IDSB: Introduction to Data Science for Beginners

Explore population and sample concepts, sampling types (random, stratified, cluster, systematic, convenience, and water sampling), and estimator properties including unbiased and biased estimators, efficiency, consistency, and sufficiency, with practical applications.

Explore descriptive statistics, including types of data (qualitative and quantitative), central tendency (mean, median, mode), percentiles and quartiles, interquartile range, boxplots, histograms, and data distribution.

Describe box plots and whiskers to visualize data distribution using the interquartile range, q1, q3, and median, identify outliers, and compare datasets; also explore histograms and skewness.

Explore frequency distribution and sampling distribution of the mean, linking population mean mu and standard error of the mean with relative and cumulative frequencies, histograms, and frequency polygons.

Formulate null and alternative hypotheses, apply hypothesis testing, and decide if observed results reflect a real effect. Explore confidence levels, confidence intervals, and how sample size narrows uncertainty.

Explore non rejection region and confidence intervals, and compare observed sample means to hypothesized means to decide about rejecting the null, using z, t, and chi-square tests.

Discover how hypothesis testing guides data-driven decisions across business, healthcare, manufacturing, social science, technology, and agriculture using t tests, ANOVA, z tests, chi-square, regression, and A/B tests.

Explore regression by modeling the x-y relationship with scatter plots and a regression line, assessing correlation and prediction error.

Fitting a regression line with the least squares method, minimize residuals, and interpret the line of best fit, then build models separating systematic from random components.

Learn how the coefficient of determination, r squared (rho square), measures how well the regression line explains variation in y, including SSR, SSE, and SST, with confidence intervals.

Forecasting uses time series decomposition to reveal trends, seasonality, and cycles, then applies smooth moving averages to predict future values.

Compare original time series with a smooth moving average to reveal underlying trends, reduce noise and seasonality, and clarify forecasting insights.

Learn forecasting methods from moving averages, regression models, and neural networks to simulate scenarios and forecast inventory management, meteorology, and financial markets, including oil demand.

Learn analysis of variance (ANOVA) to compare means across three or more groups, assess between-group and within-group variances, and apply the f statistic with a pooled variance.

Explore ANOVA by testing whether three population means are equal, using the f statistic to compare between-sample and within-sample variance, with real-world applications in marketing, manufacturing, healthcare, and agriculture.

Explore probability as a quantitative measure of uncertainty, with sample space and events, rules and axiom, and complementary events, illustrated through coin toss and dice examples.

Master set operations such as union, intersection, and complement, and learn probability rules. Explore conditional probability, Bayes theorem, multiplication principle, permutations, combinations, and the binomial distribution.

Apply the multiplication principle to count outcomes, distinguish permutation from combination for ordered versus unordered arrangements, and explore the binomial distribution alongside union, intersection, and complement formulas.

Explore probability distribution, including discrete and continuous types, the CDF, random variable outcomes, binomial and conditional probabilities, bias, and applications of conditional probability.

Explore discrete probability distributions, from Bernoulli and binomial to Poisson and geometric, mapping discrete outcomes to probabilities and applying them to real-world scenarios.

Explore continuous probability distributions, including normal, exponential, and uniform, and learn how areas under the curve determine probabilities for ranges of continuous random variables.

Explore binomial probabilities and the binomial distribution for fixed two-outcome trials, and learn how the normal distribution approximates it under the central limit theorem using mean and standard deviation.

Learn the standard normal distribution, its mean zero and unit variance, and how to standardize data with z-scores. Explore conditional probability and interpreting probabilities from contingency tables.

Apply conditional probability to update the likelihood of high economic growth given dollar appreciation, using priors (high 0.3, moderate 0.5, low 0.2) and conditional rates (0.7, 0.4, 0.2).

Explore data mining as the process of uncovering patterns and insights from data sets using machine learning, statistics, and techniques like clustering, classification, regression, association, rule mining, and anomaly detection.

Explore relational databases, data warehouses, and data marts, including SQL, normalization, and primary and foreign keys, and learn data mining techniques such as classification, clustering, regression, and association rule mining.

