Data Science

What Is Data Science?

Data science is part of several industrial sectors, and the demand for data scientists has grown dramatically over the years. Most companies are attracted to data science techniques as this helps create a solid customer base and enables the business to grow. We will talk about data science, data scientists, and more in this article. 

What Is Data Science?

Data science is a topic of study that works with vast volumes of data. This data is thoroughly checked with the latest software and techniques to derive important business information and patterns. These help businesses monitor their statistics to develop a successful business plan based on intelligent decisions. 

The software used in data science comprises complex code algorithms used to create models. These models are predictive. The software can generate the data and analyze it through various sources and several formats.

What Is the Data Science Life Cycle?

A data science life cycle comprises the following five stages:

  • Data Capture

This step is the accumulation of data that needs to be analyzed. The data that is collected can be either structured or unstructured raw information. The tasks performed in this stage include:

  • Signal Reception
  • Data Extraction
  • Data Acquisition
  • Data Entry
  • Data Maintenance

The raw data is collected in a single place. It is also converted from a raw form to another more usable format. Tasks that are part of this stage are:

  • Data Staging
  • Data Processing
  • Data Architecture
  • Data Warehousing
  • Data Cleansing
  • Data Mining

Structured and processed data is lightly analyzed in this stage. Data scientists look for patterns or ranges and compare them to deduce whether the information is helpful. Tasks include:

  • Data Mining
  • Data Summarization
  • Clustering or Classification
  • Data Modeling
  • Data Analysis

This is a thorough and essential analysis phase. A series of techniques and methods are utilized to analyze and extract data. The process includes:

  • Exploratory or Confirmatory
  • Regression
  • Text Mining
  • Qualitative Analysis
  • Predictive Analysis
  • Data Communication

This is the last and final step of any data life cycle. The analyzed and extracted data is converted to readable information using graphs, charts, and reports. This is done through:

What Is the Job of a Data Scientist?

A data scientist analyzes various forms of data to extract meaningful and valuable information. A data scientist is supposed to provide a solution to business problems. They do this by following the steps below:

  • A data scientist looks for the problem by asking the correct questions to gain a better understanding 
  • A proper set of variables and data sets is determined
  • Raw structured or unstructured data is collected through business records, public data, and other sources
  • Raw data is converted into a more suitable, readable format. The data is filtered to ensure preciseness, consistency, and completeness
  • Structured, processed data is fed into an ML (Machine Learning) algorithm or a statistics model
  • The data is analyzed to derive patterns, trends, and other information
  • The data is interpreted further to look for solutions to the original problems
  • Results, solutions, and insights are categorized into charts, graphs, lists, reports
  • Final data is shared with the business personnel

What Are the Fields in Data Science?

Data Scientist

A data scientist looks for the problem and identifies the correct sources for data extraction. They help clean, decipher, mine, and present essential data.


  • Programming knowledge of SAS, R, and Python
  • Storytelling ability
  • Data visualization
  • Statistical or mathematical knowledge.
  • Knowledge of Hadoop
  • Knowledge of SQL
  • Machine learning

Data Analyst

A data analyst connects business analysts and data scientists. They help format and analyze various forms of data. This enables them to provide answers to the problem by creating action items.


  • Statistical and mathematical knowledge
  • Programming knowledge of SAS, R, and Python
  • Expertise in data wrangling
  • Knowledge of data visualization

Data Engineer

A data engineer creates, applies, manages, and improvises organization data. They help create a healthy data infrastructure. Data engineers are also responsible for backing up data scientists in converting data into queries.


  • NoSQL databases (MongoDB and Cassandra DB)
  • Knowledge of programming languages like Java, Scala, and frameworks


Glassdoor and Forbes have predicted that data scientists will be further in demand by 2026. The demand will increase by 28%. In another survey, the data scientist profession won second place in the Best Jobs in America for 2021. The average salary was recorded to be USD 127,500.

If you’re interested in becoming a data scientist, you will need to get a degree like the RMIT online masters of data science. RMIT Online allows you to get one right from the comfort of your home. Start working towards this exciting career opportunity today!