It is the right time to choose your career path as a data scientist because, among various evolving technology, data scientists' demands are increasing exponentially. According to the job portals, data scientists have created numerous job opportunities that other tech-based jobs.
The U.S. Bureau of Labour Statistics (BLS) anticipates that demand for data scientists will increase with the acceleration of three times faster than the previous year.
Data scientist positions were recognized as the best in the U.S., making it the industry's fourth year at the top of the list. Data scientists are in demand across all industries nationwide, and this desire doesn't seem to be decreasing anytime soon.
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In this blog, we shall discuss data science types, the application of data science, concepts of data science and uses of data science.
What is Data Science?
Data science is the study of a huge amount of data, in which data scientists are involved in extracting meaningful insights from the raw structure and unstructured data. The method is processed by the scientific method using multiple technologies and algorithms.
Data science is a multidisciplinary field in which scientists utilise various data-driven tools and techniques to manipulate data. By this, you can drive meaningful and valuable data.
To resolve the data-oriented complexity,components of data science are essential. The components of data science are:
- Math and Statistics Expertise
- Computer Science Skills
- Business Knowledge.
Data science uses hardware, programming systems, and algorithms to solve data-related issues. Moreover, it is the future of artificial intelligence.
- Data science helps in analyzing structured and unstructured raw data.
- Aid in modelling data using the algorithm
- Help to visualize the data structure
- Finally, help in deriving valuable data for decision-making.
Example:
If you intend to travel from one part of the country to another by car, you will take some decisions like which routes will be easy to reach your destination, which routes will be traffic free and so on. These decision-making criteria will serve as input data, and we will use these decisions to determine the right response; therefore, this data analysis is a component of data science.
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Importance of Data Science and application of data science
Times in the past, data was stored in a structure on an excel sheet easily and could be processed using Business Intelligence tools. Still, data storage has becoem massive, which approximately 2.8 quintals of bytes generated daily. Due to the data explosion, every Business requires data scientists to handle the massive data, which is becoming a challenging task for every organization.
As a result, we needed complex, strong, and effective algorithms and technology to handle, process, and analyze this; that technology is known as data science. These are some of the key justifications for employing data science technology:
- Data science technology allows us to transform a sizable volume of unstructured, raw data into insightful knowledge.
- Most prominently, data science is also used for automation purposes. For example, data science has bought great transformation in the self-driving car, which is hoped to bring a significant shift in transportation.
- Top companies like Accenture, IBM, Deloitte and many other small to large enterprises utilized data science for business processes and growth.
- For example, Netflix, Google, and Amazon use data science algorithms to manage massive data to provide a better customer experience.
- Data science can assist with many predictions, including surveys, elections, confirming aircraft tickets, etc.
Application of data science
- Risk Detection
- Healthcare
- Search engine
- Transport
- Image Recognition
- Speech Recognition
- Airline Route Planning
- In E-Commerce
- Gaming
Data science Jobs
According to the survey, it is stated that data science is expected to create more than 2 million jobs globally. As it became a demanding career, it is considered the best career path for professionals because data science compiles various job roles.
Data scientists are professionals who can interpret and analyze data using a wide range of statistical tools and machine-learning techniques. The average pay scale for a data scientist will be between $92,000 and $ 163,000 per year. According to several studies, by the year 2026, 11.5 million new jobs will create.
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Types of Data Science Job
Learning data science gives you the chance to find a variety of intriguing career possibilities in this industry. The following list of major job roles:
- Data Scientist
- Data Analyst
- Machine learning expert
- Data Engineer
- Data Architect
- Data Administrator
- Business Analyst
- Business Intelligence Manager
The definitions of a few critical positions in data science are provided below.
Data Analyst
A data analyst models large amounts of data, analyses it, and looks for patterns, trends, etc. He creates reporting and visualization for data analysis for decision-making and problem-solving.
Skill required
- Mathematics
- Knowledge of statistics.
- Data mining
- Business intelligence
- Computer languages and tools include Python, Hive, SQL, Spark, Pig, Excel, JS, etc.
Machine Learning Expert
Machine learning professionals work with multiple machine learning algorithms in data science, such as regression, Decision Tree, Linear Regression, Logistic Regression, Naive Bayes, etc.
Skill Required
- Must have Programming language skills such as Python, Java, etc.
- Understanding of data science algorithms
- Probability and statistics skills
- problem-solving and analytical skills.
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Data Engineer
Working with vast amounts of data, a data engineer is in charge of creating and managing the data model of a data science project.
Skill required
- Knowledge of SQL, MongoDB, HBase, Apache Spark, Hive, etc
- Programming language skills like Python, Java, Perl, etc.
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Data Scientist
A data scientist is an expert who uses a massive quantity of data and a broad range of tools, techniques, algorithms, etc., to provide compelling business insights.
Skill required
- Technical language skills include SAS, Python, Pig, SQL, Apache spark, R, Hive, etc.
- Data science must have a profound understanding of statistics, Mathematics, visualization, and communication skills.
Technical Prerequisite
- Machine learning: If you intend to becoem a data scientist, you must understand the core concepts of machine learning because data scientists must use a machine learning algorithm to solve complex problems.
- Mathematical modelling: Data scientists must do mathematical calculations. These skills will help you exact the valuable data from the massive data structure.
