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:

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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.

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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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