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AD-404 DATA SCIENCE Notes

5 notes for DATA SCIENCE, unit by unit.

Unit 1 · Short notes Introduction to Data Science Types of Data: structured and unstructured data · Data Science Road Map: Frame the Problem; Understand the Data; Data Wrangling; Exploratory Analysis; Extract Features; Model and Deploy Code · Graphical Summaries of Data: Pie Chart, Bar Graph, Pareto Chart, Histogram · Measures of central tendency of Quantitative Data: Mean, Median, Mode · Measures of Variability of Quantitative Data: Range, Standard Deviation and Variance · Probability: Introduction to Probability, Conditional Probability
Unit 2 · Short notes Unstructured Data Analytics Importance of Unstructured Data · Unstructured Data Analytics: Descriptive, diagnostic, predictive and prescriptive data analytics based on case study · Data Visualization: boxplots, histograms, scatterplots, features map visualization, t-SNE · Overview of Advance Excel: Introduction, Data validation, Introduction to charts, pivot table, Scenario manager, Protecting data, Excel macro basics, Introduction to macros
Unit 3 · Short notes Statistical & Probabilistic analysis Statistical & Probabilistic analysis of Data · Multiple hypothesis testing · Parameter Estimation methods · Confidence intervals · Correlation & Regression analysis, logistic regression · Shrinkage Methods, Lasso Regression, Bayesian statistics
Unit 4 · Short notes Data Manipulation With Pandas Introduction to Pandas: understanding DataFrame, handling missing values, data operations, string manipulation, regular expressions, data cleaning, outlier detection · Visualization tools in Python: representation of pie chart, bar chart, histogram, scatterplots · Data analysis performance metrics: ROC curve, types of errors, overfitting & underfitting, model evaluation techniques (holdout, random sampling, cross‑validation, bootstrap) · Ensemble methods: bagging & boosting, gradient boosting, random forests, committee machines
Unit 5 · Short notes Introduction to Business Intelligence Introduction to Business Intelligence, types of Business Intelligence, modern Business Intelligence tools · Data Science and Ethical Issues: unfair discrimination, reinforcing human biases, lack of transparency · Discussions on privacy, security, ethics, and the role of next‑generation data scientists

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