Fundamentals of Data Analytics
Types of Data Analytics:
- Descriptive Analytics:
- Definition: Focuses on summarizing and interpreting historical data to understand what has happened. It provides insights into past performance and trends using data aggregation and reporting techniques.
- Examples: Monthly sales reports, website traffic statistics, average customer satisfaction scores.
- Techniques: Data visualization, summary statistics (e.g., mean, median), dashboards.
- Diagnostic Analytics:
- Definition: Goes beyond descriptive analytics by digging deeper into the data to understand the reasons behind certain outcomes. It helps to identify the root cause of trends or anomalies.
- Examples: Analyzing why sales dropped in a specific region, identifying factors that caused high churn rates in a subscription service.
- Techniques: Drill-down analysis, data mining, correlation analysis.
- Predictive Analytics:
- Definition: Uses historical data and statistical algorithms to predict future outcomes and trends. It answers the question: “What is likely to happen?”
- Examples: Forecasting future sales, predicting customer behavior, risk assessment.
- Techniques: Regression analysis, time series analysis, machine learning algorithms.
- Prescriptive Analytics:
- Definition: Suggests possible actions or strategies based on predictive analytics. It not only predicts what will happen but also provides recommendations for the best course of action.
- Examples: Optimizing inventory levels based on predicted demand, recommending product pricing strategies.
- Techniques: Decision trees, optimization models, simulation.
Key Concepts:
- Data Points:
- Individual units of information or observations collected during analysis. A data point typically represents a single measurement or fact.
- Example: In a dataset of customer purchases, a single row (e.g., “Customer A bought product B for $50”) is a data point.
- Variables:
- Features or attributes that describe data. Variables can be either dependent (the outcome of interest) or independent (factors that influence the outcome).
- Types of Variables:
- Numerical Variables: Quantitative data (e.g., sales, temperatures).
- Categorical Variables: Qualitative data (e.g., gender, product type).
- Datasets:
- A collection of related data points organized in a structured format. Datasets can be large or small, and they typically contain multiple variables.
- Example: A dataset of customer transactions might include variables such as “Customer ID,” “Product Purchased,” “Amount Spent,” and “Purchase Date.”
- Metadata:
- Data that describes other data. Metadata provides context and additional information about the dataset, such as the structure, origin, or meaning of the data.
- Example: In a dataset, metadata might describe the meaning of each column, such as “The ‘Date’ column represents the date of the transaction.”
Commonly Used Terms and Jargon in Data Analysis:
- Algorithm: A step-by-step set of rules or instructions for solving a problem or performing a task, often used in data analysis for pattern recognition or prediction (e.g., machine learning algorithms).
- Anomaly Detection: The process of identifying unusual data points that do not conform to expected patterns or behaviors.
- Big Data: Extremely large datasets that are complex and challenging to process using traditional data management tools. Big data requires specialized technologies for storage, processing, and analysis (e.g., Hadoop, Spark).
- Correlation: A statistical measure that expresses the relationship between two variables. A positive correlation means that as one variable increases, so does the other, while a negative correlation means that as one increases, the other decreases.
- Data Mining: The process of discovering patterns, trends, and relationships in large datasets using techniques such as clustering, classification, and association rules.
- ETL (Extract, Transform, Load): A process used in data integration where data is extracted from source systems, transformed into a suitable format, and loaded into a destination system, often a data warehouse.
- KPI (Key Performance Indicator): A measurable value used to evaluate the success of an organization, process, or project in meeting specific objectives.
- Machine Learning: A branch of artificial intelligence (AI) where algorithms learn from data to make predictions or decisions without being explicitly programmed.
- Outlier: A data point that significantly deviates from the other data points in a dataset, which can indicate an error or an important insight.
- Sample: A subset of data taken from a larger dataset (population) used to make inferences about the population.