What is Data Science? Introduction and Importance

What is Data Science?

Data is an integral part of the modern IT industry, often referred to as the "new oil" for its valuable role in driving business growth. Companies collect vast amounts of data every day from their customers and users, but what is the purpose of all this data?

Data science is the field of study that deals with the extraction of meaning from large amounts of data. A Data Scientist is the professional who applies this science to data. In this article, we will cover:

What is data science?
The importance of data science through a real-world case study
The types of data collected by companies
How companies use the data they collect
The skills required to become a data scientist
How data science helps businesses grow

Now that you have an understanding of what data science is, let's dive deeper and explore its significance through a case study.

Importance of Data Science

Data science is no longer just a future trend, it is already a dominant force in the industry. Companies collect vast amounts of data, and data scientists use advanced techniques to extract valuable insights from it. These insights are used by companies to make important business decisions. To further illustrate the power of data science, let's take a closer look at a real-world case study.

Case Study

A milk farm located in the city was experiencing steady annual growth in revenue. The owner was dedicated to understanding customer feedback and collected monthly ratings from customers. This was successful for several years, but the owner noticed a sudden decline in the average rating. With a large customer base, it was not feasible to survey every consumer, so the owner asked nearby customers for their input. They reported that they did not have any issues with the milk.

To find the root cause of the problem, the owner sought the help of a Data Scientist. The data scientist analyzed the monthly rating data provided by the owner and noticed that the ratings were significantly lower in July, August, and September. By plotting the data and extracting weather data for the city, the data scientist discovered that the problem was related to the rainy season. However, as the owner had mentioned, nearby customers were satisfied, so the problem was with distant customers.

The data scientist, being an expert in his field, hypothesized that the problem could be related to the containers used to deliver the milk to distant customers. The more the container was exposed to rain, the more likely it was that water would mix with the milk. The owner was able to use this information to make changes to their delivery process, which solved the problem and improved customer satisfaction.

Case study to show the importance of data science

The owner of the milk farm conducted an investigation and found that the data scientist's hypothesis was correct. He made the decision to change the position of the container's opening from the top to the bottom, and sealed it to prevent water from entering. This decision proved to be successful and helped the owner retain thousands of customers.

This case study highlights the importance of data in making strategic decisions. It emphasizes the value of data science, the field of extracting meaning from data, and its growing significance in today's world. Companies worldwide collect various forms of data available online, analyzing and tracking online behavior to provide personalized recommendations.

What are the various types of data collected by companies?

Companies collect various types of data on our online movements, both with and without our consent. This data can be classified into three categories: structured, unstructured, and semi-structured data.

Structured Data: This type of data can be represented in a tabular format, with rows and columns. For example, a transactional dataset that includes information such as the target account number, transaction time, and mode of transaction.
Unstructured Data: Data that does not have a specific structure, such as email text or datasets that include text, image, and audio samples.
Semi-structured Data: Data that has partial structure, but cannot be represented in a tabular format, such as data in XML or JSON files.

Understanding the different types of data and how to effectively analyze them is crucial for businesses to extract meaningful insights and make informed decisions.

How do companies use these datasets?

Companies use various techniques to analyze the data they collect, including:

Descriptive Analysis: Companies use visualization techniques such as bar plots and histograms to gain insights into their collected data.
Diagnostic Analysis: Companies analyze their data to identify and solve any problems they encounter. As seen in the case study of a milk farming company, they used monthly rating data to understand the cause of a decrease in average rating.
Predictive Analysis: Companies use Machine Learning models to analyze their data and predict future outcomes. For example, a milk farming company can use predictive analysis to estimate the percentage of new customers in the coming month and plan accordingly.
Prescriptive Analysis: It is an extension of predictive analysis, where data science not only predicts future outcomes but also suggests possible actions for a particular prediction. For example, an interview preparation company can use data science to grade an interview automatically and then offer suggestions for improvement.

Understanding these analysis techniques and how to apply them effectively is crucial for companies to extract meaningful insights and make data-driven decisions.

What does a Data scientist do?

