What Is Data Science, What Does It Do, And Why Is It Important To Companies?

What Is Data Science, What Does It Do, And Why Is It Important To Companies?

Data Science Tells You How Data Can Provide Insightful Business Insights, Accelerate Digital Transformation, And Allow You To Make Data-Driven Decisions. 

In this article, we will explore what data science means.

What is data science?

Data science combines mathematics and statistics, specialized programming, advanced analytics, artificial intelligence, and machine learning with other technical skills to reveal hidden insights at the heart of enterprise data. These insights can be used to guide strategic planning and decision-making.

The life cycle of a data science project

Organizations increasingly rely on data to interpret data and derive actionable recommendations to improve business outcomes. The increasing amount of data sources and the nature of that data has made data science one of the fastest-growing fields in any industry. As a result, it’s no surprise that the data scientist role has been named the hottest job of the 21st century by the Harvard Business Review. The data science lifecycle includes various parts, tools, and processes that enable analysts to gain actionable insights. Typically, a data science project goes through the following steps to completion:

  • Data capture: The life cycle begins with data collection. These data are structured or unstructured and collected in various ways from different sources. Data sources may host structured data, such as customer data, or unstructured data, such as log files, video, audio, images, IoT, social media, etc. These methods can include manual entry, web searches, or data generated in real time by systems and devices.
  • Data storage and processing: Since data can have different formats and structures, companies should consider other storage systems based on the data collection type. For this purpose, IT and data management teams prepare data storage and structure standards so workflows around analytics, machine learning, and deep learning models have an integrated form. This step involves data cleansing, copying, transforming, and combining data using ETL (Extract, Transform, Load) processes or other data integration technologies. This data preparation is done to improve the quality of the data before it is stored in a data warehouse, data lake, or repository.
  • Data Analysis: Data scientists perform exploratory data analysis to examine biases, patterns, ranges, and distributions of values ​​in data. This process of data discovery and analysis helps them conduct a/b tests. It also allows analysts to discover data relationships and use data more accurately in modeling efforts and perform predictive analytics. Depending on the model’s accuracy, organizations use the received information for business decisions to achieve the desired insight and expand the range of business activities.
  • Communication: Finally, insights are used in the form of reports and other data visualization mechanisms to make it easier for business analysts and other stakeholders to understand the senses and their impact on the business. Data science programming languages such as R or Python provide visualization capabilities to organizations. Of course, data scientists can also use dedicated visualization tools.

Data science and data scientist

Data science is a field, while data science is a job title associated with this field. Note that data scientists are not directly responsible for all processes involved in the data science lifecycle. For example, data transmission lines are usually managed by data engineers. Still, the data scientist may recommend the kind of valid data or how to construct these lines.

While data scientists can build machine learning models on a macro scale, more software engineering skills are needed to optimize a program to run faster. For this reason, in most cases, a data scientist works with machine learning engineers to scale machine learning models.

Typically, the responsibilities of a data scientist may overlap with that of a data analyst, particularly for exploratory data analysis and data visualization. However, the skill set of a data scientist is broader than that of a data analyst. In addition, data scientists use common programming languages ​​such as R and Python for statistical inference and data visualization.

To perform these tasks, data scientists need more computer and scientific skills than a typical business analyst or data analyst. Also, the data scientist should have sufficient knowledge about the various aspects of the businesses they are planning to enter, such as e-commerce, finance, or healthcare.

In summary, a data scientist should be able to:

  •  Have enough information about the business to ask relevant questions and identify the problematic points of the company.
  •  Use statistics, computer science, and business intelligence in data analysis.
  • Use a wide variety of tools and techniques to prepare and extract data. To be more precise, I can work with different types of relational and non-relational databases in data mining and use other methods for data integration.
  •  From big data and with the help of analysis solutions, extract insights from data and provide accurate predictions. For this purpose, it must have the Ability to work with machine learning models, natural language processing, and deep learning.
  •  To have the Ability to build programs that automatically perform data processing and calculations.
  •  Its Ability to explain technical issues in the form of stories so that decision-makers and stakeholders at any level of technical knowledge understand what he means.
  •  Explain how the obtained results can be used to solve business problems.
  •  Collaborate with other members of the data science team, such as data and business analysts, IT architects, data engineers, and application developers.

These skills are highly sought after by companies, and as a result, most people entering the data science profession try to take various courses to acquire the necessary skills.

Data Science vs. Business Intelligence

Since data science and business intelligence have many similarities, they are often confused because they both focus on analyzing the organization’s data but do so in different ways.

Business intelligence refers to the set of actions of data preparation, data mining, data management, and data visualization. Business intelligence tools and processes enable end users to extract actionable information from raw data.

This issue has made business intelligence facilitate data-based decisions in various organizations and industries. Business intelligence focuses more on data that is already available, and the insights provided by business intelligence tools are more descriptive than data science.

It uses data to understand what has happened in the past to provide general information about a set of actions to be taken in the future. Business intelligence tends towards static data, which is usually structured.

In contrast to data, science tries to use descriptive data to determine predictor metrics and then use these variables to categorize data or make predictions.

However, the vital thing to note is that data science and business intelligence are not mutually exclusive; innovative organizations use both to fully understand and extract value from their data.

Data science tools

Data scientists rely on popular programming languages to perform exploratory data analysis and statistical regression. These open-source languages ​​support built-in statistical modeling, machine learning, and graphics capabilities. These languages ​​are as follows:

  • R Studio: The R Studio development environment allows developers to use the R programming language and an efficient development environment for statistical and graphical computing.
  • Python is a dynamic and flexible programming language. Python includes libraries like NumPy, Pandas, and Matplotlib for fast data analysis.

