Master Data Science with SQL and Python

Explore how probability underpins data science, with practical discussions of Bayes' theorem and posterior probability, and how to apply statistics like chi test and ANOVA in real data.

Explore inferential statistics by comparing with descriptive statistics, and master estimation and hypothesis testing using confidence intervals, p-values, null and alternative hypotheses, and tests like t, ANOVA, and chi-square.

Explore Python basics, including what Python is, how to install it, and how to use Anaconda and Jupyter notebooks for data science with Python.

Master Python syntax by tracing line structure, statements, indentation, and comments. Explore variables, strings, integers, floats, booleans, dynamic typing, and PEP 8 naming conventions like snake_case.

Explore data structures in Python, including lists, sets, tuples, and dictionaries, learning how each structure stores and accesses data, order, mutability, and operations like append, insert, remove, add, update.

Master Python data structures and string manipulation, including lists, sets, tuples, dictionaries, and replace, join, split, and reverse, then master arithmetic, assignment, logical, and identity operators.

Master Python control structures to manage program flow with while and for loops, break and continue. Learn about else branches and how modular, error-aware code improves readability.

Define Python functions as reusable blocks with def, parameters, and an optional return. Call them to perform tasks, show scope rules, default arguments, docstrings, and lambda alternatives.

Master Python file handling by distinguishing text and binary files, opening, reading, writing, appending, and managing paths, plus copying, renaming, deleting, and handling errors.

Learn to work with numpy arrays, perform elementwise operations, broadcast shapes, and apply linear algebra and random number generation for data science.

Learn Pandas essentials for data science, including series and data frames, reading and writing CSV and Excel, data manipulation, cleaning, transformation, time series, and basic analysis with numpy integration.

Master SciPy and Matplotlib essentials for data analysis in Python, covering optimize, stats, linalg, io, and plotting methods like line, scatter, histogram, bar plots, and subplots.

Learn to connect Python with MySQL by installing the MySQL connector, testing connections, and using a cursor to execute queries, then fetch results and convert them to a data frame.

Master Python with MySQL by creating databases and tables, establishing connections, and defining tables with rows and columns, including primary keys, using Python code and MySQL workbench.

Learn to perform core MySQL operations in Python, including describing table schema, altering tables, dropping tables, and inserting records using a Python MySQL connector.

Master data management with Python and MySQL by reading records, filtering with where, joining multiple tables, and handling transactions, including create, insert, update, and delete operations.

Explore the fundamentals of machine learning, including supervised and unsupervised learning, data preparation, features and labels, training and testing data, model fitting, evaluation metrics, and common algorithms with Python tools.

Explore supervised machine learning with classification and regression, training models on labeled data using scikit-learn, including decision trees, naive Bayes, and linear regression, with evaluation and deployment basics.

Explore supervised machine learning with logistic regression, support vector machines, and random forests, then master unsupervised clustering using k-means, mean shift, and hierarchical methods.

Explore an end-to-end data science capstone project in Python that applies machine learning to automate loan eligibility, featuring data preprocessing, feature engineering, classification with random forest, and segmentation using clustering.

An introduction to advanced machine learning concepts within a data science curriculum using SQL and Python to build predictive models.