Career guidance
  •  Introduction
  • Why Industry Need Data Scientist
  • Average Salary- Data Scientist
  • Data Science Overview
  • All you need to know about Data Science
  • Data Scientist- Role
  • Skillset required
  • Takeaway

 

Introduction

If you are interested in becoming a data scientist, this blog is for you. This blog includes the skills and tools that are needed to become an experienced data scientist.

Why do Industries need Data Scientists?

With regards to accepting present-day and developing innovation, the world is rising and radiating brilliantly. To assist them with accumulating substantial knowledge from organizations, recruit Data science professionals. A rapid rise in demand for profoundly qualified specialists who comprehend both the business world and the tech world is evident in businesses. Today, organisations are effectively looking for experts who can fill this ever-growing gap between the demand and supply of Data Science developers. 

 

Average Salary: Data Scientist

The average salary of a Data Scientist is Rs 698,412 per annum. An entry-level data scientist can earn around Rs 500,000 per annum with less than one year of experience. Early level data scientists with 1 to 4 years of experience get around Rs 610,811 per annum.

There is no shortage of good pay in this domain. The only thing missing is the supply of proficient Data Scientists.

 

Data Science Overview

Data science isn’t just the future; it is also the present.

Data science has been here since the 1990s. However, its worth was recognized when organizations got unfit to utilize enormous information for making decisions. Data science has been assisting organizations with upgrading the traditional methods of data consolidation. It empowers the associations to approach more data and permits seeing new things better from a different perspective. Associations are getting the insight and are unequivocally utilizing information science to change over data and information right into it, prompting increasingly more information researcher occupations.

 

All that you need to know about Data Science

Data science mixes different tools, calculations, and machine learning principles to find concealed patterns from the raw information. In short, Data science is an interdisciplinary field that utilizes algorithms and information to extract knowledge from organised and unstructured data, and then apply information and knowledge from data across a wide range of application domains. It is related to data mining, big data, and machine learning. If you’d like to learn more about data science and AIML, check our blog on data science versus machine learning and AI.

Many statisticians have argued that data science is not a new domain but rather a synonym for statistics. Others claim that data science is entirely different from statistics because it focuses on issues and techniques unique to digital data. A few statisticians believe that statistics focuses more on quantitative data and description. In contrast, data science deals with quantitative and qualitative data and emphasizes prediction and action.

 

Data Scientist- Role 

Data Scientists is a widely used term these days. Everyone is labelled as Data Scientists, from analysts to data visualizers and business domain specialists. Albeit, this net of an idea is right, a Data Scientist can be characterised as a person who is a segment mathematician, part computer scientist, and part watcher of business drifts and can ride both the universes of IT and business.

Data Scientists are not just expected to have a more extensive assortment of abilities, yet businesses additionally request considerably more sound specialization and cooperation.

 

Required skillset

One doesn’t turn into a Data Scientist in an instant. It takes a great deal of learning, experience, and comprehension of the ideas. Before you jump into a decision to turn into a Data Scientist, there are a few inquiries that you need to pose to yourself: Do you cherish numbers, figures, and diagrams? If you don’t care for hanging out with numbers, it might bother you later in your profession. Would you be able to program without trouble? Data Scientists ought to be well-versed with different programming languages like R, Python, and so on.

Are you excited to learn from the basics to reach the level of a high-paying Data Science job? 

If you have had no involvement with data analysis, you may need to start at entry-level. Only when you have had ample experience, then you can proceed further with your decision.

Data Science is all about skills, tools, and programming languages. You must have a command over; SQL and NoSQL databases, Relational algebra, Parallel databases, Scala, Java, SQL, Data Visualization, Machine learning, Statistical modelling, Sentiment Analysis, Data optimization, Storytelling, and Graphical display tools.

 

  • Math & Statistics

It is the place where you investigate the establishment of Data Science. The critical ideas covered under this segment include probability, basics of linear algebra, and basics of statistics. You will likewise figure out how to perform EDA or exploratory data analysis.

  • Fundamentals of Machine Learning

Apart from Data Science basics, you will have to learn the basics of Machine Learning. You should know basic algorithms and techniques, including logistic and linear regression, SVM, or support vector machines.

  • Projects & Portfolio

You won’t get paid until and unless you haven’t worked on any practical or real-life project. If you are currently working, try to implement everything you are learning in your work. On the other hand, if you are not working, start building your projects that include all the latest data science tools you know. You should have a few tasks, such as data cleaning projects that include data preparation, data, munging, and data cleaning, data storytelling, and visualization project, group projects, etc., to give you in-depth knowledge to step into this domain.

  • Flaunt Your Projects and Their Outcomes-

Start showcasing the worth of your projects related to your workplace. If you are not working anywhere, display the outcomes of your personal projects and their impact on your blog, LinkedIn, GitHub, YouTube, Twitter, or any other digital medium that helps in making your achievements and learnings go viral. Your employers and acquaintances must know what you have learned and how you communicate your results through graphs and data visualizations.

 

Takeaway

The e-learning industry offers many training opportunities related to data science. You need the right approach and right domain to make a mark in this niche. Data Science is not just limited to one specific job role. There are many opportunities and profiles that you can opt for.

 

References: