Career in Data Science

Data Science

With the advent of the internet, more than a billion messages, tweets and emails are sent every day on different digital platforms. In the present times, when more and more businesses are coming into existence, managing quintillion bytes of data is no less than the work of a hero. This is where the role of a data scientist comes in. Acquiring a powerful place in the contemporary times, pursuing a career in Data Science has created quite a buzz in the world and perhaps, will continue to stay in demand, thanks to the continuous technological advancements. If you possess the skills necessary for building a career in this field, such as problem-solving, persuasive and listening skills, critical thinking and analytical skills, then there are plenty of opportunities waiting for you to grab. Through this blog, let us take you on an educational tour on everything you want to know about a career in Data Science. 

Data Science: Overview

In simpler words, Data Science is a study of data, which involves a blend of algorithms, principles of machine learning and various other tools that are used to record, store and analyze data to obtain significant and useful information. Data scientists extract and interpret data from an extensive range of sources like log files, social media, sensors, customer transactions, to unlock useful information to influence the decisions of a business and stimulate competitive advantage over other counterparts. 

Educational Requirements for Data Science

To pursue a successful career in Data Science, the aspirants need to have graduated with certain specializations or subjects essential to the field. Here are the educational requirements that a student needs to fulfil.

  • Higher secondary education (12th grade) with Physics, Chemistry and Maths, i.e. MPC subjects as compulsory.
  • A B.Tech or B.Eng degree in Computer Science, Physical Science, Mathematics, Mathematics and Computing, Statistics or Engineering.
  • An, MS or M.Eng degree in Data Science, Mathematics or any other related fields.

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Technical Skills Required in Data Science

Being an interdisciplinary field, Data Science requires not just one or two, but a diverse set of technical skills and knowledge in the field of Computer Science. Here are the following skills that a Data Scientist must possess, irrespective of the experience gained.

  • Python Coding: Python is the most useful coding language in Data Science as it helps in the mining of data, developing models of machine learning or web scraping. Taking various formats of data, python can help you create and find datasets and import SQL tables into your code.
  • R Programming: R is a program, generally made for data analysis and provides the formulas and methods for information processing and statistical analysis.
  • Machine Learning and AI: Learning different techniques of Machine Learning such as logistic regression, decision tree, supervised machine learning, time series, computer vision, outlier detection, survival analysis, natural language processing, etc is inherently significant to tackle several challenges in the field of Data Science.
  • Hadoop Platform: When the volume of data is extremely massive, it can exceed the memory of the system and the Hadoop Platform helps Data Scientists to send or transfer the remaining data to different servers. This platform is also useful for data filtration, data sampling and summarization, exploration, etc.
  • SQL: Structured Query Language (SQL) is a programming language which helps you communicate, access and manage the database by adding, subtracting or extracting the data. The Data Scientist needs to be proficient in SQL, as it is specifically designed to save time and reduce the amount of programming for difficult queries, through its compact commands. 

Other skills that are essential to the field of Data Science include:

  • Data Visualization
  • Apache Spark
  • Unstructured Data
  • Business Acumen
  • Communication and Persuasive skills
  • Data Wrangling
  • Algebra and Calculus
  • Statistics
  • Java
  • Unix
  • PHP

Elements of Data Science

Being vast in nature, the field of Data Science can be divided into three main elements, i.e. Business Intelligence, Machine Learning and Big Data. Let’s have a detailed look on these three components.

Business Intelligence

As every business is using computing technologies to operate, each organisation produces a large amount of data every day. On the careful analysis of this generated data, the data scientists present it in different graphs and charts. Thereafter, it helps the management make the best business decisions based on what the statistics represent.

Machine Learning

Involving mathematical and statistical models and algorithms, Machine Learning is adopted by almost all the business organizations to prepare the machines into understanding and reacting to daily circumstances and progresses. Through the use of Machine Learning in the field of Data Science, a machine can predict various trends in the markets or financial systems, based on the historical data patterns.

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Big Data

With an exponential increase in internet users every day, the viewers or customers generate innumerable clicks in the form of videos, images, articles, comments, order, etc. Generally, all these activities result in the generation of unstructured data. Data Scientists are responsible for turning that unstructured data into a structured one.

Roles and Responsibilities of a Data Scientist

After completing the education and acquiring the technical skills required in Data Science, here are some of the most common roles and responsibilities that you will be carrying out:

  • Staying up-to-date with all the emerging tools and techniques in statistical modelling, machine learning, etc.
  • Mine the data and generate a hypothesis to help achieve the high-level business objectives.
  • Analyse and mine large data sets using Hadoop Platform.
  • Work in collaboration with IT managers, statisticians, programmers and other experts for carrying out business decisions or creating products and services.
  • To solve various analytical problems with incomplete datasets, data scientists develop specially tailored algorithms as well.

Career Opportunities

Studying Data Science brings along a plethora of career opportunities in varied sectors. Not just limited to being a Data Scientist, you can go opt for various other job profiles under this vast domain. 

Data Analyst

A Data Analyst is responsible for turning the datasets into a usable structure, such as presentations, reports, graphs, etc. They are the ones who gather, refine, perform and analyse the statistical data to support and influence the objectives of a business. Being an entry-level position in the organizational chart of the business, a data analyst should have deep knowledge in Python, R, C, C++, HTML, SQL, Machine Learning, Excel, Probability and Statistics. They work closely with different departments and experts in the business and identify and extract key business risks and performance, in compliance with the data and convert it into a simple and legible format.

Business Analyst

Though a Business Analyst is technically less skilled than its other counterparts in Data Science, yet they have a strong knowledge of all the commercial procedures and has a solid business intelligence. Serving as a nexus between the IT and Business Administration, a business analyst is responsible for processing basic data through different data visualization tools and data modelling. They mostly focus on producing the data in the form of graphs, charts, reports, etc., which can be easily read and eventually work towards the interest of the business. If you are planning to work as a business analyst, you will need a strong educational background in Computer Science, Statistics, Mathematics, Business Administration, Economics, Finance or other related fields.

Data Engineer

Skilled in coding languages such as Python, SQL, R, Java, Ruby, MATLAB, Hive, Pig, SAS, etc., Data Engineers design, construct and manage the information or bigger chunks of data. This is one of the most extraordinary careers in Data Science, as a Data Engineer focuses on the hardware systems that facilitate the data activities of a business. They are responsible for developing an architecture that further helps in processing and analysing the data in such a manner which is best suited for a business organization. After acquiring an advanced degree and significant years of experience in the field of Data Science, one can secure a senior position under this career profile as well.

Apart from these three main and most demanded career outlooks in Data Science, here are some other common job profiles you can consider:

  • Marketing Analyst
  • Data Architect
  • Data and Analytics Manager
  • Statistician
  • Machine Learning Engineer
  • Database Administrator
  • Data Mining Specialist

Top Global Recruiters in Data Science

With a huge amount of production of data on a daily basis, there is a special demand for Data Scientists in businesses and multinational companies. Here are the top international recruiters of Data Science professionals.

  • Amazon
  • Microsoft
  • Deloitte
  • KPMG
  • Facebook
  • Google
  • Verizon
  • Apple
  • Walmart
  • Accenture
  • JP Morgan Chase

When a candidate looks forward to a successful career in Data Science, acquiring the right and high-quality education from the best institute is of utmost importance. For this, you can reach out to us at Leverage Edu. Sign up for a 30-minute free career counselling session with us and our experienced mentors and counsellors will help you choose the best course and university that aligns with your skills, interests and preferences and can provide you with the ideal exposure and knowledge to build your career in this field.

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