Jun 25, 2025
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Data Analytics is a cross-disciplinary subject that entails the extraction, transformation, analysis, and interpretation of data in order to inform decision-making and address problems.
It integrates aspects from a number of main fields:
1. Statistics and Mathematics
Essential for data analysis, trend identification, and forecasting.
Subjects: probability, statistical modeling, hypothesis testing, regression, etc.
2. Computer Science
Subjects: programming (Python, R, SQL), databases, algorithms, data structures, etc.
3. Business and Domain Knowledge
Knowledge of the particular industry or environment where data is being used is essential for insightful analysis.
Subjects: business processes, key performance indicators (KPIs), market trends, etc.
4. Information Technology
Subjects: cloud computing, data warehousing, big data technologies (e.g., Hadoop, Spark).
5. Machine Learning and Artificial Intelligence (Advanced)
Uses sophisticated algorithms to find patterns and make informed predictions or decisions based on data.
Typical Uses:
Business intelligence
Market analysis
Operations optimization
Healthcare analytics
Sports analytics
Financial modeling
Social media analysis
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While many people refer to the "4 Pillars of SAP" in their conversations, this concept usually relates to the foundation of SAP BTP