Data Analysis Overview

Data Mining vs. Data Analysis

• Data mining focuses on discovering patterns and knowledge from large datasets.
• Data analysis focuses on interpreting data to answer specific questions or solve problems.

Data Mining vs. Data Profiling

• Data mining aims to find hidden patterns and relationships within data.
• Data profiling describes the characteristics of a dataset.

Good Data Model Indicators

• Accurate representation of the data relationships.
• Efficiency in querying and retrieving data.
• Scalability to accommodate future changes and growth in data volume

Data Analyst Soft Skills

• Communication skills (explaining analysis findings).
• Critical thinking (evaluating data sources).
• Problem-solving skills (identifying and addressing issues in the data).

Data Analyst Problems Encountered

• Incomplete or inaccurate data (making correct interpretation challenging)
• Data silos (information stored separately, preventing a holistic view of data).
• Lack of clear business questions (leading to irrelevant or unhelpful analysis).

KNN Imputation Method

• KNN (k-nearest neighbors) imputation substitutes missing values using the values from similar data points in the dataset.

Data Analysis Technical Tools

• (No specific tools were listed)*

Data Cleaning Best Practices

• Identifying and handling missing values.
• Correcting inconsistencies in data formats.
• Removing duplicate entries.
• Validating data for accuracy and completeness.

EDA (Exploratory Data Analysis)

• Exploratory data analysis involves analyzing data to summarize main characteristics of variables in a dataset.

Importance of EDA Skills

• Identifying patterns and anomalies (supporting hypotheses).
• Understanding data distribution.
• Data visualization to reveal insights (allowing comprehensive analysis).

Descriptive Analytics

• Descriptive analytics summarizes past data to understand what has happened.

Predictive Analytics

• Predictive analytics forecasts future outcomes based on historical data.

Prescriptive Analytics

• Prescriptive analytics recommends actions based on data insights.

Sampling Techniques

• Random sampling (every data point has an equal chance of selection).
• Stratified sampling (divides populations into subgroups for more representation).
• Cluster sampling (divides into clusters to select a sample from each).
• Systematic sampling (selecting samples at regular intervals)
• Convenience sampling (using easily accessible data).

Data Analysis Steps

• Data Collection: Gathering data relevant to the analysis goal.
• Data Cleaning: Handling missing data, errors, and inconsistencies.
• Data Transformation (or Interpretation): Converting data into suitable format for analysis.
• Modeling: Developing algorithms and models to analyze the data.
• Reporting: Presenting findings in a clear and concise manner to stakeholders.

Data Validation

• Verifying data accuracy and quality (ensuring correctness).

Data Validation Steps

• Data Accuracy Testing (Checking for correctness).
• Data Consistency Validation (Ensuring that data follows established rules and patterns).

Data Analyst Interview Questions & Answers

• Explain the main steps involved in data analysis. Collecting, cleaning, analyzing, modeling, and reporting on data.
• What is data validation? Ensuring the data is accurate, complete, and consistent by implementing checks.
• Explain the main steps involved in data validation. Screening and verifying data, including checks for inaccurate values.
• Explain what data cleansing is. Identifying and removing errors in data.
• Which skills are required to be a good data analyst? Technical skills such as data cleaning, visualization, programming languages, and soft skills like critical thinking and communication.

This quiz explores the differences between data mining and data analysis, as well as profiling methods. It also highlights key indicators of a good data model and essential soft skills for data analysts. Understand the common challenges faced by analysts in the field.