What is Data Science – Meaning and Career Opportunities

What is Data Science

What is Data Science and How to Make a Career in it?

We live in a technology-driven world where massive volumes of data are generated daily. To ensure that these large data sets are analysed and sorted correctly, there are many scientific tools and techniques.
The process of using scientific and mathematical tools and techniques to draw meaningful insights from unstructured data is called data science. It (data science) is adopted by various industries to enhance performance, incite business growth, and improve customer experience or CX.

Key takeaways from this article:



  • What is data science?
  • What is the significance of data science in the current era?
  • What is a data science lifecycle?
  • How to build a career in data science?
  • What is a data scientist, and how to become one?

What is Data Science?

Data science is the process of analysing massive data volumes using cutting-edge technology and modern techniques to discover patterns, important information, and insights.
The data used for analysis comes from websites, apps, social media, third-party websites, marketing campaigns, as well as customer support platforms, which can be converted into deep insights with the help of predictive models, AI and machine learning algorithms.

Significance of Data Science

Here are three points that highlight the growing importance of data science in the current milieu.

  1. It helps leverage data to make business decisions that drive growth.
  2. It helps build infrastructure to store important customer data like birthdays, age, and other demographics, also confidential information related to the company’s network and process.
  3. Using scientific and analytical tools help in attaining greater efficiency and introducing agile business practices to your organisation.

Data Science Lifecycle

There are five different stages in a data science lifecycle, with a peculiar set of tasks performed at each stage:

Data Capture

– The first stage of the data science lifecycle involves compiling raw, structured, and unstructured data from different sources. This stage requires data scientists to perform data acquisition, entry, signal reception, and extraction.

Data Maintenance

– In this stage, raw data is converted into a format suitable for business. This stage involves processes like data warehousing, data cleansing, data staging, data processing, and data architecture.

Data Processing

– Data scientists analyse refined data and determine patterns, ranges, and biases in this stage. Data mining, clustering/classification, data modelling, and data summarisation are some of the standard processes of this stage.

Data Analytics

– This is the most crucial step of the data science lifecycle. This process uses different analytical tools to convert data into a digestible format. This stage involves exploratory/confirmatory and predictive analysis, regression, text mining, and qualitative analysis.

Communication

– In the final stage of the data science lifecycle, tasks such as data reporting, data visualisation, business intelligence, and decision-making are undertaken to communicate the insights to business leaders, stakeholders, peers, and decision-makers.

Prerequisites for a Career in Data Science

To make a successful career in the field, data science aspirants must be well-versed in different tools, techniques, and processes. Below are the technologies and concepts every data scientist must know about.

Machine Learning

Machine learning is a branch of artificial intelligence that uses data and algorithms to imitate how humans learn. It is the backbone of data science. Therefore, a data scientist must have a basic understanding of machine learning.

Data Modelling

Data modelling is an integral part of the data science process. It helps in speeding up the calculation and data-driven prediction. Additionally, it trains data models to identify the best algorithms to solve complex business problems.

Statistics

Statistics are the primary language for anyone using data to extract insights and achieve desired outcomes.

Programming

An aspiring data scientist must be proficient with programming languages. You must be well-versed in common programming languages like Python and R. Many include programming languages in their curriculum.

Database

A database is an organised collection of information or data stored electronically in a computer system. Data scientists must know the working of databases, how to create, manage, and extract meaningful data from them, etc.

How to learn the fundamentals of data science?

Aspiring professionals can acquire the essential skills required to build a successful career in data science by pursuing an advanced training programme. They can take courses that teach them how to analyse large data sets and perform activities like data mining, visualisation, cleaning, and others.

Emeritus India has partnered with prestigious institutes like IIM, IIT, and ISB to offer advanced certificate courses in data science for fresh graduates and experienced professionals. These courses will skyrocket your career.

