Statistical Analysis and Data Processing Methods in Turkey

The Fundamental Stages of Data Processing

The data processing journey is generally examined in three main stages:

1. Data Collection (Data Acquisition)

This is the process of gathering data from various sources in line with the research objective.

• Data Sources:

  • Primary Sources: Surveys, interviews, field research, sensor data.

  • Secondary Sources: Statistics from government institutions (TÜİK), academic publications, corporate databases (CRM, ERP), social media data.

• Importance in Turkey: The challenges of natural language processing (NLP) for Turkish, restrictions on access to public data, or the integration of data from different formats require special attention at this stage.

2. Data Preprocessing

This is the most time-consuming and critical stage, where raw data is made suitable for analysis.

A. Data Cleaning

This is the process of correcting errors and inconsistencies in the dataset.

• Handling Missing Values (Missing Data Imputation): Filling empty cells using statistical methods like mean or median, or with prediction models, or by removing the relevant rows.

• Outlier Detection: Identifying and correcting or removing values that are significantly different from the rest of the dataset (e.g., observation errors, data entry mistakes) using methods like the IQR method or Z-score.

• Ensuring Data Consistency: Standardizing typographical errors, such as different spellings for the same information (e.g., “ist.” or “istnbul” instead of “İstanbul”).

B. Data Transformation

This involves changing the structure of the data to suit the analysis method.

• Normalization and Standardization: Bringing variables on different scales (e.g., salary and age) to a standard range (Min-Max Normalization, Z-Score Standardization). This is especially important for distance-based machine learning algorithms.

• Encoding Categorical Data: Converting textual or categorical data into a numerical format.

  • One-Hot Encoding: Creating a new binary (0 or 1) variable for each value of a categorical variable.

• Data Integration: Combining data from multiple sources into a single, consistent structure.

C. Data Reduction

This is the process of reducing the volume of data to decrease the complexity and computational load of the analysis.

• Dimensionality Reduction: Reducing the size of the dataset by removing negligible or redundant variables or by creating new, lower-dimensional structures (e.g., Principal Component Analysis – PCA).

• Sampling: Selecting a small subset from a large dataset that represents the main population.

3. Data Analysis & Interpretation

This stage involves applying statistical and algorithmic methods to the preprocessed data.

• Descriptive Analysis: Determining the basic features of the dataset, such as mean, median, and standard deviation.

• Relational Analysis: Examining the relationships between variables using methods like Correlation and Regression.

• Machine Learning: Applying algorithms for tasks like classification, clustering, and prediction.

Recommendations for Data Analysts in Turkey

To succeed in data projects in Turkey, it would be beneficial to focus on the following points:

1. Proficiency in Turkish Natural Language Processing (NLP): It is essential to learn specific libraries (Zemberek, Trankeyword) and techniques for processing Turkish text data (social media, customer feedback, etc.). Since Turkish is an agglutinative language, standard English tools cannot overcome challenges in tokenization and stemming.

2. Attention to Outliers: The rate of outliers and missing data can be high in Turkey due to manual data entry errors or the complexity of corporate systems. It is important to establish robust data cleaning protocols for these situations.

3. Legal and Ethical Awareness: It is mandatory to be conscious of regulations, especially the KVKK (Personal Data Protection Law), and to prioritize anonymization and security in data processing.

4. Mastery of Essential Tools: In Python, the Pandas and NumPy libraries are fundamental for data processing and manipulation. Knowing Matplotlib and Seaborn for data visualization helps in making sense of the processed data.

Data processing is the cornerstone of data science. Remember, the “Garbage In, Garbage Out” principle means that no matter how advanced your analysis method is, the lower the quality of your data, the less reliable your results will be.