What Is Data Science ?
Table Of Contents:
- What Is Data Science?
- Why We Need Data Science?
- Data Science Prerequisites.
- What Is Data Science Used For
- Applications Of Data Science.
- Algorithms In Data Science.
(1) What Is Data Science?
- Data Science is the study of data to extract meaningful insights for business.
- It is a multidisciplinary approach that combines principles and practices from the fields of mathematics, statistics, artificial intelligence, and computer engineering to analyze large amounts of data.
- This analysis helps Data Scientists to ask and answer questions like what happened, why it happened, what will happen, and what can be done with the results.
(2) Why We Need Data Science?
- Nowadays, organizations are overwhelmed with data.
- Data Science will help in extracting meaningful insights from that by combining various methods, technology, and tools.
- In the fields of e-commerce, finance, medicine, human resources, etc, businesses come across huge amounts of data.
- Data Science tools and technologies help them process all of them.
(3) Data Science Prerequisites.
Statistics:
- Data Science relies on Statistics to capture and transform data patterns into usable evidence through the use of complex machine-learning techniques.
- Strong Statistical knowledge is necessary to understand Data Science concepts.
Programming:
- Python, R, and SQL are the most common programming languages.
- To successfully execute a Data Science project, it is important to instill some level of programming knowledge.
Machine Learning:
- Making accurate forecasts and estimates is made possible by Machine Learning, which is a crucial component of Data Science.
- You must have a firm understanding of Machine Learning if you want to succeed in the field of Data Science.
DataBase:
- A clear understanding of the functioning of Databases, and skills to manage and extract data is a must in this domain.
- SQL Concepts you must know to handle databases.
Modeling:
- You may quickly calculate and predict using mathematical models based on the data you already know.
- Modeling helps in determining which algorithm is best suited to handle a certain issue and how to train these models.
(4) What Is Data Science Used For.
Descriptive Analysis:
- It helps in accurately displaying data points for patterns that may appear that satisfy all of the data’s requirements.
- In other words, it involves organizing, ordering, and manipulating data to produce information that is insightful about the supplied data.
- It also involves converting raw data into a form that will make it simple to grasp and interpret.
Predictive Analysis:
- It is the process of using historical data along with various techniques like data mining, statistical modeling, and machine learning to forecast future results.
- Utilizing trends in this data, businesses use predictive analytics to spot dangers and opportunities.
Diagnostic Analysis:
- It is an in-depth examination to understand why something happened.
- Techniques like drill-down, data discovery, data mining, and correlations are used to describe it.
- Multiple data operations and transformations may be performed on a given data set to discover unique patterns in each of these techniques.
Prescriptive Analysis:
- Prescriptive analysis advances the use of predictive data.
- It foresees what is most likely to occur and offers the best course of action for dealing with that result.
- It can assess the probable effects of various decisions and suggest the optimal course of action.
- It makes use of machine learning recommendation engines, complicated event processing, neural networks, simulation, graph analysis, and simulation.
(5)What Is the Data Science Process?
Obtaining The Data:
- The first step is to identify what type of data needs to be analyzed, and this data needs to be exported to an Excel or a CSV file.
Scrubbing The Data:
- It is essential because before you can read the data, you must ensure it is in a perfectly readable state, without any mistakes, with no missing or wrong values.
Exploratory Analysis:
- Analyzing the data is done by visualizing the data in various ways and identifying patterns to spot anything out of the ordinary.
- To analyze the data, you must have excellent attention to detail to identify if anything is out of place.
Modeling or Machine Learning:
- A data engineer or scientist writes down instructions for the Machine Learning algorithm to follow based on the Data that has to be analyzed.
- The algorithm iteratively uses these instructions to come up with the correct output.
Interpreting The Data:
- In this step, you uncover your findings and present them to the organization.
- The most critical skill in this would be your ability to explain your results.
(6)What Are Different Data Science Tools?
Here are a few examples of tools that will assist Data Scientists in making their job easier.
- Data Analysis – Informatica PowerCenter, Rapidminer, Excel, SAS
- Data Visualization – Tableau, Qlikview, RAW, Jupyter
- Data Warehousing – Apache Hadoop, Informatica/Talend, Microsoft HD insights
- Data Modelling – H2O.ai, Datarobot, Azure ML Studio, Mahout
(7) Benefits of Data Science in Business.
- Improves business predictions
- Interpretation of complex data
- Better decision making
- Product innovation
- Improves data security
- Development of user-centric products
(7) Applications Of Data Science.
Product Recommendation:
- The product recommendation technique can influence customers to buy similar products.
- For example, a salesperson at Big Bazaar is trying to increase the store’s sales by bundling the products together and giving discounts.
- So he bundled shampoo and conditioner together and gave a discount on them.
- Furthermore, customers will buy them together for a discounted price.
Future Forecasting:
- It is one of the widely applied techniques in Data Science.
- On the basis of various types of data that are collected from various sources weather forecasting and future forecasting are done.
Fraud & Risk Detection:
- It is one of the most logical applications of Data Science.
- Since online transactions are booming, losing your data is possible.
- For example, Credit card fraud detection depends on the amount, merchant, location, time, and other variables.
- If any of them looks unnatural, the transaction will be automatically canceled, and it will block your card for 24 hours or more.
Self Driven Car:
- The self-driving car is one of the most successful inventions in today’s world.
- We train our car to make decisions independently based on the previous data.
- In this process, we can penalize our model if it does not perform well.
- The car becomes more intelligent with time when it starts learning through all the real-time experiences.
Image Recognition:
- When you want to recognize some images, data science can detect the object and classify it.
- The most famous example of image recognition is face recognition – If you tell your smartphone to unblock it, it will scan your face.
- So first, the system will detect the face, then classify your face as a human face, and after that, it will decide if the phone belongs to the actual owner or not.
Speech & Text Convert:
- Speech recognition is a process of understanding natural language by the computer.
- We are quite familiar with virtual assistants like Siri, Alexa, and Google Assistant.
Health Care:
- Data Science helps in various branches of healthcare such as Medical Image Analysis, Development of new drugs, Genetics and Genomics, and providing virtual assistance to patients.
Search Engine:
- Google, Yahoo, Bing, Ask, etc. provides us with a lot of results within a fraction of a second.
- It is made possible using various data science algorithms.