DATA
COLLECTION
Gathering data from various sources such as databases, sensors, surveys, and logs.
DATA
CLEANING
Removing or correcting errors, inconsistencies, and duplicates in the data to ensure accuracy.
DATA
TRANSFORMATION
Converting data into a suitable format or structure for analysis, which may involve normalization, aggregation, and other preprocessing steps.
DATA
VISUALIZATION
Presenting data in graphical or pictorial formats like charts, graphs, and dashboards to make insights more accessible and understandable.
DATA
INTERPRETATION
Drawing conclusions from the analyzed data and making informed decisions based on the findings.
DATA ANALYSIS: Using statistical, mathematical, and computational techniques to explore and interpret data. Common methods include:
- DESCRIPTIVE ANALYTICS: focuses on summarizing historical data to understand what has happened in the past. It uses techniques such as data aggregation and data mining to provide insights into past events.
- DIAGNOSTIC ANALYTICS: goes a step further by examining data to understand why something happened. It uses techniques like drill-down, data discovery, data mining, and correlations to uncover the root causes of events.
- PREDICTIVE ANALYTICS: uses statistical models and machine learning techniques to forecast future outcomes based on historical data. It helps in identifying future risks and opportunities, allowing organizations to make proactive decisions.
- PRESCRIPTIVE ANALYTICS: provides recommendations on possible actions to achieve desired outcomes. It uses optimization and simulation algorithms to advise on the best course of action given the predicted future scenarios.
TOOLS AND TECHNOLOGIES
Utilizing software and tools such as MS Excel, R, Python, SAP, SQL, Tableau, Power BI, and machine learning libraries to perform data analysis.
