The term ‘data’ is used to represent raw facts, concepts, or instructions. Data may be stored on a computer’s hard disk, pen drive, CD drive, or any other storage device in binary form. As you know, in binary number system, all the information is presented only in two states that may be termed as ‘0’ and ‘1’.
All the data gets converted into binary form to make it understandable to the computer and again in its real form to make it understandable for its user.Different operations and analysis are performed on this data to get useful information.
Real data may be in any form, like text, image, audio, or video. The different forms of data are shown below .Data can be categorised into two types: quantitative and qualitative.
Quantitative Data
The term ‘quantitative’ originates from the term ‘quantity’, which means a ‘definite num So, the data that can be represented by finite numbers is considered as ‘quantitative data’ example, 25 m, 100 kg, 1.5 cm, etc.
Qualitative Data
The term ‘qualitative’ originates from the term ‘quality’, which is the measure of excellence the data that represents the qualities or attributes of an entity is called ‘qualitative data’. example, nice weather, honest person, lengthy novel, and so on. .
Types of Quantitative Data
Quantitative data is further categorised into two types: continuous data and discrete data. Continuous data refers to indefinite numerical values and discrete data refers to definite numerical values. Moreover, the continuous data varies over a period of time and can take any value between two numbers.
On the other hand, the discrete data cannot be divided into parts and is generally measured using integers. Let us understand these two terms with the help of an example. All kinds of data that you have learnt in the previous section have many real-life applications and play a very important role in many domains
When you watch a video related to a particular subject, let us say data science, the video platform starts recommending you more videos related to data science. So, how does the platform get to know that you want to watch a video related to data science?
It is done by analysing your current search, search history, and other people’s choices who have watched that video. It means your search data and choice of videos are being analysed to give you better suggestions and recommendations.
- Friend suggestions on social media platforms
- Flight suggestions through the ticket booking platform
- Finding product ratings and analysing them for pricing decisions
- Analysing customer demand
What is Data Science?
Data science is a field of technology that deals with data and processes. Its purpose is to maintain data collections and extract meaningful information from them. A data scientist is a person who makes use of various tools, devices, algorithms, and Al models to analyse the data.
Data science is a way to extract insights from data. It involves preparing data for analysis and processing, performing advanced data analysis, and presenting the results to find patterns and enable stakeholders to make informed decisions.
It includes statistics, calculus, business intelligence, machine learning, deep learning, and analytics. In today’s world, a lot of data is generated and collected from various sources in all organisations.
Life Cycle Of Data Science
The data science life cycle focuses on data analysis and getting insights, which involves steps to perform statistics, visualisation, machine learning, and deep learning on it, to find the best possible solution through different phases.
Capture
This phase involves the collection of raw data from reliable sources in the form of text, numbers, images, and video from the computer and other devices in real time.
- Data Acquisition
- Data Entry
- Signal Reception
- Data Extraction
Maintain
The data is collected in both structured and unstructured formats, but it must be in a consistent format in order to be used in subsequent process models. Cleaning, duplication, reformatting, or other data integration technologies are used to segregate data into a data warehouse for analysis.
- Data Mining
- Clustering/Classification
- Data Modelling
- Data
- Summarisation
Process
During this phase, the data scientist looks for patterns, ranges, and distribution of values in the data to give it the shape and context that computers need to analyse it.
- Data Reporting
- Data Visualisation
- Business Intelligence
- Decision Making
Analyse
During this phase, a variety of scientific methodologies are used to analyse the data and generate insights. To extract insights from the prepared data, it uses statistical analysis, predictive analytics, regression, machine learning, deep learning algorithms, and more.
- Data Warehousing
- Data Cleansing
- Data Staging
- Data Processing
Communicate
Finally, using programming languages like R or Python, the insights are displayed as graphs, reports, and other data visualisations that make the insights and their influence on the business easier to understand for decision-makers.
- Exploratory/ Confirmatory
- Regression
- Text Mining
- Qualitative Analysis
Applications Of Data Science
Data science is a powerful field that has applications in various fields and industries, such as healthcare, transport, e-commerce, banking, and many more.
Banking
By incorporating data science into its services and policies, banking has become more automated and independent. Data science allows banks to keep track of their data and provide the best solutions to their consumers. It has capabilities for detecting fraud, making loan recommendations, and responding to consumer queries
Healthcare
In healthcare industry, data science methods are being used to examine patients, helping detect the symptoms of a disease at a very early stage. Physicians can use classification algorithms to detect cancer. Hospitals can further install various equipment and devices for the diagnosis of patients.
These devices, built with the help of data science, are capable of collecting data from the patients, which helps the doctors to monitor patients’ conditions from remote locations too.
E-Commerce
Because of data science models, the e-commerce industry has grown significantly. Customers have a more pleasant lifestyle due to the e-commerce facilities that supply the best products based on their needs. Data science provides a lot of data for the recommendation system to make product recommendations based on a user’s browsing and purchasing history.
Transport
Self-driving cars are a prime example of data science in the transportation business. Data science algorithms are used by AI self-driving cars to follow traffic rules and make fast decisions based on the situation.
Your phone’s GPS system employs data science algorithms to offer the optimal route to your destination, which is entirely dependent on current traffic conditions as well as the experience and feedback of users.
Fraud Detection
You have probably heard about the different types of frauds at the workplace. By continuously monitoring and auditing, data science algorithms automate the process of fraud detection and help find the best solution to defeat frauds.