Watch Data Analysis with Python Beginner Tutorial Video Tutorial

Data Analysis with Python Beginner 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 have a basic foundation in Python (variables, data types, loops/conditionals), can work with lists/dictionaries/functions, and are comfortable importing and using libraries—because the course jumps straight into using tools like JupyterLab and data-analysis libraries to turn large, messy datasets into clear insights and better decisions. 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 how the data-analysis workflow comes together in Python—setting up and navigating JupyterLab, using helpful “magic commands,” and using pandas to load data into DataFrames (from dictionaries and CSV-style files), parse dates, define and order categories (like device health status), sanity-check data with summaries and descriptive stats, spot missing values/outliers, and then slice/filter data using labels, positions, and boolean masks (loc/iloc) to quickly answer questions (e.g., finding critical devices with high CPU usage). Start 0:00 Introduction 0:08 Establishing Datasets 4:47 Starting Jupyterlab and Using It 7:20 Using Split Windows and Magic Commands 13:10 Magic Commands and Guide 13:38 End Magic Commands 16:00 Pandas Dataframe Examples and Table 17:38 Reading a CSV and Creating a Dataframe 20:17 Creating a Dataframe and Printing the Dataframe Head 25:01 .info and Data Validation 29:25 Select dtypes and Describe 34:12 Categories and Count 39:36 .agg Function and Dates 43:40 .iloc[ ] and Indexing 45:39 Booleans and .loc With Categoricals 48:32 Create a Mask 1:01:50:20 Using a Mask 1:05:58:29 Conclusion 1:11:05:16 #pythontutorial #pythonforbeginners #pythontutorial (C) 2025 Bomberry Productions, LLC Any illegal reproduction of this content will result in immediate legal action.