other sub-topics falling under it. By understanding data mining, it becomes easier to define the meaning and the usefulness, differentiating the topics under data mining can be a bit tricky because they are quite similar to say the least.
Some of the topics that fall under data mining include:
Data wrangling: this is where the data is converted from its raw form to one that is easier to use. It involves some important steps that include parsing and cleaning to structures that are predefined.
Data mungling: this is almost the same as data wrangling.
Data cleaning: this is an important step that deals with the detection and correction/removal of inaccurate, corrupt or values that are missing within a dataset within any given time.
Data scraping: this is a technique where computer programs read data from other websites or programs.
We all want to come up with some predictive models and add visualization as well. However, you notice that these things never really come to pass until you have done the dirty and hard work first. Data collection and preparation are some of the tasks that take up data scientists time.
Resource
A course in data science can get you ready for a career in data science. This is one of the most lucrative jobs that you can get currently and that is why many young professionals are choosing this area.
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