Four Pillars Of Data Science

Introduction:

In the world of data space, the era of big data has arrived as companies work with petabytes and exabytes of data. Until 2010, data storage was very difficult for the industry. Now that popular frameworks like Hadoop and others have solved the storage problem, the focus is on data processing. This is where data science plays a big role. Data science Training in Chennai is evolving in many ways today, so prepare for the future by learning what it is and how you can add value to it. Data science means different things to different people, but at its core, data science uses data to answer questions. This definition is rather broad. That's because data science is a pretty broad field.

Four Pillars Of Data Science:

Data scientists typically come from a variety of educational backgrounds and professions, and most should be familiar with or, ideally, master four core disciplines.

1. Domain Knowledge

2. Math Skills

3. Computer Science

4. Communication Skills

Domain knowledge:

Most people think that domain knowledge is not important in data science, but it is very important. The main goal of data science is to extract useful insights from that data so that it can benefit a company's business. If you are not familiar with the business side of the business, how a business model works, and how you can't build it better, then this business will not be of any use to you. 

You need to know how to ask the right questions to the right people so you can see the right information you need to get the information you need. Some visualization tools are used in business, such as Tableau. It helps to present valuable results and insights inappropriate, non-technical formats such as graphs and pie charts that business people can understand.

Math Skills:

Math skills are very important when entering the world of data science. If you skip this part in the first place, you are guaranteed to return halfway through this section. Because when you are going to apply a complex ML algorithm to build your model, you need to understand the math behind this complex algorithm. Before we delve into data science, we need to cover: Consider this the most important and important part of data science.

• Linear Algebra, Multivariate Computing, and Optimization Techniques – These three are very important as they help you understand different machine learning algorithms that play a vital role in data science.

• Statistics and Probability: Understanding statistics is very important as it is a part of data analysis. Probability is also relevant to statistics and is considered a prerequisite for mastering machine learning.

Computer Science:

Computer science plays an important role in data science. Whether drawing complex diagrams or implementing complex machine learning algorithms, it is impossible without programming languages ​​such as Python and R. To manage a large amount of data, you need to have knowledge of relational databases, SQL programming language, MongoDB, etc. Here is a list of computer skills you should have.

Programming Knowledge: You should have a good understanding of programming concepts such as data structures and algorithms. The programming languages ​​used are Python, R, Java, and Scala. C++ is also useful when performance is critical.

Relational databases: In order to get the data you need when you need it, you need to be familiar with databases like SQL and Oracle.

Non-relational databases: There are many types of non-relational databases, but the most commonly used are Cassandra, HBase, MongoDB, CouchDB, Redis, and Dynamo.

Machine learning: One of the most important parts of data science and the hottest research topic among researchers, new developments occur in this field every year. At a minimum, you should understand the basic algorithms of supervised and unsupervised learning. There are many libraries available in Python and R to implement these algorithms. The second part is map shortcuts for working with data. You can write maps in Java or Python programs. There are many other tools like PIG, HIVE, etc.

Communication Skills:

Does it include communication with this? What happens in Project Project data is after it lasts, the conclusion that the survey will be informed. Sometimes it can be a report that you send to your boss or your work team. Other times it might be a blog post. Often this can be a presentation in front of a group of colleagues. However, data science jobs always involve some form of productive communication. That is why it is important to have good communication skills to become a data scientist.

Conclusion:

Data science education is well in the early stages of its development; it develops in an independent discipline and produces professionals with different skills and support compared to professionals in the computer, information, and mathematical sciences. To better connect with the technology, have the Salesforce Training in Chennai.