Advanced Python Data Analysis Course
5 Blocks · 14 Topics · 140+ Practice Questions
Master data acquisition, Python programming, SQL, statistical analysis, data modeling, and visualization with this advanced Python data analysis course.
Data collection & cleaning, Python programming, SQL queries, statistical analysis, Pandas & NumPy, machine learning basics, and data visualization with Matplotlib & Seaborn.
Web scraping, data pipelines, database connections, regression modeling, interactive visualizations, and data storytelling for technical and non-technical audiences.
Python 3, Pandas, NumPy, Matplotlib, Seaborn, scikit-learn, SQLite3, BeautifulSoup, requests, scipy, and statistics.
Surveys, interviews, web scraping, data aggregation from multiple sources, and storage solutions (warehouses, data lakes, cloud).
Structured vs unstructured data, handling missing values (MCAR/MAR/MNAR), normalization, scaling, encoding, and outlier detection.
Type, range, and cross-field validation methods. Establishing data integrity through validation rules and schema checks.
File formats (CSV, JSON, XML), web scraping with BeautifulSoup, data extraction from APIs, wide vs long formats, and train/test splitting.
Variables, scopes, data types, control structures, functions, data structures (lists, dicts, tuples, sets), PEP 8 and PEP 257.
Import styles, PIP package management, try/except/else/finally, common exceptions, and robust scripting practices.
Classes, constructors, encapsulation, composition, inheritance, polymorphism, and object identity/comparisons.
SELECT, JOINs, GROUP BY, CRUD operations, sqlite3, parameterized queries, SQL injection prevention, and type mapping.
Central tendency, spread, distributions (Gaussian, Uniform), Pearson's R, confidence measures, and interpreting plots.
Bootstrapping, sampling distributions, linear regression, logistic regression, model fitting, and coefficient interpretation.
DataFrames, Series, merging, reshaping, .loc/.iloc, NumPy arrays, broadcasting, groupby, and pivot tables.
Descriptive stats with Python, train/test splits, supervised learning, overfitting, bias-variance tradeoff, and model metrics.
Matplotlib & Seaborn: boxplots, histograms, scatterplots, lineplots, heatmaps, chart selection, labeling, and annotation.
Audience analysis, data narratives, presentation design, combining visuals with text, and evidence-based recommendations.