Data Science Mastery with Python: Comprehensive course

Master the basics of statistics: data collection, organization, summarization, and analysis to draw conclusions. Explore primary and secondary data and types such as quantitative and qualitative.

Explore why data quality matters for reliable insights, covering inconsistency, missing values, duplication, correctness, and timeliness, and show how sample statistics infer population behavior with a drive-thru example.

Explore descriptive statistics, including measures of central tendency, spread, and shape, and learn how mean, median, and mode summarize data and reveal outliers.

Explore measures of spread—range, interquartile range, variance, and standard deviation—to assess data variability, identify outliers, and monitor control systems.

Explore measures of shape to analyze the distribution's symmetry, left and right skewness, and the normal distribution; compare mean, median, and mode, and assess outliers with practical examples.

Understand how data visualization reveals patterns and insights. Learn how histograms, scatterplots, pie charts, and box plots illustrate distributions, outliers, and relationships using salary and education examples.

Explore inferential statistics by understanding sampling, population, and data spread; apply probability, hypothesis testing, and confidence intervals to make data-driven predictions in decision making.

Explore the core concept of probability, estimating event likelihood from prior information, with examples on rain, stock trends, cricket match outcomes, loan decisions, and independent versus dependent events.

Explore conditional probability by analyzing the probability of a car being stolen given its attributes (color, type, origin). Apply this concept to real-world tasks like spam detection and facial recognition.

Explore random variables as numeric outcomes of random phenomena, examine coin tosses, stock, and housing sales examples, and apply the expected value formula as a probability-weighted average.

Explore the normal probability distribution and its bell curve, standardizing data, with mean, standard deviation, and z-scores. Apply these concepts to real data like luggage loading times and flight bookings.

The central limit theorem shows that sample means approach a normal distribution as sample size grows, regardless of population. Taking many samples and averaging them standardizes data for machine learning.

Explore hypothesis testing for decision making by examining null and alternative hypotheses, sufficient evidence, p-values, and real-world examples from drug trials, court rulings, manufacturing, and baseball.

Explore the open source Anaconda data science toolkit, manage packages and virtual environments, and set up your development workspace. Launch Jupyter Notebook locally to work with notebooks.

Explore Google Colab, a free platform for Python, deep learning, and machine learning, with easy notebook creation and text or code cells.

Learn Python basics by exploring its syntax, structure, and logic. Discover reserved keywords and operators, import packages, and use indentation to write clear, real world Python code step by step.

Discover Python identifiers and how to name variables, functions, and classes using letters, digits, and underscores. Follow rules: never start with a digit, never use keywords, and avoid special symbols.

Learn to add clear Python comments with hash for single lines and triple quotes for multi-line notes, and use docstrings to document functions.

Explore how Python uses indentation to define code blocks with four spaces. Learn to write readable, error-free code and recognize indentation rules in loops and ranges.

Explore Python statements, including assignment statements, single-line and multi-line forms, and the continuation character or brackets to join lines.

Understand how Python stores values in variables using identifiers and assignment operator. See integers, decimals, and strings, plus multiple assignment, and note that different names can share the same memory.

Explore Python data types and objects, including integers, floats, complex numbers, booleans, and strings, and learn about lists, tuples, sets, and dictionaries and their basic operations.

Explore data type conversion in Python by using int, float, and str to convert numbers, strings, and lists, and learn how to handle invalid conversions and edge cases.

Learn how Python input-output functions display user input on the screen, and how changing values and spaces affects the printed output.

Master output formatting in Python by formatting strings, printing with proper spacing and line breaks, and exploring practical examples that demonstrate clean, readable console output.

Explore obtaining data from users with Python's input function, see how inputs are captured, and apply typecast to convert values (for example to integers) for calculations.

Learn how Python operators manipulate values, including arithmetic, assignment, comparison, logical, identity, and membership operators, with practical examples on lists, strings, and dictionaries.

Explore Python control flow, including if-elif-else logic, nesting, logical operators, and looping with while and for, plus range usage, break and continue.

Explore functions in Python, including types, arguments, recursive and lambda functions, with examples of def syntax, docstrings, return statements, and global versus local scope and factorial.

Learn two types of functions in python: built-in and user defined, through abs, all, dir, enumerate, divmod, map, reduce, and isinstance, with hands-on examples.

Explore recursive functions in Python, where a function calls itself until a base condition stops execution; learn factorial as an example and discuss benefits and memory and time trade-offs.

Learn how lambda anonymous functions enable one-line Python code for operations like squaring numbers and aggregating lists with map, filter, and reduce.

Master numpy operations for data science: create and manipulate arrays, initialize with random data, index, broadcast, reshape, and merge or split arrays while computing min, max, mean, and sqrt.

Explore pandas data frames and series, loading data from csv json and excel, indexing and selecting columns, merging, concatenating, grouping, and time-series operations.

Learn data visualization in Python by installing key packages like pandas and seaborn, and build bar, line, histogram, pie and donut charts from McDonald's nutrition and store data.

Master bivariate plotting to uncover relationships between two variables using scatterplots and linear regression. Interpret positive, negative, and neutral correlations and visualize them with heatmaps and KDE.

Explore multivariate plotting techniques for categorical and numerical data, using scatter plots, grouped box plots, heat maps, and parallel coordinates, with data standardization, normalization, and interactive visualization options.

Explore data science with EDA: define the business problem, clean and preprocess data, perform univariate analysis and profiling, and derive actionable insights from numerical and categorical datasets.

Conduct end-to-end exploratory data analysis on a McDonald's data set, including problem framing, data profiling, preprocessing, univariate and multivariate analyses, and nutrition-driven insights for revenue and menu decisions.

