What is a Data Scientist?

Introduction

The world today is being powered by data. Based on a report by International Data Corporation (IDC), due to the advent of the internet and digitalization, the amount of data we are producing is growing, and it is likely to reach 175 zettabytes by 2025, compared to just the two zettabytes generated in 2010. Industries across the board have come to rely on data-driven insights to drive their strategies and decision-making process. Whether it is a small or big organization, it has become essential for them to process large amounts of data and generate insights from it for their success and growth.

So, What is a Data Scientist? Data Scientists are the practitioners of the Data Science discipline who are responsible for processing large amounts of data residing in the organization’s repositories by applying various scientific methods. In fact, Data Scientist has been regarded as the sexiest job of the 21st century by Harvard Business Review.

If you want to become a Data Scientist, you have come to the right place. In this post, we will talk about What is a Data Scientist, what the role is, the salary of a Data Scientist, what is a data science course, and what is required to make a career out of it.

What is a Data Scientist?

Data Scientists are professionals who are responsible for solving various business problems by implementing data science techniques for an organization. They collect, process, and analyze large amounts of structured and unstructured data from a business point of view and apply various methods such as statistics, machine learning, etc. to create insights for organizations. They can be considered as a combination of computer scientists and statisticians.

Data Scientist was a completely unknown term just a decade ago, but as businesses have understood the importance of big data, they have become more and more common, and their demand is growing continuously. Big Data can be defined as a collection of data that is huge in volume and increases exponentially in size with time. It is so voluminous that none of the traditional data management tools can store and process it efficiently, and it requires advanced technologies for storing and processing.

Steps to Become a Data Scientist

The Data Scientist field offers plenty of job opportunities as the demand for Data Scientists has surged across the industries in the past few years and is expected to grow for the next decade. So whether you are a student or an experienced professional, building a career in Data Science could be a smart move as this job offers a lucrative and promising career path.

Below are a few steps to consider to position yourself for a career in Data Science :

• Pursue a degree (undergraduate/master’s) or course in data science or a closely related field
• Learn and master various skills required to become a Data Scientist
• Get an entry-level Data Scientist job
• Review various data science certifications and post-graduate learning

If you are interested in becoming a Data Scientist, you can check out the Scaler Data Science course.

Typical Job Responsibilities for Data Scientists

Working as a Data Scientist can be intellectually challenging, analytically satisfying, and put you at the forefront of new technological advances.

Below are typical responsibilities involved in a Data Scientist’s day-to-day life :

• Understanding Business Problems - Data Scientists must acquire business acumen/domain expertise to understand business problems by having open-ended discussions with multiple stakeholders or performing an extensive literature survey.
• Data Collection - Once the business problem is understood and hypotheses are formed, data scientists identify relevant data sources and collect large amounts of structured or unstructured data stored in databases using programming languages such as SQL, web scraping, APIs, etc.
• Data Preparation - Clean and prepare data by discarding irrelevant information and employing sophisticated statistical or analytical methods.
• Data Exploration - Explore prepared data by using various statistical (correlation, mean, mode, etc.) or visualization methods (scatter plots, histograms, bar charts, etc.) to identify underlying patterns in the data.
• Data Modeling - Apply various statistical or machine learning techniques to build predictive or prescriptive models by using programming languages such as Python, R, Scala, etc.
• Communication - Communicate findings from developed solutions or models to various stakeholders and recommend changes to existing procedures or strategies to solve given business problems.
• Stay Up to Date - Keep track of ongoing research in related fields such as Machine Learning, Deep Learning, Natural Language Processing, other analytical techniques, etc.

