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How to Become an Expert in Data Science
There are many skills required to become a data science expert.
But what is more important is the mastery of technical concepts. These include various factors such as programming, modeling, statistics, machine learning and databases.
Programming
Programming is the primary concept that you need to know before going into data science and its various opportunities. To complete any project or carry out some activities related to it, a basic level of programming languages is needed. Common programming languages are Python and R as they can be learned easily. It is necessary to analyze the data. The tools used for this are RapidMiner, R Studio, SAS, etc.
Model
Mathematical models help to make calculations quickly. This, in turn, helps you make faster predictions based on the raw data available in front of you. It involves identifying which algorithm would be best suited for which problem. It also teaches how to train those models. It is a process to systematically put the retrieved data into a specific model for ease of 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: design, which is considered as the primary step in the model, and logical and physical, which are related to disintegration of data and arranged in tables, charts and clusters for easy access. U 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 charts.
Statistics
Statistics is one of the four fundamental subjects required for data science. At the heart of data science lies this branch of statistics. It helps data scientists achieve meaningful results.
Machine Learning
Machine learning is considered to be the backbone of data science. You need to have a good grasp on 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.
database
A good data scientist must have the correct understanding of how to manage large databases. They also need to know how databases work and how to continue the database extraction process. It is the collected data that is structured in the computer memory so that it could be accessed later in different ways for the need. There are mainly two types of databases. The first is the relational database, in which raw data are stored in a structured form in tables and are linked to each other when necessary. The second type is non-relational database, also known as NoSQL database. These use the fundamental technique of linking data through categories and not relationships, unlike relational databases. Key-value pairs are one of the most popular forms of non-relational or NoSQL databases.
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