Data Analysis with Python Advanced Tutorial

Data Analysis with Python Advanced Tutorial Get Ad-Free Training by becoming a member today! https://www.youtube.com/channel/UCqyBfm_H9ugGirk1ufYA2YA/join Join Learn Skills Daily for ad-free training, exams, certificates, and exclusive content: https://www.learnskillsdaily.com Jupyterlab Install: https://jupyter.org/install Seaborn: https://seaborn.pydata.org/ Pandas: https://pandas.pydata.org/ Exercise Files: https://tinyurl.com/53yy6xfj Who it's for: This training is for learners who already understand Python fundamentals and want to apply Python to real-world data analysis workflows. It’s especially useful for analysts, aspiring data analysts, and technically minded professionals who want to work with data from multiple sources (CSV, JSON, and databases), clean and reshape datasets, analyze trends over time, and take the first steps into predictive modeling using regression techniques. What it is: Python is the programming language you use to do data analysis efficiently—especially when you combine it with libraries like pandas (for working with datasets) and visualization tools like seaborn to manipulate, explore, and interpret data to discover real-world insights. In this course, you run that Python work inside JupyterLab, a web-based environment (the next iteration of Jupyter Notebook) that lets you work with notebooks alongside terminals and text editors, keep multiple projects organized in one place, and manage your analysis in a “notebook with multiple pages” style workflow. It runs through a Python kernel on a local server you launch (often after installing via pip install jupyterlab), and it includes productivity features like IPython “magic commands” (single-% for line magics and double-%% for cell magics) to help you navigate your workspace and measure or capture code What you'll learn: You will learn a practical, end-to-end data analysis lifecycle in Python: importing data from CSV, JSON, Stata files, and SQLite databases; cleaning data by selecting relevant fields, validating missing values, and handling outliers; engineering features with apply and lambda functions; combining datasets with merges/joins; reshaping data from wide to long format; summarizing data with grouping/aggregation and pivot tables; working with time series data through resampling, reindexing, rolling averages, and running totals; and building and evaluating basic predictive models using correlation and scikit-learn linear and multiple regression, including handling categorical variables with encoding. Start 0:00 Introduction 0:08 Pulling Data from a Database Part 1 4:24 Pulling Data From a Database Part 2 8:51 Simplified Dataframes and SettingWithCopyWarning 11:50 Simplified Dataframes and SettingWithCopyWarning Part 2 15:06 Apply 21:18 Lambda Functions 23:57 Merge Two Databases 26:39 Long Tables 33:26 Data Visualization for Long Tables 36:24 Pivot Table and DateTime Indexing 40:49 Date Range and Mean Daily CPU Usage 53:06 Cumulative Summation 58:00 SciKit Learn Introduction and SmallDataset Testing 1:05:57 Regplot and Cars Dataset Regression 1:12:55 GetDummies and Linear Regression Improvement Suggestions 1:14:50 Conclusion 1:15:57 #pythontutorial #dataanalysis #pythontutorial (C) 2025 Bomberry Productions, LLC Any illegal reproduction of this content will result in immediate legal action.