Data Analysis Fundamentals

Data Analysis

Definition

• Data Analysis is the process of extracting insights and patterns from data to inform decisions or answer questions.

Types of Data Analysis

• Descriptive Analytics: Examines historical data to understand what happened.
  • Examples: Summarizing data, creating reports, and calculating metrics.
• Diagnostic Analytics: Identifies the causes of a problem or event.
  • Examples: Analyzing data to identify correlations, causal relationships, and root causes.
• Predictive Analytics: Uses statistical models and machine learning to forecast what may happen.
  • Examples: Predicting customer churn, credit risk, and stock prices.
• Prescriptive Analytics: Recommends actions based on the analysis.
  • Examples: Recommending prices, inventory levels, and investment strategies.

Data Analysis Steps

1. Problem Definition: Identify the problem or question to be answered.
2. Data Collection: Gather relevant data from various sources.
3. Data Cleaning: Ensure data is accurate, complete, and consistent.
4. Data Transformation: Convert data into a suitable format for analysis.
5. Modeling: Apply statistical or machine learning techniques to analyze data.
6. Interpretation: Draw conclusions and identify insights from the analysis.
7. Communication: Present findings and recommendations to stakeholders.

Data Analysis Tools

• Spreadsheets: Microsoft Excel, Google Sheets
• Statistical Software: R, Python, SAS
• Data Visualization Tools: Tableau, Power BI, D3.js
• Machine Learning Libraries: scikit-learn, TensorFlow, PyTorch

Data Analysis Techniques

• Data Mining: Discovering patterns and relationships in large datasets.
• Regression Analysis: Modeling the relationship between variables.
• Hypothesis Testing: Testing hypotheses about population parameters.
• Cluster Analysis: Grouping similar observations or cases.
• Text Analysis: Extracting insights from unstructured text data.

Data Analysis

Definition

• Data Analysis is the process of extracting insights and patterns from data to inform decisions or answer questions.

Types of Data Analysis

• Descriptive Analytics: Examines historical data to understand what happened, including summarizing data, creating reports, and calculating metrics.
• Diagnostic Analytics: Identifies the causes of a problem or event, including analyzing data to identify correlations, causal relationships, and root causes.
• Predictive Analytics: Uses statistical models and machine learning to forecast what may happen, including predicting customer churn, credit risk, and stock prices.
• Prescriptive Analytics: Recommends actions based on the analysis, including recommending prices, inventory levels, and investment strategies.

Data Analysis Steps

• Problem Definition: Identify the problem or question to be answered.
• Data Collection: Gather relevant data from various sources.
• Data Cleaning: Ensure data is accurate, complete, and consistent.
• Data Transformation: Convert data into a suitable format for analysis.
• Modeling: Apply statistical or machine learning techniques to analyze data.
• Interpretation: Draw conclusions and identify insights from the analysis.
• Communication: Present findings and recommendations to stakeholders.

Data Analysis Tools

• Spreadsheets: Microsoft Excel, Google Sheets are used for data analysis.
• Statistical Software: R, Python, SAS are used for statistical analysis.
• Data Visualization Tools: Tableau, Power BI, D3.js are used to visualize data.
• Machine Learning Libraries: scikit-learn, TensorFlow, PyTorch are used for machine learning.

Data Analysis Techniques

• Data Mining: Discovering patterns and relationships in large datasets.
• Regression Analysis: Modeling the relationship between variables.
• Hypothesis Testing: Testing hypotheses about population parameters.
• Cluster Analysis: Grouping similar observations or cases.
• Text Analysis: Extracting insights from unstructured text data.