A data scientist is responsible for analyzing various types of data, such as descriptive, diagnostic, predictive, and prescriptive analysis. The specific tasks that a data scientist performs will vary depending on the project they are working on. For example, in an autonomous vehicle company, a data scientist may be tasked with identifying and resolving vehicle faults, while another data scientist in the same company may focus on predicting the health of the battery.

Who can be a data scientist?

Data science is a rapidly expanding field within computer science, and one of the best aspects of this technology is that anyone with a curious mind can become a data scientist. The backgrounds of data scientists can be diverse, for example, a civil engineer could become a data scientist by analyzing data collected from construction sites and extracting insights from it. To become a successful data scientist, one should possess three key characteristics:

Curiosity: A desire to dive deep into the data and gain a thorough understanding of it.
Judgment: The ability to evaluate data and make informed decisions through the use of hypothesis testing.
Argumentative: The ability to defend and argue the findings and insights from the data analysis."

What Skills are required to become a Data Scientist?

As previously mentioned, individuals from various academic backgrounds can become data scientists. However, to become a professional in the field, certain technical skills are required, including:

Basic programming skills in languages such as Python, R, and SQL.
Knowledge of techniques for obtaining, storing, and manipulating data, such as web scraping, APIs, databases, and query writing.
Familiarity with big data technologies such as Hadoop, Spark, and cloud computing.
Understanding of tools and techniques for data pre-processing, analysis, and visualization.
Familiarity with machine learning concepts.

We will delve further into these topics and the importance of these technical requirements in a separate blog post.

How do businesses use data science for their benefit?

Data science plays a vital role in helping businesses, regardless of size, extract valuable insights from data and make informed decisions. Examples of this can be seen in everyday experiences such as search engines like Google suggesting what we will search for before we even finish typing our query, which improves user engagement and retention. This is made possible through the application of data science to the large amount of data that Google has collected. Similarly, food delivery service Zomato aims to deliver food within 10 minutes by analyzing data on past orders and pre-stocking nearby locations with the most popular items.

The benefits of data science can be grouped into three categories:

Resolving past issues: Through diagnostic analysis, data science can be used to identify and solve problems, as seen in the case study of a milk farming company where a data scientist resolved decreasing customer ratings.
Identifying future growth opportunities: By analyzing collected data, data science can reveal potential areas for business expansion, as seen in the film industry's increased use of VFX.
Making real-time decisions: Through analysis of real-time data, data scientists can make quick decisions to optimize resources and increase revenue, as seen in Uber's use of real-time congestion data to notify drivers of high-demand locations.

What are the processes involved in Data Science?

What are the various processes involved in Data Science project?

Data Science is a process of extracting insights and making informed decisions from large and complex data sets. The various stages of this process include:

Business Understanding: Data Scientists must have a clear understanding of the business problem they are trying to solve in order to formulate a hypothesis.
Data Collection: Once the business problem is understood, Data Scientists collect data to verify their hypothesis. Data can be obtained from existing sources or through methods such as web scraping.
Data Cleaning: Collected data requires cleaning before it can be analyzed. This includes handling missing values, removing duplicates, removing outliers, and changing the format of the data.
Data Analysis: Data Scientists use various strategies to analyze the data and extract valuable insights. This includes finding the mean and variance of data, correlation among features, and patterns in the data.
ML Modeling: For predictive and perspective analysis, data scientists use machine learning to build models. These models provide extra mathematical insights and predict future outcomes.
Providing Insights: Data Scientists deliver the results of the analysis to business analysts and work with them to convert the results into a meaningful form using visualization techniques. This helps business stakeholders make informed decisions.

How is Data Science different?

What is the difference between Data Science and Machine Learning?

Data Science and Machine Learning are closely related fields that work together to extract insights from large amounts of data. Data Science encompasses all aspects of analyzing and visualizing data, while Machine Learning specifically focuses on developing predictive models. Machine Learning Engineers focus on the technical side of creating and implementing these models, while Data Scientists use these models to perform predictive analysis and make strategic business decisions. Both are essential for understanding and utilizing the vast amounts of data that companies collect today.

