Data Analytics

INTRODUCTION TO

DATA ANALYTICS

WHAT IS DATA ANALYTICS? Data Analytics is the science of analyzing raw data to draw meaningful conclusions and support decisionmaking.

IMPORTANCE OF DATA ANALYTICS Informed Decision Making: Helps organizations make smarter decisions. Competitive Advantage: Leverages data to stay ahead in the market. Optimizing Performance: Identifies inefficiencies and areas for improvement. Customer Insights: Personalizes customer experience and marketing efforts.

TYPES OF DATA ANALYTICS Descriptive Analytics Diagnostic Analytics Predictive Analytics Prescriptive Analytics

KEY STEPS IN DATA ANALYTICS PROCESS

1. Data Collection

4. Data Analysis

2. Data Cleaning

5. Visualization

3. Data Exploration

6. Decision Making

TOOLS USED IN DATA ANALYTICS Excel: For basic data analysis and visualization. SQL: For querying and managing databases. Python & R: For advanced statistical analysis and data modeling. Tableau & Power BI: For creating interactive visualizations and dashboards. Hadoop & Spark: For handling big data.

REAL-WORLD APPLICATIONS OF DATA ANALYTICS Healthcare: Predicting disease outbreaks, improving patient care. Finance: Fraud detection, risk management, stock market predictions. Retail: Personalized recommendations, customer behavior analysis. Sports: Player performance analysis, strategy optimization. Manufacturing: Supply chain optimization, predictive maintenance.

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