Introduction to Data Analytics

Data Analytics: The science of analyzing raw data to make conclusions about that information.

Descriptive Analytics
- Summarizes past data to understand trends.
- Utilizes statistical measures (mean, median, mode).
- Often presented through reports and dashboards.
Diagnostic Analytics
- Explains why past events occurred.
- Involves data mining and statistical analysis.
- Helps identify patterns and anomalies.
Predictive Analytics
- Forecasts future events based on historical data.
- Uses statistical models and machine learning techniques.
- Commonly applied in risk assessment and marketing strategies.
Prescriptive Analytics
- Suggests actions based on predictive analysis.
- Utilizes optimization and simulation algorithms.
- Aims to improve decision-making processes.

Data Collection
- Gathering data from various sources (surveys, databases, IoT devices).
- Ensuring data quality and relevance.
Data Cleaning
- Removing inaccuracies, duplicates, and irrelevant data.
- Standardizing data formats.
Data Analysis
- Applying statistical and computational techniques.
- Identifying trends, correlations, and insights.
Data Interpretation
- Translating analysis results into actionable insights.
- Communicating findings effectively.
Data Visualization
- Using charts, graphs, and dashboards to present data.
- Enhances understanding and facilitates decision-making.

Business Intelligence: Enhancing operational efficiency and strategic planning.
Healthcare: Improving patient outcomes and optimizing resource allocation.
Finance: Risk management and fraud detection.
Marketing: Customer segmentation and campaign management.

Descriptive Analytics:
- Summarizes historical data to identify trends using statistical measures like mean, median, and mode.
- Typically presented through reports and dashboards for clarity.
Diagnostic Analytics:
- Investigates the reasons behind past events through data mining and statistical techniques.
- Aids in detecting patterns and anomalies to better understand outcomes.
Predictive Analytics:
- Utilizes historical data to predict future events, employing statistical models and machine learning methods.
- Widely used in risk assessment and marketing strategies to forecast trends.
Prescriptive Analytics:
- Provides actionable recommendations based on predictive analytics.
- Employs optimization and simulation algorithms to enhance decision-making processes.

Data Collection:
- Involves collecting data from diverse sources, including surveys and IoT devices, while ensuring the data’s quality and relevance.
Data Cleaning:
- Focuses on removing inaccuracies, duplicates, and irrelevant information, as well as standardizing formats.
Data Analysis:
- Applies statistical and computational methods to identify trends, correlations, and insights within the data.
Data Interpretation:
- Converts analysis results into actionable insights, emphasizing effective communication of findings.
Data Visualization:
- Utilizes charts, graphs, and dashboards to visually represent data, thereby enhancing understanding and aiding in decision-making.

Data Analytics Software:
- Popular tools include R, Python, Excel, SAS, and Tableau for data analysis.
Big Data Technologies:
- Technologies such as Hadoop, Spark, and NoSQL databases enable processing of vast datasets.
Machine Learning Frameworks:
- Frameworks like TensorFlow, Scikit-learn, and Keras are essential for developing machine learning models.

Business Intelligence:
- Enhances operational efficiency and strategic planning through insightful data analysis.
Healthcare:
- Focuses on improving patient outcomes and optimizing resource allocation through data-driven insights.
Finance:
- Aids in managing risk and detecting fraud, improving financial decision-making.
Marketing:
- Enables customer segmentation and effective campaign management based on analytical insights.