Data Analysis with Python Intermediate Tutorial
Data Analysis with Python Intermediate Tutorial
Get Ad-Free Training by becoming a member today!
/ @learnskillsdaily
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 is for learners who already know basic Python and pandas and are ready to do real analysis work—exploring a dataset, transforming it, and then communicating insights through clear visualizations (especially if you need to summarize findings quickly for a coworker or manager).
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’ll learn an end-to-end workflow for examining a dataset (shape, columns/rows, data types, quick stats), then building and interpreting practical visualizations in both pandas and seaborn—including line plots, histograms, box plots, scatterplots (with color-coded categories), and regression trend lines—while also covering more advanced seaborn techniques like facet grids for “split-by” views, categorical plots (count plots, violin plots), distribution smoothing (KDE), and the finishing steps that make charts presentation-ready (labels/titles, style/context tweaks, and saving figures with filename choices and DPI). Along the way, you’ll reinforce key analysis judgment calls like correlation vs. causation and what your charts are truly saying.
Start 0:00
Introduction 0:08
Read CSV and .sort_index() 2:33
WS-001 Plot and How to Read a Plot 7:08
Histogram and Box Plot 11:04
Seaborn Introduction and Scatter Plots in Seaborn and Pandas 16:59
Scatter Plot and Regression Line in Seaborn 20:45
Mean and Bar Plot 24:10
Seaborn Barplot and Using Mean as an Estimator 29:35
Seaborn Relational Plot 30:09
Frequency of Categoricals and ViolinPlot 32:50
ViolinPlot and Histogram with Trend Line 39:30
Conclusion 45:23
#pythontutorial #dataanalysis #pythontutorial
(C) 2025 Bomberry Productions, LLC
Any illegal reproduction of this content will result in immediate legal action.
Nesta página do site você pode assistir ao vídeo on-line Data Analysis with Python Intermediate Tutorial duração hora minuto segundo em boa qualidade , que foi baixado pelo usuário Learn Skills Daily 01 Janeiro 1970, compartilhe o link com seus amigos e conhecidos, no youtube este vídeo já foi visto 2,441 vezes e gostou 80 espectadores. Boa visualização!