Roles and Responsibilities of a Data Scientist

The role of a data scientist is to analyse business data and generate actionable insights that can be used to make data-driven business decisions.
Here are the duties of a data scientist:

  • Understanding what exactly needs to be done before starting the process.
  • Identifying the data that is needed for analysis.
  • Gathering data from diverse sources for analysis.
  • Converting unstructured data into a uniform, relevant, and accurate data set.
  • Analyse data using analytics, Machine Learning algorithm or a statistical model.
  • Turn insights (collected by analysing data) into reports and sharing them with the stakeholders for further communication and implementation.

Why Become a Data Scientist?

The data science field is rapidly growing. It is estimated that the market will grow by nearly 28% by 2026. Hence, there are ample high-paying opportunities for qualified data science professionals, and the demand will only rise with time. Therefore, if you want to make a career in data science, now is the right time to start your journey.

Where Would You Fit in the Data Science Industry?

There are different job profiles available for data science aspirants; let’s take a look at some of them:

Data Scientist

A data scientist’s job profile includes the following:

  • Identifying the problem.
  • Probing about it.
  • Finding the correct data.
  • Analysing it.
  • Presenting data-driven insights to peers.

Data performs data mining, cleaning, visualisation, and interpretation, among others.

Here are some skills required to become a successful data scientist:

  • Well-versed in programming languages
  • Excellent storytelling and visualisation skills
  • Good with statistics and mathematics
  • Knowledge of software like Hadoop, SQL, and Machine Learning

Data Analyst

A data analyst is a connecting link between data scientists and business analysts. Their job is to organise and analyse data to find answers to the organisation’s challenges.

A data analyst must have statistical and mathematical skills, programming skills (SAS, R, Python), and well-versed with different analytical and visualisation tools.

Data Engineer

A data engineer focuses on preparing data for analytics. They are trained professionals who make the data useful for analysis. Therefore, they play a crucial role in the data science process.
A data engineer must know about databases (e.g., MongoDB, Cassandra DB), programming languages including Java and Scala, and frameworks (Apache Hadoop).

Best Data Science Courses for Data science Aspirants

With the surge in demand for data science professionals, some high-quality courses offered by Emeritus India will help you excel in a data-driven world. Joining our programmes will provide insight into modern data science practices, and gain a competitive advantage, among other things.

Our courses are offered in partnership with institutes like IIM, ISB, IIT, and XLRI. These programmes are designed for fresh graduates and experienced professionals transitioning into managerial positions.

FAQ

What is data science in simple words?
It is the process of deriving meaningful insights from large data sets.

What is a data scientist?
Data scientists are trained professionals who work closely with business stakeholders to understand how data can be used to achieve business goals. They overlook the data modelling process, create algorithms and predictive models to extract data, help analyse it, and share insights with their peers.

What is the eligibility of a data science course?
Graduates or diploma holders from a recognised university in any discipline are eligible for data science courses. You can check out our data science certificate programmes for all the necessary details.

What is the difference between data science and analytics?

Data Science Data Analytics
Design and construct new processes for extracting and making sense of data Examining large data sets to identify trends, develop patterns to make complex business decisions
It mainly deals with unstructured data It mainly deals with structured data
It uses Python mainly to perform its activities. Sometimes also uses C++, Java, etc. It uses Python and R only for performing its activities

What are the applications of data science?
Data science is used for image recognition, recommendation system, logistics, storing and accessing patient details and health reports, fraud detection, internet search, speech recognition, targeted advertising, airline route planning, and augmented reality.

What kind of problems do data scientists solve?

Here are the following issues that data scientists solve:

  • Loan risk mitigation
  • Issues related to the effectiveness of various types of online advertisement
  • Resource allocation

What are the skills required for a career in data science?
There are two essential skills that a data science professional must have. They are technical and non-technical skills.

Technical skills include:

  • Statistical analysis and computing
  • Machine learning
  • Deep learning
  • Data visualisation
  • Data wrangling
  • And mathematics

Non-technical skills include:

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