FUNDAMENTALS OF DATA ANALYSIS
Jan-Jul 2026
Jan-Jul 2026
Lec 1: The data analytics lifecycle
Lec 2: Digital imaging, image transformations, image history
Lec 3: Image editing, Basics of text editing
Lec 4: Clustering concepts, K-means clustering, hierarchical clustering, applications of clustering
Lec 5: Bar charts, line charts, histograms, box plots, scatter plots, heatmaps,
Lec 6: Statistics on images, Image annotation
Lab 1: Data Imputation Notebook
Lab 2: Digital imaging, image transformations, image history
Lab 3: Image editing, Basics of text editing Notebook
Lab 4: Clustering concepts, K-means clustering, hierarchical clustering, applications of clustering Notebook
Lab 5: Bar charts, line charts, histograms, box plots, scatter plots, heatmaps Notebook
Lab 6: Statistics on images, Image annotation
Unit 1: Introduction to Data AnalyticsOverview of data analytics and its significance in various domains, Types of data: structured, unstructured, semistructured, Data analytics lifecycle: data collection, cleaning, analysis, interpretation, Case studies demonstrating the impact of data analytics.
Unit 2: Data Analysis TechniquesDescriptive statistics: measures of central tendency and variability, Inferential statistics: hypothesis testing, confidence intervals, Introduction to exploratory data analysis (EDA), Data visualization techniques and tools.
Unit 3: Introduction to Predictive AnalyticsBasics of regression analysis: linear and logistic regression, Overview of classification techniques, Time seriesanalysis and forecasting basics, Introduction to model validation and selection.
Unit 4: Data Analytics Tools and ApplicationsIntroduction to data analytics software: R, Python and its libraries, Data analytics in business decision-making, Ethical considerations in data analytics, Emerging trends in data analytics.
1. "Data Science for Business" by Foster Provost and Tom Fawcett
2. "Python for Data Analysis" by Wes McKinney
3. "Naked Statistics: Stripping the Dread from the Data" by Charles Wheelan