What is Data Science? – Development, Process, and More

Introduction

Data science has become an essential portion of many industries and one of the most discussed topics in IT circles, given the sheer amount of data produced. As a result, its popularity has grown over the years, and also companies have started implementing data science techniques to grow their business and increase customer satisfaction. In this object, you will learn what data science is and how you can become a data scientist.

What is Data Science?

As a speciality, data science is young. It comes from the fields of statistical analysis and data mining. Data Science Journal was published in 2002 and edited by the International Council for Science: Committee on Data for Science and Technology. In 2008, the title of data scientist appeared, and the field rapidly gained momentum. Since then, there has been a shortage of data scientists, although more colleges and universities have begun to offer data science degrees.

Data Scientist responsibilities may include developing data analysis strategies, preparing data for analysis, exploring, analyzing, and visualizing data, creating models with data using programming languages ​​such as Python and R, and implementing models in applications.

The data scientist does not work alone. Active data science is prepared in teams. In addition to a data scientist, this site may include a business analyst who defines the problem, a data engineer who designs the data and how to access it, an IT architect who oversees the underlying processes and infrastructure, and a software developer. Applications that provide the models or results of the analysis to be transformed into applications and products.

The Data Science development

Now that you recognize what data science is let’s focus on the data science lifecycle. The data science lifecycle consists of five different phases, each with its tasks:

• Capture: data acquisition, data input, signal reception, data extraction. This step involves collecting raw structured and unstructured data.
• Maintenance: data storage, data cleansing, data delivery, data processing, data architecture. This step includes taking the raw data and converting it into a usable form.
• Process: data mining, clustering/classification, data modelling, data summary. Data scientists take prepared data and examine its patterns, ranges, and also biases to determine its usefulness in predictive analytics.
• Analysis: Exploratory/Confirmative, Predictive Analysis, Regression, Text Mining, Qualitative Analysis. Here is the real meat of the life cycle. This step consists of performing the various analyzes of the data.
• Communicate: data reporting, data visualization, business intelligence, result making. In this final step, analysts prepare the analysis in an easy-to-read format, such as tables, graphs, and reports.

Prerequisites for Data Science

Here are some technical concepts you need to know before learning it.

Machine Learning

Machine learning is the backbone of data science. Therefore, data scientists should have a solid understanding of ML in addition to a basic knowledge of statistics.

Modelling

Math models allow you to perform quick calculations and make predictions based on what you already know about the data. Modelling is also part of machine learning and involves identifying which algorithm is best suited to solve a given problem and how these models can be trained.

Statistics

Statistics are at the heart of data science. Strong statistics management can help you get more insights and produce more meaningful results.

Programming

A certain level of programming is required to complete a it project. The most mutual programming languages ​​are Python and R. Python is particularly popular because it’s easy to learn and supports multiple data science and ML libraries.

Databases

A skilled data scientist should understand databases, manage them, and also extract data.

Who oversees the process of Data Science?

In most organizations, it projects are typically led by three types of managers:

Business Managers

These managers work with the data science squad to describe the problem and strategize for analysis. They may be the leader of a line of corporate, such as marketing, finance, or sales, and also have a data science team reporting to them. You will work closely with IT and its leaders to confirm project delivery.

IT managers

Senior IT managers are answerable for the infrastructure and architecture that support it operations. They continuously monitor operations and resource usage to ensure it teams work efficiently and securely. You may also be responsible for creating and updating computing environments for its teams.

Data Science Managers watch over the data science team and their day-to-day work. They are team makers who can balance team development with project planning and tracking.

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Use of Data Science

• It can uncover patterns in seemingly unstructured or unconnected data, allowing conclusions and also predictions to be drawn.
• Technology companies that collect user data can employ strategies to turn that data into valuable or profitable information.
• Such as driverless cars, has also made its way into the transportation sector. For example, it is easy to reduce the number of accidents by using driverless vehicles. For request, in the case of driverless cars, the training is fed into the algorithm, and the data is examined using it approaches, such as B. the speed limit on the highway, busy roads, etc.
• its applications offer a higher level of therapeutic personalization through genetic and genomic research.

Where do you fit in Data Science?

It allows you to focus and specialize in one aspect of the subject. Here is an example of how you can fit into this exciting and growing field.

Data scientist

• Responsibilities: Determine what the problem is, what questions need to be answered, and where to find the data. They also extract, clean, and present relevant data.
• Required skills: Programming skills (SAS, R, and Python), data storytelling and visualization, statistical and also mathematical skills, knowledge of Hadoop, SQL, and machine learning.

Analyst

• Job Role: Analysts bridge the gap between data scientists and business analysts by organizing and also analyzing data to answer questions posed by the organization. They take technical analysis and turn it into qualitative metrics.
• Required skills: Statistical and mathematical skills, programming knowledge (SAS, R, Python), and also experience in data management and visualization.

Data Engineer

• Job Role: It focus on developing, implementing, managing, and optimizing the organization’s data infrastructure and data pipelines. Engineers help data scientists translate and transform data for queries.
• Required skills: NoSQL databases (e.g., Mongo DB, Cassandra DB), programming languages ​​such as Java and Scala, and frameworks (Apache Hadoop).

Tools

Being a data scientist is challenging, but fortunately, many tools help data scientists succeed in their work.

• Data Analysis: SAS, Jupiter, R Studio, MATLAB, Excel, Rapid Miner
• Data Storage: Informatica/Talend, AWS Redshift
• Data visualization: Jupiter, Tableau, Cognos, RAW
• Machine Learning: Spark MLib, Mahout, Azure ML Studio

Applications of Data Science

It has started its applications in almost every business.

• Health Care
• Gaming
• Image Recognition
• Recommendation Systems
• Logistics
• Fraud Detection
• Internet Search
• Speech Recognition
• Targeted Advertising
• Airline Route Planning
• Augmented Reality

Conclusion

Data will be the lifeblood of business for the foreseeable future. Knowledge is power, and data is an actionable insight that can mean the difference between business success and failure. By integrating data techniques into their business, companies can predict future growth, anticipate potential problems, and also develop informed strategies for success.

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