- Statistics: Data scientists must have a fundamental understanding and knowledge of statistics and its concepts. This skill will help you data-driven approach and analysis.
- Computer programming: You must know at least one programming language for data science. Some of the computer programming languages needed for data science are R, Python, and Spark.
- Databases: Data science requires a thorough understanding of databases, such as SQL, to obtain and deal with data.
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Data Science Components
The following list includes the key elements of data science:
- Statistics: It is one of the essential components of data science, which helps us to gather, and evaluate the numerical data in a massive amount to analyze meaningful insights.
- Domain Expertise: In data science, Domain expertise means the person with specialized knowledge or skills in your role. If you choose your career in data science, you need to be an expert in your preferred domain.
- Data engineering: Data engineering is also a division of data science, in which Data engineering deals with gathering, maintaining, retrieving, and transforming data.
- Visualization: The goal of data visualization is to present information visually so that viewers can understand its data information quickly. The primary purpose of data visualization is to visually present a huge amount of data in a simple method.
- Advanced computing: Advanced computing skills are the primary industry standard of data science. Planning, developing, troubleshooting, and managing computer programme source code are all part of advanced computing.
- Mathematics: Data science requires a strong background in mathematics. The study of Statistics, Linear Algebra, Probability, and Calculus are the main component of mathematics while you are involved in data structuring and analysis. If you choose this path, you must have good mathematical skills.
- Machine learning: Data science is built on machine learning.
Machine learning aims to train a machine to function like a human brain. We employ various machine learning methods in data science to overcome the difficulties.
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Tools for Data Science
Here are a few tools needed for data science:
- Data Analysis tools: R, Python, SAS, R Studio, RapidMiner, etc.
- Data Warehousing: ETL (Extraction, Transformation, and Loading), SQL, Hadoop, AWS Redshift, etc.
- Data Visualization tools: Zoho Reports, Jupyter, Visual.ly, Tableau, RAW, Cognos, etc.
- Machine learning tools: Spark, Scikit-learn, Mahout, Weka, Azure ML studio, etc.
Machine learning in Data Science
To become a data scientist, you must have an in-depth understanding of machine learning and its methods. This is because many machine learning algorithms are employed in data science. The names of a few machine learning algorithms used in data science are as follows:
- Regression
- Decision tree
- Clustering
- Principal component analysis
- Support vector machines
- Naive Bayes
- Artificial neural network
- Apriori
Here, we'll provide you with a brief introduction to a few of the most significant algorithms.
Linear Regression Algorithm: The most widely used supervised learning-based machine learning algorithm is linear regression. Regression is a modelling technique that relies target values on independent variables and shows how this algorithm operates. It depicts the shape of the linear equation, which establishes a connection between a collection of inputs and a prognostic outcome.
Decision Tree: The decision Tree algorithm is the best machine learning algorithm which aids in the supervised learning algorithm. Among various algorithms, the decision tree is most prominent in machine learning. With the aid of this, we can solve both classification and regression problems.
K-Means Clustering: It is one of the essential machine learning algorithms used for unsupervised learning algorithms. The prominent purpose of using this algorithm is to solve the complex problem of clustering.
So far, we have discussed the components of data science, the application of data science and data science types. Now, we shall look at the life cycle of data science.
The life cycle of data science
Applications of Data Science
Above, we have discussed the applications of data science. Now, we must understand how data science applications are used in sectors.
Image recognition and speech recognition
- Data science is used for various purposes; among these, it is prominently used forImage and speech recognition. For example, once you upload your image on Instagram, it will ask for suggestions for tagging your friends. These automation suggestions are processed by an image recognization algorithm which is part of data science.
- Another best example is that when you say hello to google or Siri, the device responds to your voice and provides the thing you require. Here are data science algorithms used for speech recognization.
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Gaming world
Machine learning algorithms are being utilized progressively in the game industry. Data science is commonly used by companies like EA Sports, Sony, and Nintendo to improve user experience.
Internet search
When we search for something on the internet, we use different search engines like Google, Bing, Baidu, Yahoo!, Yandex, Ask, DuckDuckGo, etc.
These search engines have data science applications and are integrated with data science technology; due to this, users can get their required information in a fraction of a second. So, here data science is used to gain a better user experience.
Transport
Self-driving automobiles are being developed by the transportation sector using data science technology. The number of traffic accidents can be quickly decreased with self-driving vehicles.
Healthcare
Data science has many advantages in the healthcare industry. Tumour identification, medicine identification, medical image analysis, virtual medical robots, and other data science applications are being applied.
Recommendation systems
Several organizations, like Amazon, Netflix, Google Play, and others, use data science technologies to improve the user experience with tailored recommendations. For example, data science technology is responsible for the suggestions for related products you receive when you search for something on Amazon.
Risk detection
Fraud and the risk of shortfalls have long been problems in the finance sector, but data science can help in fraud detection.
To reduce risk and potential expenses and to improve customer experience, most banking organizations are looking for data scientists.
Now that you have understood the real benefit and scope of data science. Moreover, you have comprehended the components of data science, concepts of data science and various application of data science. So, to begin your career in such a demanding field, you can join
Data Science Course in Bangalore will help you flourish in your career in data science.
