A career in data science

Data science employs concepts and methods of data analysis, machine learning, and statistics to gain an understanding and analysis of data-related phenomena. The disciplines of Mathematics, Statistics, Computer Science and Information Technology contribute their theories and techniques in establishing the field of Data Science. The establishment of data science as an independent term is a recent phenomenon. Previously, it was used as an alternative to the term Computer Science. The interaction of data with certain processes and the representation of data through various programs form the study area of ​​computer science. The manipulation, storage, and communication of digital information require competent use of algorithms. Computer science facilitates the use of these algorithms. A computer scientist learns to design software systems and gains a thorough understanding of the theory of computation.

Data literacy helps you ask the right questions and gain insights from big data, teaches you how to manipulate data sets, and gives you the ability to visualize your own findings in compelling ways. A well-designed course trains you on how to handle data science tools. The tools that build the foundation are mathematical tools and computational tools. Deep understanding of these tools and proficiency in handling these tools help suggest data-driven solutions in the business.

Mathematical and applied are two aspects and to learn data science, one has to understand both aspects. Probability, statistics, and machine learning fall under the purview of the mathematical aspect, while the applied aspects help you gain knowledge of data science, languages ​​including Python, MATLAB, JAVA, SQL. It also helps to understand the use of the specific toolset. Applied aspects allow you to enter the real world of data. Training in a data science course gives you experience in collecting big data, as well as analyzing and cleaning it. This training helps you run big data analytics at scale. It also trains you on how to communicate your findings convincingly.

The term that shares a very close association with data science is machine learning. Machine learning deals with algorithms to draw patterns from data and make predictions. For this purpose of making predictions and drawing patterns, machine learning employed data modeling methods. When making predictions, machine learning trains predictive models using labeled data. Awareness of ground reality gives rise to observations that qualify themselves as labeled data. This task of making predictions includes training models to let them know how to predict unknown data from labeled data. Model training can be done using various methods. While some of these methods are simple, like regression, others are complex, like neural networks. While discovering patterns from the data, machine learning tries to find some patterns or find some data associations in a situation where there is no labeled data. While there are more categories for machine learning, these two are part of the main categories.