Explore data profiling, data types, normalization, missing values, outliers, grouping, and correlation to uncover actionable insights from a mental health survey dataset using Python.

Learn the fundamentals of machine learning within data science, from data collection and cleaning to building models and making future predictions using supervised, unsupervised, semi-supervised, and reinforcement learning.

Explore unsupervised learning, discovering patterns in data without labeled outputs. Learn clustering to group similar data and association to reveal co-occurring items, illustrated with customer segments and product pairings.

Explore reinforcement learning and related paradigms—supervised, online, and model-based learning—through practical Python-focused examples. See how agents learn from actions and rewards and apply machine learning concepts to real-world tasks.

Explains linear regression as a predictive model linking a numeric dependent variable to independent variables, with examples like temperature and ice cream sales, and introduces least-squares fitting and evaluation metrics.

Learn to use linear regression to predict sales from tv, radio, and newspaper ads, with data preparation, assumptions, standardization, train-test split, and MAE, MSE, RMSE, and R-squared evaluation.

Learn logistic regression for binary and multiclass classification, using the sigmoid function to model probabilities, compare with linear models, and apply to spam, images, and fraud detection.

Preprocess Titanic data with missing-value handling and feature engineering (family size, gender class), then train a logistic regression model and evaluate with accuracy and confusion matrix.

Explore decision trees, their types, algorithms, and applications, with examples of branches, decision nodes, and yes/no splits for categorical and continuous targets.

Explore how decision trees use entropy and information gain to select features, build branches, and predict outcomes, illustrated with weather and golf examples.

Explore how the Gini index measures impurity in decision trees, compare it with entropy, and learn to select splits like outlook and humidity to build pure classifications.

Identify issues in decision trees, such as underfitting and overfitting, and how training data accuracy, bias, and variance influence splitting, leaf nodes, and pruning.

Explore how decision trees predict customer churn, support retention, fraud detection, and credit risk scoring. See how amount, transactions per day, area code, and age form leaf node rules.

Apply decision trees to Titanic dataset, handle missing values, create a child feature from age, and use grid search on entropy and Gini to boost accuracy from 0.77 to 0.8.

Describe random forest as an ensemble learning method with multiple decision trees trained on random data subsets and random feature selections, combined by averaging for classification and regression.

Explore how a random forest uses multiple decision trees to tackle classification and regression tasks, employing majority voting for classification and averaging for regression.

Understand why random forest uses an ensemble of decision trees to reduce overfitting and achieve high accuracy, even with missing data, for classification and regression.

Explore how random forest uses multiple decision trees to identify loyal and fraud customers in banking, with references to C4.5, information entropy, and pruning low-importance branches.

Explore random forest implementation on the Titanic dataset, including preprocessing, feature engineering, and model tuning with grid and randomized searches to boost accuracy.

Explore regression model evaluation by comparing mean absolute error, mean squared error, and root mean squared error to assess future prediction accuracy and outlier impact.

Learn how r-squared measures the proportion of variance explained by a regression model and how adjusted r-squared penalizes complexity when selecting predictors.

Apply linear regression to predict sales from tv, radio, and newspaper budgets and compare models using MAE, MSE, RMSE, R-squared, and adjusted R-squared on training and test data.

Evaluate classification performance using accuracy, confusion matrix, precision and recall, and learn when accuracy fails on imbalanced data through the classification report.

Learn how a confusion matrix summarizes classification results with true/false positives and negatives, and balance false positives and false negatives for tasks like medical screening or spam filtering.

Explore recall, the sensitivity of a classifier, by examining true positives and false negatives in the confusion matrix. Learn how recall relates to precision and positive predictions.

Balance precision and recall with the F1 score, a harmonic mean reflecting both metrics for your business case, and explore specificity and false positive rate.

Explore the AUC/ROC curve and its use in evaluating classification models, including sensitivity, specificity, thresholds, and the distinction between true and false positives, illustrated with a credit default example.

Learn how to build a credit card transaction model with random forest, address data imbalance, and evaluate with precision, recall, and AUC to minimize false negatives.

Practice data analysis with R by installing RStudio on Windows, navigating its interface, and performing basic computations like eight plus five.

Explore the scripted window in the data analysis environment, learn to write and run code, print outputs like Hello World, manage variables and data sets, and connect to data sources.

Explore the history, advantages, and core capabilities of R as a free, cross-platform programming language for statistical computing and graphics, supported by thousands of packages and rich visualizations.

Explore big data concepts, learn the five Vs: volume, variety, value, velocity, veracity, and how Hadoop, MapReduce, and data warehouses enable storage, processing, and insights for data science.

Learn how Hadoop stores and processes large datasets in parallel and distributed fashion through HDFS and MapReduce, and explore the YARN architecture with NameNode, DataNode, and ResourceManager.

Explore data visualization with Tableau to create interactive dashboards and visual analytics that drive storytelling, enable instant insights, and support fast, data-driven business decisions across structured and unstructured data.

Explore how business analytics transforms data into wisdom through the lifecycle from data collection to decision making, and master descriptive, diagnostic, predictive, and prescriptive methods.

Kick off the data science lifecycle with data abstraction by installing and configuring MongoDB (local or Atlas), connecting via Python, and exploring telecom data as document-based collections.

Install my school and its shell. Connect to localhost with your username and password, then create a project database and tables, run sql queries with pandas, and export results.

This project guides you through data preprocessing for churn analysis, including loading data, handling missing values, encoding categories, exploring distribution and correlations, and preparing features for modeling.

Import libraries, split data into train and test sets, compare logistic regression, decision tree, and random forest using cross-validation scores, grid search, and evaluate with confusion matrix and classification report.

This project guides you through deploying a churn-prediction model with Flask, linking frontend and backend, using a virtual environment, and testing with a live app link.