Required Skills for a Data Scientist

If you are looking to become a Data Scientist, below are the skills you will need to be proficient in :

• Programming Languages - Data Scientists spend a lot of time using programming languages to collect or prepare data, build and develop machine learning models, explore the data, etc. Popular programming languages data scientists use to perform various methods are -

  • Python
  • R
  • SQL
  • Scala
  • SAS

• Data Visualization - Visualization is a key part of a Data Scientist’s job to identify underlying patterns in the data by creating various charts and graphs. Data Scientists should be familiar with below visualization tools :

  • Tableau
  • PowerBI
  • Excel
  • Python or R visualization libraries

• Machine Learning - Building and developing machine learning-based predictive or prescriptive models are the most important part of the job of a Data Scientist. Data scientists must have a sound understanding of various Machine Learning algorithms spanning classification, regression, deep learning, etc.

• Big Data - Data Scientists should have familiarity with various Big Data processing frameworks such as Apache Spark, Hadoop, etc. This will enable them to deal with large amounts of data efficiently.

• Communication - As a data scientist, you must communicate your findings and recommendations to non-technical colleagues or stakeholders. This may include senior management, other departments within your company, or even customers. It is therefore essential to develop strong communication skills to become a Data Scientist.

• Business Acumen/Domain Expertise - Business acumen is the ability to translate business problems into data solutions and connect to business impact. Many data scientists focus on learning technical skills but spend little time developing the soft skills needed to become successful. Having business acumen is the kind of skill that can help you stand out from the crowd.

Terms and Technologies Commonly Used by Data Scientists

Below are terms and technologies commonly used by Data Scientists -

• Data Cleaning - It is about discarding irrelevant information, imputing missing values, or handling outliers values from the data so that it can be used for further exploration.
• Exploratory Data Analysis (EDA) - This is regarding exploring the cleaned data using various statistical (correlation, mean, mode, etc.) or visualization methods (scatter plots, histograms, bar charts, etc.).
• Feature Engineering - It is also called data preparation which is the process of identifying the most impactful and relevant features from the raw data by applying domain knowledge. These engineered features can help in boosting the accuracy of the ML model.
• Machine Learning - It is a field in Computer Science that can enable computers to learn the patterns in the data without explicitly being programmed.
• Deep Learning - It is a subfield within Machine Learning research that models data using Neural Networks. Neural Networks are nothing but mathematical models mimicking the human brain.
• Pattern Recognition - It is used to recognize various patterns in the data. This term is often used as a synonym for Machine Learning.
• Text Analytics - Is applying various techniques to text data (unstructured data, chats, reviews, pdf, etc.) to generate business insights.

Data Analyst vs. Data Scientist

It is very common to confuse the role of a Data Scientist with that of a Data Analyst. While there could be an overlap in many skills, there are also some significant differences between these two roles.

Data Analysts are responsible for applying statistical and visualization methods to prepare dashboards, charts, reports, etc. by collecting and processing the data using basic programming languages. They mostly deal with structured data.

For example, a Data Analyst can analyze a marketing campaign’s effectiveness by keeping track of multiple KPIs and metrics. This analysis can help businesses understand the success of their products with respect to various age groups and demographics or otherwise find consumer patterns.

While Data Scientists are responsible to collect and process structured and unstructured data, cleaning and preparing them in a format that is usable and understandable. They apply advanced programming languages and tools to build and develop predictive or prescriptive models. They look for patterns and create algorithms and models so businesses can use the data collected and interpret it for different scenarios.

For example, a Data Scientist can come up with a consumer segmentation approach to understand consumers' purchase behavior with respect to demographics or age which can help businesses to come up with improved marketing strategies.

You can check out our blog Data Analyst vs. Data Scientist if you want to understand the differences between these two roles in detail.

What is a Data Scientist's Salary?

The salary of a Data Scientist depends on multiple factors such as years of experience, education, skillset, company, and location. Some companies pay higher to Data Scientists having specialized skills such as Computer Vision, Natural Language Processing, etc.

The salary for a Data Scientist in India ranges from ₹ 4.5 Lakhs to ₹ 25.0 Lakhs, with an average annual salary of ₹ 10.5 Lakhs.

In India, the average salary of a Data Scientist having 1-4 years of experience is ₹ 4.8 LPA, while Senior Data Scientists take home a salary of ₹ 20 LPA on average.