In this blog following topics will be covered 

1. - Why Data Science?
2. - What is Data Science?
3. - Who is a Data Scientist?
4. - Job Trends
5. - How to solve a problem in Data Science?
6. - Data Science Components
7. - Data Scientist Job Roles

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Why Data Science?

• Harvard Business Review hails data science as "the sexiest job of the 21st century". What makes data science so important? 
• Why are data scientists the highest-paid professionals? 
• Most importantly, why study data science? 
• In this article, we will examine some of the main reasons why data science has become one of the most attractive jobs in the market. 
• We understand the needs of companies and the need for data scientists to maximize their performance.

What is Data Science?

• Data science is the process of combining experts, programming skills, knowledge of mathematics, and statistics from the field to gain meaningful insights from data. 
• Data science practitioners use machine learning algorithms for numbers, text, images, video, audio, and more to develop artificial intelligence (AI) systems to perform tasks required by the humanities. 
• These systems generate statistics that can be translated into clear business value for the consequences and for business users.
• Data science professionals are emerging as one of the most promising and desirable career paths for skilled professionals. 
• Successful data professionals today realize that large-scale data overrides traditional skills for analyzing data processing and programming skills. 

• In order to find effective statistics for their organizations, data scientists must have mastered the entire spectrum of the Data science life cycle and have the flexibility and understanding to maximize revenue at each stage of the process.

Who is a data scientist?

Data scientists have several definitions. Simply put, a data scientist is someone who adheres to the art of data science. The most popular term for ‘data scientist’ is Created by Patil and Jeff Hamperpatcher. 

Data scientists are people who twist complex data problems with strong expertise in certain fields of science. They work with a number of components, including mathematics, statistics, and computer science (although not experts in these fields).

Job Trends in Data Science 

Data science is forecast to grow over the next decade. It is a shocking fact that 90% of the world's data was created in just 2 years. 

• It is unimaginable to recognize the amount of data that will be generated in the next decade. 
• The demand for data scientists will increase by 28% by 2020 alone. 
• More and more businesses are starving for data, and they need data to include specialized data scientists who can design products for users. The U.S. The Bureau of Labor Statistics estimates that by 2026, about 11.5 million jobs will be created.

How to solve a problem in Data Science

1. Discovery:

• The first step is discovery, which involves asking the right question. When you start any data science project, you need to determine what the basic requirements, priorities and project budget are. 
• At this point, we need to determine all the requirements such as project number, technology, time, data, and an end goal, and then the business problem can be designed at the first conceptual level.

2. Data preparation: 

• Data preparation is also known as data munching. At this point, we need to do the following:
• Data cleaning
• Data reduction
• Data integration
• Data transformation
• After completing all of the above tasks, we can easily apply this data to our additional processes.

3. Model planning:

• At this stage, we need to determine the different methods and techniques for establishing the relationship between the input variables. 
• We use research data analysis (EDA) using various statistical formulas and visualization tools to understand the relationships between variables and to see what data can tell us. 

4. Model Building: 

• At this stage, the model construction process begins. 
• We will create databases for training and testing purposes.

• We will use various techniques such as association, classification, and clustering to create the model.

5. Operationalize: 

• At this stage, we will provide the final reports, summaries, code, and technical documentation of the project. 

• This step gives you a brief overview of the full project performance and other factors prior to full deployment.

6. Communicate the results: 

• At this stage, we will check whether we are reaching the goal we set in the initial stage. 

• We will communicate the findings and final results with the business team.

Data Science Components

1- Statistics:

• Statistics are an important component of data science. 

• It is a method of collecting and analyzing large quantities of numerical data to obtain useful and meaningful statistics.

2-Visualization:

• Visualization is the representation of data in areas such as maps and maps so that people can easily understand it. 

• This makes it easier to access extensive data. 

• The main goal of data visualization is to help identify patterns, trends, and foreigners in large data sets.

3-Machine Learning:

• Machine learning acts as the backbone of data science. 

• This means training a machine to function as the human brain. 

• Different methods are used to solve problems. 

• With the help of machine learning, it is easy to make predictions about unexpected/future data.

4- Domain expertise:

• Domain expertise is the specific knowledge or skills of a particular area. There are different areas of data science, for which we need field experts. 

• You cannot open the entire feature of an algorithm without proper knowledge of where the data is coming from. 

• The more we know about the problem, the more difficult it will be to solve. Also, a high level of expertise in the area will greatly improve the accuracy of the model you want to create. 

• This is why data scientists are generally well aware of the various areas in which they work. 

• They may not be experts, but a good data scientist usually focuses on multiple skills.

5- Data Engineering:

• Includes data engineering, data recovery, storage, retrieval, and transfer. The key to understanding data engineering is in the field of engineering. 

• Engineers design and create things. Data engineers design and create tubes that transform and carry data, making it very useful for data scientists and other end users. 

• These pipelines take data from different sources and store it in a single warehouse.

6- Programming Languages:

Python: 

• Python is a high-level programming language provided by a wide-ranging library. This is a very popular language because most data scientists like it. 

• It offers expandable and generated data analysis libraries. 

• The best features of Python are dynamic type, functional, object-oriented, automated memory management, and practice.

R: 

• R is a popular programming language among data scientists that can be used on Windows, Unix, and Mac operating systems. 

• The best feature of the R language is data visualization, which is tough on Python, but it is more startup-friendly than Python. 

• R language is used to perform social analysis using subsequent data. Twitter language is used for data visualization and semantic clustering, and Google uses it to evaluate ad performance and make financial predictions.

Data Scientist job role?

The role of the data scientist is really a challenge! Although the skill packages and capabilities used by data scientists may vary as a skilled data scientist.

• Be very innovative and unique in his approach to extracting data, gaining useful insights into solving business problems and challenges, and using various technologies intelligently.

• Ability to find and create rich data sources.

• A handful of experience in data mining techniques such as graph analysis, method finding, result perspectives, clustering, or statistical analysis.

• Develop working models, systems, and tools using experimental and functional methods and techniques.

• Analyze data from different sources and perspectives and find hidden statistics.