What is the difference between a Data Scientist and a Data Analyst?

Data Analysis and Data Science are two distinct but related fields in the technology industry. Data analysts primarily focus on analyzing and interpreting existing data through techniques such as data visualization and statistical analysis. They provide insights and reports on the collected data. Meanwhile, Data Scientists take a more strategic approach by determining how data should be analyzed, stored, and manipulated in order to solve specific business problems. They also create advanced methods and models for data analysis, manipulation, and prediction using techniques such as machine learning and statistical modeling.

What is the difference between a Data Scientist and a Business Analyst?

Data science plays a crucial role in the IT industry, where business analysts collect and provide data to data scientists to solve specific business problems. Data scientists analyze the data and use machine learning techniques to build models that can provide valuable insights and predictions. These insights are then translated into actionable plans by business analysts, who present them to the stakeholders in a clear and easy-to-understand format. This process of data collection, analysis, and implementation helps companies make data-driven decisions that can optimize their operations and increase revenue.

What is the difference between a Data Scientist and a Data Engineer?

Data Engineering and Data Science are two distinct but related fields within the IT industry. Data Engineers focus on the infrastructure and systems that support the storage, collection, and manipulation of large data sets. This includes tasks such as designing and maintaining databases, creating data pipelines, and implementing data storage solutions in the cloud. On the other hand, Data Scientists use this infrastructure to analyze and extract insights from the data. They perform data analysis, manipulation, and use machine learning techniques to build solutions for business problems. Data Scientists often work closely with Data Engineers to ensure that the data is properly prepared and accessible for their analysis.

What are the challenges in the Data Science field?

Data Science is a rapidly growing field that offers exciting job opportunities, but it also comes with its fair share of challenges. As data scientists play a critical role in providing valuable insights and making business decisions, the job can be demanding. Some of the common challenges faced by data scientists include:

Understanding the business needs: Data scientists are tasked with breaking down complex business objectives to identify specific problem statements to solve. However, this can be challenging, especially when working with large teams where requirements may vary significantly. It is crucial for data scientists to have a strong understanding of business needs in order to effectively analyze and extract meaningful insights from data.
Multiple Data Sources: Data Science requires working with diverse data sources, which can come in various forms and structures. For example, a database may contain attributes of different data types, such as numeric and text. This requires data scientists to have the ability to handle different data types and apply appropriate pre-processing techniques to make the data usable for analysis and modeling.
Working with multiple teams: Data Scientists need to work closely with other teams such as Data engineers, data analysts, Machine Learning (ML) teams, and business analysts to effectively gather and combine data. Coordinating with multiple teams can be a challenge in the data science process.
Understanding what an outlier is and eliminating bias: Eliminating outliers in data can be a straightforward task, however, identifying and defining what constitutes an outlier can be challenging. This definition can vary depending on the specific problem being analyzed and the available data. Additionally, data can often contain biases which can negatively impact the performance of machine learning models. Data Scientists must accurately assess and address any potential biases in the data, making it a significant challenge in the field of data science.

What are the different technologies involved in Data Science?

Data Science is a rapidly growing field that encompasses various technologies. Some of the most commonly used technologies in Data Science include:

Artificial Intelligence and Machine Learning: These technologies are used to perform predictive and prescriptive analysis on data.
Cloud Computing: With multiple teams working on the same dataset, cloud computing allows for easy access and collaboration by providing a common platform for all teams to extract, transform, and load data.
Internet of Things (IoT): IoT refers to the technology that connects devices to the internet and generates large amounts of data that can be analyzed by data scientists to solve business problems.

Conclusion

Data Science is a rapidly growing field as the amount of data generated continues to increase, making the role of data scientists increasingly important. Businesses aim to extract valuable insights from this data, and this is where the skills of a data scientist come in. In this blog post, we provided an overview of Data Science, including a case study, the use of data by businesses, and what it takes to become a data scientist. In our next blog post, we will delve deeper into the steps to becoming a data scientist. We hope you found this article informative and enjoyable.

Introduction to Data Science for Beginners