Data Analytics
COURSE OVERVIEW
This course provides a comprehensive introduction to data analytics, equipping students with the essential skills and knowledge to extract valuable insights from data. The curriculum covers a wide range of topics, from data collection and cleaning to advanced statistical analysis and data visualization.
SYLLABUS SUMMARY
Data Analytics
Introduction to Data Analytics
- Definition of data analytics
- Importance of data analytics in today’s world
- Data analytics lifecycle
- Ethical considerations in data analytics
Data Collection and Cleaning
- Data sources (primary and secondary)
- Data collection methods (surveys, interviews, observations)
- Data quality issues and challenges
- Data cleaning techniques (missing values, outliers, inconsistencies)
Data Exploration and Visualization
- Descriptive statistics (mean, median, mode, standard deviation)
- Data visualization techniques (charts, graphs, dashboards)
- Exploratory data analysis (EDA) using statistical tools.
- Storytelling with data
Statistical Analysis
- Hypothesis testing (t-test, ANOVA, chi-square)
- Correlation and regression analysis
- Time series analysis
- Dimensionality reduction techniques (PCA, t-SNE)
Data Mining and Machine Learning
- Introduction to data mining
- Supervised learning (classification, regression)
- Unsupervised learning (clustering, association rules)
- Model evaluation and validation
Big Data Analytics
- Introduction to big data
- Big data technologies (Hadoop, Spark, NoSQL)
- Big data analytics techniques
- Case studies in big data applications
- Data visualization techniques (charts, graphs, dashboards)
- Exploratory data analysis (EDA) using statistical tools.
- Storytelling with data
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