To facilitate the sharing of code and other information, data scientists may also use GitHub and Jupyter notebooks. Two standard organizational tools used for statistical analysis are as follows:

  • SAS: Comprehensive toolset for visualizations and interactive dashboards for analytics, reporting, data mining, and predictive modeling.
  • IBM SPSS: Provides advanced statistical analysis capabilities and includes an extensive library of machine learning algorithms, text analysis, open source extensibility, comprehensive data integration, and seamless deployment of models in applications.

Data scientists use big data processing platforms such as Apache Spark, the open-source Apache Hadoop framework, and NoSQL databases to do their work. They use a wide range of data visualization tools, including Microsoft Excel, commercial visualization tools Tableau and IBM Cognos, and open source tools such as D3, a js library used to create interactive data visualization charts, as well as RAW charts to perform. They operate daily activities.

To build machine learning models, data scientists often use frameworks such as PyTorch, TensorFlow, MXNet, and Spark MLib.

People who have different skills in analyzing data. Typically, data science and analytics projects are time-consuming, and companies look for accelerated ROI. For this reason, they try to hire top talents in this field—p

On the other hand, some companies are turning to machine learning-based data science (DSML) platforms, preferring to focus on a concept called the “citizen data scientist.”

Using the DSML platform makes intra-organizational collaboration more efficient. DSML platforms leverage automation, self-service portals, and low-code or no-code user interfaces so that people with little to no digital technology or data science background can create business value using data science and machine learning. In addition, the above platforms also support expert data scientists by providing a technical interface.

Data science and cloud computing

Cloud computing provides professionals with access to mighty processing power, ample storage space, and other tools needed for data science projects in a scalable platform.

From where science has often used big data, tools that can scale with data are essential for time-sensitive projects. Cloud storage solutions, such as data lakes, provide access to storage infrastructure that can quickly receive and process large volumes of data. These storage systems provide flexibility to end users and allow them to make changes to large clusters as needed.

They can add incremental compute nodes to accelerate data processing tasks, allowing businesses to perform short-term processing to achieve long-term results. Typically, cloud platforms have different pricing models and provide the required resources to end users based on a subscription model.

Data science is used. When teams host workloads in the cloud, they no longer have to worry about installing, configuring, maintaining, or updating equipment locally. Today, major cloud service providers such as IBM, Microsoft, Google, Amazon, and the like have designed ready-to-use kits that enable data scientists to build models without coding and gain insight driven by data.

Use cases of data science

Data science provides many benefits to companies. However, in most cases, data science is used to optimize processes through intelligent automation, targeting, and personalization of offers to improve the customer experience. In more specific applications, data science is used to:

  • Banks that provide quick services such as loans through mobile apps can use machine learning-based credit risk models and cloud-based hybrid architecture to check the process of allocating or not allocating loans to customers.
  • An electronics company is developing 3D-printed sensors to guide self-driving cars. To do this right, the company relies on data science and analytics tools to increase object detection accuracy in real-time.
  • A robotic process automation (RPA) solution provider can create a cognitive business process mining solution that reduces customer incident handling time by 15 to 95 percent. The answer should receive data-driven training to understand the content and sentiment of customer emails so that the sales team can provide valuable recommendations to customers via email.
  • A multimedia company can build an audience-driven analytics platform that allows clients to see what drives the most audience engagement. This solution can use deep analytics and machine learning to gain real-time insight into viewer behavior.

Data science and job opportunities in this field

Data science allows you to focus on one area of ​​expertise. Among the job positions in data science, the following should be mentioned:

data scientist

A data scientist identifies problems and provides data-driven solutions to solve them. Also, it describes the issue from which sources the required data should be obtained. These professionals help organizations extract, refine, and refine sent relevant data. Typically, a data scientist needs programming skills (SAS, R, Python), data storytelling and visualization, statistical and mathematical skills, extensive data management and databases, and machine learning.

data analyst

Analysts bridge the gap between data scientists and business analysts, organizing and analyzing data to answer organizations’ questions. They focus on technical analysis and try to provide qualitative analysis. A data analyst needs statistical and mathematical skills, programming skills (SAS, R, Python), and data visualization.

data engineer

Data engineers focus on developing, deploying, managing, and optimizing an organization’s data infrastructure and transmission lines. Engineers help data scientists by transferring and transforming data into a form that can run queries on it. A data engineer needs skills working with NoSQL databases like MongoDB, and Cassandra DB, programming languages ​​like Java and Scala, and frameworks like Apache Hadoop.

What does a data scientist do?

Now you know what data science is, and you must wonder what a data scientist does. A data scientist analyzes business data to extract meaningful insights. In other words, a data scientist solves business problems through a series of steps as follows:

  •  Before collecting and analyzing data, the data scientist asks questions to understand the problem correctly.
  •  Next, the data scientist determines the correct variables and data set.
  •  A data scientist collects structured and unstructured data from a variety of sources. More precisely, it goes to organizational data, public data, etc.
  •  After collecting the data, the data scientist processes the raw data and converts it into a format suitable for analysis. This approach includes data refinement and validation to ensure consistency, completeness, and accuracy.
  •  After the data has been converted into a usable form, it is fed into an analytical system based on machine learning or a statistical model. It is where data scientists analyze and identify patterns and trends.
  •  Once the data is thoroughly presented, the data scientist interprets it to find opportunities and solutions.
  •  Data scientists complete the work by preparing results and insights to share with stakeholders and communicate the final results.