How to Become an Expert in Data Science
There are many skills required to become an expert in data science.
But what is most important is mastery of the technical concepts. These include various factors like programming, modeling, statistics, machine learning, and databases.
Programming
Programming is the primary concept you need to know before heading into data science and its various opportunities. To complete any project or carry out some activities related to it, there is a need for a basic level of programming languages. The common programming languages are Python and R since they can be learned easily. It is required for analyzing the data. The tools used for this are RapidMiner, R Studio, SAS, etc.
Modeling
The mathematical models help with carrying out calculations quickly. This, in turn, helps you to make swifter predictions based on the raw data available in front of you. It involves identifying which algorithm would be more befitting for which problem. It also teaches how to train those models. It is a process to systematically put the data retrieved into a specific model for ease in use. It also helps certain organizations or institutions group the data systematically so that they can derive meaningful insights from them. There are three main stages of data science modeling: conceptual, which is regarded as the primary step in modeling, and logical and physical, which are related to disintegrating the data and arranging it into tables, charts, and clusters for easy access. The entity-relationship model is the most basic model of data modeling. Some of the other data modeling concepts involve object-role modeling, Bachman diagrams, and Zachman frameworks.
Statistics
Statistics is one of the four fundamental subjects needed for data science. At the core of data science lies this branch of statistics. It helps the data scientists to obtain meaningful results.
Machine Learning
Machine learning is considered to be the backbone of data science. You need to have a good grip over machine learning to become a successful data scientist. The tools used for this are Azure ML Studio, Spark MLib, Mahout, etc. You should also be aware of the limitations of machine learning. Machine learning is an iterative process.
Databases
A good data scientist should have the proper knowledge of how to manage large databases. They also need to know how databases work and how to carry on the process of database extraction. It is the stored data that is structured in a computer’s memory so that it could be accessed later on in different ways per the need. There are mainly two types of databases. The first one is the relational database, in which the raw data are stored in a structured form in tables and are linked to each other when needed. The second type is non-relational databases, also known as NoSQL databases. These use the fundamental technique of linking data through categories and not relations, unlike relational databases. The key-value pairs are one of the most popular forms of non-relational or NoSQL databases.
