Introduction to Data Science for Beginners

What is Data Science?

In the tech industry, data is often referred to as the "new oil" because of its valuable role in driving business growth. Companies collect vast amounts of data every day from their customers and users, but the question is: What is the purpose of all this data?

This is where data science comes into play. Data science is a field that extracts, processes, analyzes, and interprets data to extract insights, knowledge, and value from it. It combines techniques and methods from statistics, computer science, mathematics, and domain expertise to discover patterns, trends, and relationships hidden in complex and large datasets.

• There are several stages in the process of data science like data collection, data preprocessing, data analysis, and data visualization. It uses various tools and technologies like machine learning, artificial intelligence, deep learning, and big data technologies to extract insights and generate recommendations.
• Data science is used in various industries like finance, healthcare, retail, transportation, marketing, and social media. Its main goal is to improve decision-making processes, identify customer needs, predict future trends, and optimize business operations.

In this blog, we will discuss:

• Importance of data science
• 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?

Case Study to Understand the Importance of Data Science

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.

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.

Various Types of Data Collected by Companies

Companies collect various types of data online and this data can be classified into three categories: structured, unstructured, and semi-structured data.

1. 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.
2. Unstructured Data: Data that does not have a specific structure, such as email text or datasets that include text, image, and audio samples.
3. Semi-structured Data: Data that has partial structure, but cannot be represented in a tabular format, such as data in XML or JSON files.

How do Companies use these Datasets?

Companies use various techniques to analyze the data they collect:

1. Descriptive Analysis: Companies use visualization techniques such as bar plots and histograms to gain insights into their collected data.
2. 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.
3. 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.
4. 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.

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:

1. Curiosity: A desire to dive deep into the data and gain a thorough understanding of it.
2. Analytical Thinking: The ability to evaluate data and draw meaningful conclusions from it. They should be able to identify patterns and trends in the data and use statistical methods to test hypotheses and validate their findings.
3. Persuasiveness: The ability to effectively communicate the findings and insights derived from the data analysis. This involves being able to present the data and analysis in a clear, concise, and compelling manner, and to address any questions or concerns that may arise.

Skills 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:

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

Note: We will discuss the importance of these technical requirements in a separate blog.

How do businesses use data science for their benefit?

Examples of this can be seen in everyday experiences. For example, Google's search engine suggests what we may be searching for before we even finish typing our query. This is made possible through the application of data science to the vast amount of data that Google has collected.

In another example, the food delivery services analyze data on past orders to pre-stock nearby locations with the most popular items. This use of data science allows them to provide faster service to their customer.

So the benefits of data science can be grouped into three categories:

1. 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.
2. 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.
3. 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.

Processes involved in Data Science

1. Business Understanding: Data Scientists must have a clear understanding of the business problem they are trying to solve in order to formulate a hypothesis.
2. 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.
3. 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.
4. 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.
5. 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.
6. 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?

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.

Difference between Data Scientist and 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.

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.

Difference between Data Scientist and Business Analyst

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.

Difference between Data Scientist and Data Engineer

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.

Challenges in Data Science

Data Science is a rapidly growing field that offers exciting job opportunities, but it also comes with its fair share of challenges. Some of the common challenges faced by data scientists include:

1. 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.
2. 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.
3. 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.
4. 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.

Different technologies involved in Data Science

1. Artificial Intelligence and Machine Learning: These technologies are used to perform predictive and prescriptive analysis on data.
2. 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.
3. 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

In this blog, we have 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. We hope you found this article informative and enjoyable. If you have any queries, doubts, or feedback, please write to us at contact@enjoyalgorithms.com. Enjoy learning, data science!