What six problem types do data analysts typically work with? Option 1: Classification, Regression, Clustering, Anomaly Detection, Time Series Analysis, Natural Language Processing Option 2: Descriptive Statistics, Hypothesis Testing, Data Visualization, Correlation Analysis, Forecasting, Data Cleaning Option 3: Linear Algebra, Calculus, Probability Theory, Machine Learning, Database Management, Data Mining Option 4: Sampling Techniques, Inferential Statistics, Dimensionality Reduction, Data Preprocessing, Feature Engineering, Model Evaluation

The six problem types that data analysts typically work with are:

Option 1: Classification, Regression, Clustering, Anomaly Detection, Time Series Analysis, Natural Language Processing

These problem types encompass various techniques and methodologies that data analysts use to analyze and interpret data. Let's briefly explain each of these problem types:

1. Classification: In classification, data analysts categorize data into predefined classes or categories based on certain features or attributes. For example, classifying emails as spam or non-spam based on various characteristics.

2. Regression: Regression analysis helps data analysts understand the relationship between a dependent variable and one or more independent variables. It is used to predict or estimate numerical values based on historical data.

3. Clustering: Clustering involves grouping similar data points together based on their characteristics or similarities. It helps identify patterns and relationships within the data, without predefined classes or categories.

4. Anomaly Detection: Anomaly detection focuses on identifying unusual or abnormal data points or patterns. It is used to detect outliers or deviations from expected behavior.

5. Time Series Analysis: Time series analysis deals with data collected over time and focuses on understanding patterns, trends, and seasonality in the data. It is commonly used in forecasting and predicting future values.

6. Natural Language Processing (NLP): NLP involves analyzing and understanding human language data. It includes tasks like sentiment analysis, language translation, and text generation.

These problem types are not exhaustive, and data analysts may encounter other problem types as well. However, Option 1 provides a comprehensive list of problem types commonly addressed in data analysis.