Sunday, November 24, 2019

Importance of Data Science in the Modern World and Steps of a Data Science Project Lifecycle

Data science is an important field focused on understanding and drawing specific business, financial, manufacturing and medical research and forecasting. 
The data science process is about analyzing, visualizing, extracting, managing and storing data to create insights from analytics. 
These insights and reports help companies analyze their marketing strategies, make powerful data-driven decisions and create better advertisements.

Data Science Process
Importance of Data Science in the Modern World -Steps of a Data Science Project Lifecycle

Importance of Data Science in the Modern World and Steps of a Data Science Project Lifecycle

What is Data Science?

Data science is a field of Big Data designed to provide meaningful information based on large volumes of complex data. Data science combines various fields of work in mathematical, statistical and computational tasks to interpret data for decision-making purposes.

What is a Data Scientist?

A data scientist is someone who collects large volumes of data, analyzes them, and interprets to help a company increase its sales and improve its operations. 
Certified professionals in data science develop advanced analytical tools and statistical models to analyze data and use various findings and results to find trends, patterns, and relationships in data sets.
This information can be used to calculate the repeat purchase rate and predict consumer behavior while identifying operational risks and meeting the challenges faced by small businesses. 
A data scientist is often a storyteller who presents data insights to people in an organization or institution in a simple and effective way.

Importance of Data Science in the Modern World 

Data science is important to improve marketing. Big data and data science are key factors for future development.
The data science process is about analyzing, visualizing, extracting, managing and storing data to create meaningful insights from analytics. These insights and reports help companies analyze their marketing strategies, make powerful data-driven decisions and create better advertisements.

Data is drawn from various fields and platforms, including social media, phones data, e-commerce sites, healthcare surveys, internet searches, etc. Increasing the number of available data opens the door to develop a new field of study called Big Data - or extremely large data sets that can help produce better operational tools in all areas. 

Easy access to continuously growing sets and data is possible in collaboration with financial technology companies, who use technology to innovate and enhance traditional financial products and services.

The data produced creates more data for emerging financial technology products such as cloud computing and storage that are easily shared across all entities. 
However, the interpretation of vast amounts of unstructured data for effective decision making can prove to be very complex and time-consuming for companies. To deal with such inconveniences, data science has emerged in the world.

Benefits of Data Science

 Data Science and Big Data are very important to help improve the company's operations in the future. Data science is very beneficial for better marketing forecasting.
Data science can help reduce the constraints of time and budget allocation and help in the development of business.
Data science has determined the results of many manual tasks that could be superior to human influences.
Data science helps prevent a debt default, is used in fraud detection, and many other cases in the financial domain.
Data science generates insights from raw, unstructured text data.
Data science helps predict future results that can prevent the financial losses of many large corporations.

Subsets of Data Science

Data science is a mixture of artificial intelligence, machine learning, deep learning, mathematics and statistics, domain knowledge, Information technology, and software development.

Everything from an analysis of core data to mathematics and statistics to the model building is the basics of data science, which requires dealing with probability, numerical, vectors and more.

Machine learning can be divided into artificial intelligence and deep learning. Actually, machine learning is a subset of artificial intelligence and is a model building subset of data science.
Additionally, the necessary software development and information technology skills are considered essential to apply machine learning in those areas.

Finally, basic knowledge of business domains may take a long time to determine the accuracy of results as different businesses use different data analytics for prediction and verify the reliability of the outputs using the correct metrics and right data.

How Data Science Works?

Data science involves tools and technologies, ranging from multiple disciplines to large collected data sets. These tools derive insights, extract meaningful data from data sets, and interpret that data for decision-making purposes.
Disciplinary fields that make up the data science field include mining, statistics, analysis, machine learning, and some programming. 
Data mining applies algorithms to complex data sets to reveal patterns that are used to extract usable and relevant data from the sets. 
Statistical measures such as predictive analysis use this extracted data to measure events based on future events. 
Machine learning is an artificial intelligence device that processes large amounts of data that humans will be unable to process over a lifetime. 
Machine learning affects the decision model presented under the approximate analysis by matching the probability of the event to what actually happened at the estimated time.

Steps of a Data Science Project Lifecycle

Data science process will be divided into the following categories.

1. Exploratory Data Analysis: It is said that 90% of the work done by a data scientist is related to data analysis. The term "data analysis" refers to the cleaning and pre-processing of data before the construction of a statistical model. This step includes outliers, duplicate data, null values ​​, and many other anomalies, which do not fall within the data agreement required for business purposes.

2. Understanding the problem: It is imperative that the details of the problem are clear before you dive into the actual implementation part. It is important to find out what is right to get the right data and get the right solution.

3. Getting the right data: Once the problem is understood, it is mandatory to get the right data to perform the operation.

4. Using the correct metrics: Depending on the business domain, the metric that will determine the completeness of a model should be selected.

5. Data visualization: Data visualization is a general term that refers to a graphical representation of information and data using visual elements such as graphs, charts, and maps. 
Data visualization tools help people understand the importance of data and provide an accessible way to see the trends, patterns, and relationships in data.
Once data has been cleaned and processed in advance, it is necessary to visualize the data to determine the correct features or columns to use in the statistical model.

6. Model Selection: The selection of the correct model is necessary for a particular problem statement because each model may not fit perfectly to each data set.

7. Hierarchical Encoding: This step of the data science process is applicable for instances where input attributes are explicit and need to be converted into numbers used in the model because the machine cannot function properly with some ranges.

8. Communication: businessmen, salesmen or shareholders, usually do not understand the technical knowledge of data science, and therefore it is necessary for their business to communicate the findings, products, and services to their customers in simple terms, which can then come up with measures to alleviate any potential risk.

9. Deployment: Sometimes, the word "implementation" is used to mean the same thing. Once the statistical model is built, and the business domain is satisfied with the findings and results, this model can be deployed and implemented to build analytical tools and improve business efficiency.

Breathtaking Applications of Data Science

Data science has helped bring the financial industry into the tech-savvy era. Through the use of data science, companies are using big data to derive value from their consumers.

Asset management firms are using big data to predict the probability of data security going up or down in the future.

Banking institutions are capitalizing on big data to enhance their fraud detection success.

Netflix—An American media-services provider and production company—uses data science and analytics to determine what its users are interested in and uses this information to decide on the creation and host of TV shows. The company also uses existing algorithms to create personalized recommendations on what to watch based on the user's viewing history.

Under analytics, the data analyst collects and processes structured data from the machine learning phase using algorithms. He interprets the data into a cohesive language, converts, and summarizes what the decision-making team can understand. 
As the role of a data scientist is better understood, more skill sets will be added to the field that includes areas such as data architecture, data engineering, and data administrators.

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