Contact for Admission

Arun Arya

Prof. (Dr.) Arun Arya

Contact for Admission

Arun Arya

Prof. (Dr.) Arun Arya


Essential skills required for a data scientist career – Top Engineering Colleges

What are the skills required for a data scientist career

As the demand for data scientists increases, the discipline presents an enticing career path for students and existing professionals. This includes those who are not data scientists but are obsessed with data and data science. Leveraging the use of Big Data as an insight-generating engine has driven the demand for data scientists of best engineering colleges in Jaipur at the enterprise-level across all industry verticals. Whether it is to refine the process of product development, improve customer retention, or mine through data to find new business opportunities, organizations are increasingly relying on data scientist skills to sustain, grow, and stay one step ahead of the competition. Some of the important skills required to become an expert data scientist are as follows:

1. Education

Data scientists are highly educated, as a very strong educational background is usually required to develop the depth of knowledge necessary to be a data scientist. To become a data scientist, you could earn a Bachelor’s degree in Computer science from Top Computer Science Engineering Colleges, social sciences, Physical sciences, and Statistics. A degree in any of these courses will give you the skills you need to process and analyze big data.

After completing your degree program, you can pursue Master's degree or Ph.D. or undertake online training to learn a special skill like how to use Hadoop or Big Data querying. Therefore, you can enroll for a master's degree program in the field of Data science, Mathematics, Astrophysics or any other related field. The skills you have learned during your degree program will enable you to easily transition to data science.

2. R Programming

In-depth knowledge of at least one of these analytical tools, for data science R is generally preferred. R is specifically designed for data science needs. Students of top engineering colleges in Jaipur can use R to solve any problem they encounter in data science. However, R has a steep learning curve. It is difficult to learn especially if you already mastered a programming language.

3. Python Coding

Python is the most common coding language, typically required in data science roles, along with Java, Perl, or C/C++. Python is a great programming language for data scientists. Because of its versatility, students of engineering colleges can use Python for almost all the steps involved in data science processes. It can take various formats of data and you can easily import SQL tables into your code. It allows you to create datasets and you can literally find any type of dataset you need on Google.

4. SQL Database/Coding

Even though NoSQL and Hadoop have become a large component of data science, it is still expected that a candidate of Top BTech Colleges will be able to write and execute complex queries in SQL. SQL (structured query language) is a programming language that can help you to carry out operations like add, delete and extract data from a database. It can also help you to carry out analytical functions and transform database structures.

You need to be proficient in SQL as a data scientist. This is because SQL is specifically designed to help you access, communicate and work on data. It gives you insights when you use it to query a database. It has concise commands that can help you to save time and lessen the amount of programming you need to perform difficult queries. Learning SQL will help you to better understand relational databases and boost your profile as a data scientist.

5. Apache Spark

Apache Spark is becoming the most popular big data technology worldwide. Apache Spark is specifically designed for data science to help run its complicated algorithm faster. It helps in disseminating data processing when you are dealing with a big sea of data thereby, saving time. It also helps data scientist to handle complex unstructured data sets. You can use it on one machine or cluster of machines.

Apache spark makes it possible for data scientists of Top Private Engineering Colleges to prevent loss of data in data science. The strength of Apache Spark lies in its speed and platform which makes it easy to carry out data science projects. With Apache spark, you can carry out analytics from data intake to distributing computing.

6. Machine Learning and AI

A large number of data scientists are not proficient in machine learning areas and techniques. This includes neural networks, reinforcement learning, adversarial learning, etc. If you want to stand out from other data scientists, you need to know Machine learning techniques such as supervised machine learning, decision trees, logistic regression etc. These skills will help you to solve different data science problems that are based on predictions of major organizational outcomes. Data science needs the application of skills in different areas of machine learning.

7. Data Visualization

The business world produces a vast amount of data frequently. This data needs to be translated into a format that will be easy to comprehend. People naturally understand pictures in forms of charts and graphs more than raw data. As a data scientist of top BTech colleges in India, you must be able to visualize data with the aid of data visualization tools like ggplot, d3.js and Matplottlib, and Tableau. These tools will help you to convert complex results from your projects to a format that will be easy to comprehend.

8. Unstructured data

It is critical that a data scientist be able to work with unstructured data. Unstructured data are undefined content that does not fit into database tables. For instance, videos, blog posts, customer reviews, social media posts, video feeds, audio etc. They are heavy texts lumped together. Sorting these types of data is difficult because they are not streamlined.

Most people referred to unstructured data as 'dark analytics" because of its complexity. Working with unstructured data helps you to unravel insights that can be useful for decision making. As a data scientist, you must have the ability to understand and manipulate unstructured data from different platforms.

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