Python Crash Course: Part 4 - Understanding Data Types

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Are you ready to have more impact at work with analytics and data science powered by Python? This crash course is designed for ANY professional to learn the subset of Python needed to analyze data.
After completing this crash course series, you will have the foundational Python skills to take additional analytics, data science, and machine learning courses using Python.
Part 4 of this Python crash course introduces you to fundamental Python data types: ints, floats, strings, and booleans. These are the building blocks for analytics and data science using Python
☕ If you found this content useful and would like to support the channel, you can buy me a coffee: www.buymeacoffee.com/DaveOnData
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VIDEO CHAPTERS
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00:00 Intro
00:41 Data Table Columns Have Types
07:19 Integers
25:16 Floats
34:54 Strings
49:57 Booleans
59:27 Free Data Science Crash Courses
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FREE DATA SCIENCE CRASH COURSES
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If you're ready to see what Python skills make possible, be sure to check out my FREE on-demand data science crash courses!
Here's what you get with any of these courses:
1️⃣ On-Demand: Watch the course when it fits your schedule
2️⃣ PDF of all slides
3️⃣ All Python code as a Jupyter Notebook
4️⃣ All data so you can experiment
5️⃣ Resources to continue your learning.
Data science with Python. You've got this if you want it:
💻 Decision Trees with Python:
bit.ly/DecisionTreesWithPython
💻 Tuning Decision Trees with Python:
bit.ly/TuningDecisionTreesWit...
💻 Logistic Regression with Python:
bit.ly/LogisticRegressionWith...
💻 Cluster Analysis with Python:
bit.ly/ClusterAnalysisWithPython
#python #pythonprogramming #pythontutorial #pythonforbeginners #pythoncrashcourse #pythoncourse

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Опубликовано:

26 июн 2024

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Комментарии : 19

@DaveOnData 5 месяцев назад

-------------------------------------------------------------------------------------------- FREE DATA SCIENCE CRASH COURSES -------------------------------------------------------------------------------------------- If you're ready to see what Python skills make possible, be sure to check out my FREE on-demand data science crash courses! Here's what you get with any of these courses: 1 - On-Demand: Watch the course when it fits your schedule 2 - PDF of all slides 3 - All Python code as a Jupyter Notebook 4 - All data so you can experiment 5 - Resources to continue your learning. Data science with Python. You've got this if you want it: 💻 Decision Trees with Python: bit.ly/DecisionTreesWithPython 💻 Tuning Decision Trees with Python: bit.ly/TuningDecisionTreesWithPython 💻 Logistic Regression with Python: bit.ly/LogisticRegressionWithPython 💻 Cluster Analysis with Python: bit.ly/ClusterAnalysisWithPython

@beckyreese61 5 месяцев назад

Excellent course David. Clear, concise and understandable. Thank you for creating.

@DaveOnData 5 месяцев назад

You are most welcome! I am happy to hear that you are enjoying the crash course. More videos to come next week!

@abhig3235 5 месяцев назад

Thank you David for this excellent course. Truly appreciate your effort. I like that you first explain the fundamentals (concepts) and then illustrate with examples. Great work indeed!

@DaveOnData 5 месяцев назад

Thank you so much for taking the time to write such kind words! I am glad you are liking the tutorial.

@user-ob6kd1ix5r 5 месяцев назад

You rock. Going for a data annotation specialist job that requires some python. You literally saved me brother. Thank you.

@DaveOnData 5 месяцев назад

Outstanding! Two new videos will be published tomorrow.

@BicycleFriends 4 месяца назад

Thank you for these videos. They are super helpful.

@DaveOnData 4 месяца назад

You are welcome! I am glad you are enjoying the tutorial series.

@-Hussainnn Месяц назад

Nice I'm happy that C was my first language to learn because for Python I'm just learning the syntax honestly

@DaveOnData 29 дней назад

@-Hussainnn - Indeed! Learning the first programming language is always the hardest. I'm an old-school C++ guy, so I found Python easy to learn. Although, I think Python is kind of clunky compared to other options for data science (e.g., R).