Data Science and Analytics

Data science is an interdisciplinary field that employs scientific methods, processes, algorithms, and systems to extract information and insights from data in various structured and unstructured forms. Data analytics is the science that supports the processes of discovering useful information, drawing conclusions, and making decisions by examining, cleaning, transforming, and modeling data. Data analysts classify, analyze, store, and report data from organizational records using computer systems, enabling organizations to make accurate forward-looking decisions based on the obtained data. The processing of vast amounts of data generated by technological advancement and the proliferation of digital platforms is crucial for the future and efficiency of companies. In this context, data science aims to create value from data by integrating statistics, computer science, and domain knowledge.

Definition and Scope

Data science fundamentally manages the process of extracting useful information from data. This process can be governed by standards such as the Cross-Industry Standard Process for Data Mining (CRISP-DM). Data analysts examine all types of data, including financial records, to identify potential business needs and develop simple solutions to address them. In this process, the accurate and understandable reporting of data and the ability to derive conclusions through reasoning are essential.

Today, data science and analytics deal not only with traditional structured data but also with large and complex data sets (Big Data) from sources such as social media, websites, and smartphone applications. Unlike traditional media, new media environments generate continuous and multidirectional data flows. This has necessitated the development of new architectures and technologies for collecting, storing, and processing data.

Core Disciplines and Skills

Data science is a combination of numerous disciplines and skills. Success in this field requires both technical and communication competencies.

Statistics

Even in the era of Big Data, statistics remains a foundational pillar of data science. The increasing volume of data does not eliminate the need for statistical methods; on the contrary, it makes them even more critical. Statistics is not limited to sampling theory or hypothesis testing. It also encompasses critical processes such as data reduction, preparation, visualization, defining variables and relationships, conducting significance tests, and building models. Needs such as decision-making, probability calculation, benchmarking, and model construction will persist regardless of data volume. Therefore, statistics is a discipline present in almost every step of data science.

Programming Languages and Libraries

One of the most valuable programming languages for applied data science is Python. Python 3 has become the default version supported by the vast majority of libraries. One of the most essential libraries for data processing, manipulation, and analysis is Pandas. Pandas DataFrames serve as a standard input format for most machine learning libraries. In addition to Python, the R language is another important tool used by data scientists. For larger-scale projects, languages such as Java and C++ may also be required.

Database Management and Data Flow Tools

Although SQL has existed since the 1970s, it remains one of the most important skills for data scientists. Most organizations use relational databases as analytical data warehouses, and SQL is the standard tool for querying data from these databases. With the rise of unstructured data, NoSQL databases have gained importance as alternatives or complements to traditional systems. Open-source workflow management tools such as Apache Airflow are increasingly in demand for managing data pipelines (ETL) and machine learning workflows.

Software Engineering Principles

Moving machine learning models into production environments requires more than just analysis. A solid understanding of software engineering principles is essential in this process. Code must be clean, testable, and maintainable. In this context, adhering to Python style guides such as PEP 8, performing unit testing, using version control systems (e.g., Git and GitHub), and managing virtual environments with container technologies like Docker are fundamental skills required by a modern data scientist.

Data Visualization in Data Science

Data visualization is a critical tool for interpreting data and presenting it to decision-makers.

• Matplotlib is a fundamental Python module for creating 2D graphics.
• Seaborn adds aesthetic appeal and ease of use with statistical elements to visualization.
• Tableau has gained widespread adoption in enterprise settings by providing clickable dashboards and interactive reports.

These tools help users intuitively understand distributions, time series, and correlations within data. The performance of these libraries has been evaluated in various case studies in academic research.

MLOps: Model Monitoring and Versioning

MLOps is a systems engineering discipline aimed at ensuring the safe and reliable operation of machine learning models. Versioning practices provide traceability for code, data, and model artifacts. Three key components stand out:

• Model Registry – adds metadata and training parameters to models.
• Model Monitoring – detects performance degradation or shifts in data distribution.
• Versioning – enables rollback and comparative analysis between different model versions.

In addition, integrated MLOps systems with DevOps pipelines subject models to continuous training and deployment through CI/CD pipelines.