Career In Data Science: A Detailed Overview For 2021

Introduction

Data Science is the buzzword of the 21st century. Data Science careers show immense potential to change the way we work with data and computers. In this field, there are always requirements for experts as the days progress. Data Science jobs feature in the top 10 careers list in the world of technology because of the following reasons:

  • It is very rewarding to provide artificial intelligence-based solutions.
  • Data Science is adopted by thousands of companies across the globe. 
  • Careers in data science is one of the highest paying jobs in this decade with a constant requirement.
  • We generate worth megabytes of data every hour, and it’s vital to use this data and drive analytics out of it. Hence data science is the trending career path for the decades ahead.

Data Science roadmap for beginners:

The career outlook for Data Scientist looks extremely good due to the increased use of data in every sector. With the increased demand, students are considering choosing a Data Scientist career path.

If you are still wondering how to start with Data Science, here we have the Data Science roadmap to learn Data Science.

You have three options to start your learning:

  • You can enroll in the university Data Science degree course and follow through with the program.
  • Bootcamp is similar to a degree program; you can choose a program with a duration of between 8 to 12 weeks.
  • Take an in-person Postgraduate Certificate Diploma in Data Science and Machine Learning, get peer mentoring and work on personal projects to build your portfolio.

The Data Science roadmap:

1) Building Block of Data Science

  • Start with learning Statistics. It’s the essential building block of all the data fields.
  • The next step is learning to code. You can choose Python or R.
  • Python is favorably easy, flexible, and more general. R is convenient for more statistical chores. 
  • Start studying SQL to learn database handling.
  • Take a course that introduces these tools in the context of Data Science. This way, you will learn Machine Learning at the same time.

2) Practice and showcase your skills

  • Once you have a command of basic concepts, practice your coding skills.
  • You should solve problems using syntax. Solve SQL and Python/R questions. And start building your portfolio of the projects on GitHub, LinkedIn, etc.

These steps can help you build a career in Data Science. Of course, the voyage doesn’t conclude here, as Data Science needs endless learning. 

In this article, let us look at:

  1. Different Job Roles for Data Science Experts
  2. Skills required to build a Career in Data Science
  3. How do I become a Data Scientist?
  4. What Kind of Salary can you Expect in a Data Science Career?

1. Different Job Roles for Data Science Experts

Different career options in data science are 

  • Data Analyst: It is an entry-level position in this field. The job entails analyzing the data to figure out the market and business trends. 

  • Data Scientist: They build ML models for statistical analysis and predictions. They study the data patterns and present them in a simple way that everyone can understand.
  • Data Architect: They work with senior management to assess a company’s data needs and help design the enterprise data management framework. 
  • Data Engineer: This job has more to do with data collection and building data pipelines for data scientists. They are responsible for building and maintaining the data infrastructure.

2. Skills required to build a Career in Data Science

Here’s a list of skills required to have a successful and rewarding career in data science: 

  • A strong foundation of mathematics should be the start of your career in data science. A sound understanding of concepts like matrix operations, linear algebra, permutations combinations, and differential calculus. 
  • Statistics is used to simplify the mathematical operations on real-time data. This includes concepts like measures of spread, shaping operations, statistical applications, central tendency, etc.
  • Fundamentals in languages like Python and R will go a long way. Understanding control structures and object-oriented programming is a must. Working with various types of data seamlessly is an important skill. To break it down further, knowledge of data structures, exception handling, matrix handling, language syntax, etc.
  • Data Handling skills are a must if you want to make a career in data science. This includes a collection of data, converting data into useful information, data ingestion, and manipulation of data as and when required.
                
  • Machine learning is the next milestone in the learning curve. Learning should include things like recommendation systems, semi-supervised learning, and simplified ML algorithms.
  • Deep Learning is the key part of the data science journey. The fascinating concepts of neural networks, NLP (Natural Language Processing), image recognition, and knowledge of Python libraries such as Tensorflow, Keras, Theano, etc., helps. It is vital to understand the latest trends and requirements.
  • The most sought-after data scientist skill is data visualization to make the end result understandable to everyone. This includes Business Intelligence tools like Tableau, types of charts and plots, data streaming, dashboarding tools, etc.
  • One of the data scientist jobs is thinking about solving Big Data issues. Begin by learning about the Apache ecosystem, followed by concepts of data engineering, big data tools such as SparkR, PySpark, Scala, and more.
  • As a budding data scientist, it is imperative to learn about version control. Knowledge of working with repositories, Git and Bitbucket, basics of GitHub and VC tools, branching, and production can add sparkle to your resume.

3. How do I become a Data Scientist?

Technical skills required to be a data scientist are discussed in detail in an earlier section. Here is the roadmap of the learning journey. 

  • A basic understanding of math and statistics can start your data science career. However, mathematical fluency is required to become an expert in data science.
  • It does, however, necessitate knowledge of programming language and the ability to work with data in that language. Python and R and excellent data science programming languages. To begin with, you should concentrate on learning a single language and its ecosystem of data science packages.
  • Basic SQL and database knowledge
  • A degree or a certified course in data analysis, manipulation, and visualization.
  • A course on machine learning.
  • Understanding machine learning in more depth requires both experience and more study.
  • Last and most important is the practical application of the things you have learned.

4. What Kind of Salary can you Expect in a Data Science Career?

Data Science is one of the most highly paid jobs and is a lucrative career option. According to Glassdoor, a Data Scientist earns $108,224 on average per year. The salaries range from $79,000 to $145,000, depending upon the skills and experience.

India is now the second-biggest hub for data science. One can have a bright career in data science in India also. On average, a data scientist earns approx. 7 lakhs per year which can go up depending on experience, location, job role, and skillset. 

Conclusion

Data Science professionals continue to be in demand and are well compensated. There are data science job opportunities with reputed companies that offer provide job stability, growth, and security. Apart from the IT domains, a data scientist can work in other domains like healthcare, financial, pharma, consulting, and other industries. Data Science is a perfect way to overcome downturns of career stagnation. 

If you are interested in making a career in the Data Science domain, our 11-month in-person PG Certificate Program in Data Science and Machine Learning course can help you immensely in becoming a successful Data Science professional. 

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