Explore major data mining methods—classification, prediction, frequent pattern mining, association, and cluster analysis—with common algorithms and real-world applications.

Explore association and frequent patterns in data mining, including apriori, support, confidence, and lift. Apply these rules to marketing, retail, and text mining for cross-sell and targeted campaigns.

Learn market basket analysis and association rules to identify products customers buy together, using support, confidence, and lift to determine if x implies y and optimize placement, promotions, and inventory.

Explore data structures for efficient data management, including data cubes and multidimensional models, with slicing, dicing, rolling up, and drilling down to support classification, clustering, and data mining in OLAP.

Learn how classification uses supervised learning to predict customer class labels from features like age, income, and spending, building models to distinguish high, medium, and low spenders.

classify safe or risky loan outcomes and will buy versus will not buy using credit score, income, loan amount, employment history, and explore clustering with k-means and hierarchical methods.

Explore cluster analysis in unsupervised learning to group data by similarity, use centroids for clusters, and apply linear discriminant analysis for classification, with fraud analytics and marketing for customer segment.

Data mining across marketing, manufacturing, healthcare, and financial services analyzes purchase data to target campaigns, predict responses, and boost cross-selling using decision trees, clustering, and association.

forecast patient enrollment and material demand in healthcare using historical data, arima, regression, neural networks, and bayesian probability; optimize inventory and resources, and assess financial risk with monte carlo simulations.

Explore machine learning techniques, including apriori algorithm and artificial neural networks, and learn how data, training, and evaluation drive supervised, unsupervised, and reinforcement learning.

Explore how artificial neural networks learn by breaking problems into subproblems across layers, using gradient descent and backpropagation to minimize cost, with architectures like feedforward, CNN, RBF, and deep learning.

Learn how decision trees serve as intuitive predictive models for classification and regression, using root and leaf nodes, feature-based splitting, and pruning to prevent overfitting, with Chad analysis.

Analyze decision trees, their limitations from overfitting and feature bias, and how pruning and thresholds including min samples, max depth, and features control complexity, with cart and chi square methods.

Leverage gradient boosting to assemble weak decision trees that iteratively correct errors for regression and classification, while mastering gradient descent, learning rate, and mini-batch variants to minimize loss.

Explore k-means clustering, an unsupervised algorithm that forms data-driven groups using centroids, distance metrics, and the elbow method to determine the optimal k.

Explore linear regression and logistic regression, two fundamental techniques for predicting continuous values and binary outcomes using the logistic function to estimate probabilities.

Explore the Naive Bayes classifier, a probabilistic model based on Bayes theorem with a conditional independence assumption. It excels in text classification, spam detection, and sentiment analysis.

Explore the nearest neighbor algorithm, a non-parametric lazy learner used for classification and regression, including k nearest, radius, and weighted variants with distance metrics like Euclidean, Manhattan, and cosine similarity.

Explore random forests, an ensemble of decision trees built with bootstrapping and a random subset of features, then aggregated to improve accuracy, reduce overfitting, and support classification and regression.

Explore how the support vector machine, a powerful supervised learning model, classifies data using linear or nonlinear boundaries, the kernel trick, and the concept of the margin and support vectors.

Explore genetic algorithms, fuzzy set optimization, linear programming, and simulation, and learn how fitness functions, selection, crossover, and mutation solve complex engineering and data science problems.

Explore fuzzy sets and fuzzy logic to model uncertainty with membership functions, linguistic variables, and if-then rules, and apply to control systems, data matching, and decision support.

Explore optimization and linear programming to maximize profit or minimize cost under constraints. Discover nonlinear, integer, and mixed-integer, network, and stochastic programming and key applications like production planning and timetabling.

Explore stimulation methods, including discrete event, continuous, agent-based, and Monte Carlo simulations, for modeling complex systems, generating synthetic data, and analyzing uncertainty in risk and decision making.

Learn how business intelligence tools analyze and visualize data to support decision making, using ETL, data warehouses, and BI platforms like Power BI, Tableau, and Looker.

Trace the evaluation and evolution of data science from its statistical roots in the 17th–18th centuries to a field, and examine how statistics and probability underpin models and machine learning.