The process of gathering, examining, cleaning, transforming, and modeling data to discover useful information, draw conclusions, and support decision-making.
Key Components of Data Analytics:
- Data Collection: Gathering data from various sources, such as databases, spreadsheets, sensors, social media, etc.
- Data Cleaning: Removing or correcting errors, inconsistencies, and missing values to ensure the data is accurate and usable.
- Data Transformation: Converting data into a suitable format for analysis, which may involve normalization, aggregation, or encoding.
- Data Analysis: Applying statistical, mathematical, or machine learning techniques to analyze the data. This could include descriptive analysis (summarizing data), diagnostic analysis (identifying causes of a problem), predictive analysis (forecasting future trends), or prescriptive analysis (recommending actions).
- Data Visualization: Presenting data in visual formats such as charts, graphs, or dashboards to make the insights easier to understand and communicate.
- Interpretation and Decision-Making: Using the insights gained from the analysis to make decisions, optimize processes, or identify new opportunities.
Types of Data Analytics:
- Descriptive Analytics: Focuses on summarizing historical data to understand what has happened in the past.
- Diagnostic Analytics: Examines data to understand the reasons behind past outcomes.
- Predictive Analytics: Uses historical data and statistical models to predict future outcomes.
- Prescriptive Analytics: Recommends actions based on data analysis to achieve desired outcomes.
Tools and Technologies:
Data analytics can be performed using various tools and technologies, such as:
- Spreadsheets (e.g., Excel)
- Programming Languages (e.g., Python, R)
- Database Management Systems (e.g., SQL)
- Business Intelligence Tools (e.g., Tableau, Power BI)
- Big Data Technologies (e.g., Hadoop, Spark, Azure Data Factory)
- Machine Learning Frameworks (e.g., TensorFlow, Scikit-learn)
Available azure services:
- Azure Synapse Analytics
- Azure Blob Storage
- Azure Data Lake Storage
- Azure Data Lake Analytics
- Azure Analysis Services
- Azure HDInsight
- Azure Databricks
- Azure Machine Learning