Python A-Z™: Python For Data Science With Real Exercises!
- Description
- Curriculum
- FAQ
- Reviews
Learn Python Programming by doing!
There are lots of Python courses and lectures out there. However, Python has a very steep learning curve and students often get overwhelmed. This course is different!
This course is truly step-by-step. In every new tutorial we build on what had already learned and move one extra step forward.
After every video you learn a new valuable concept that you can apply right away. And the best part is that you learn through live examples.
This training is packed with real-life analytical challenges which you will learn to solve. Some of these we will solve together, some you will have as homework exercises.
In summary, this course has been designed for all skill levels and even if you have no programming or statistical background you will be successful in this course!
I can’t wait to see you in class,
What you will learn:
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Learn the core principles of programming
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Learn how to create variables
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How to visualize data in Seaborn
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How to create histograms, KDE plots, violin plots and style your charts to perfection
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Learn about integer, float, logical, string and other types in Python
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Learn how to create a while() loop and a for() loop in Python
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And much more….
Sincerely,
Kirill Eremenko
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27Project Brief: Basketball Trends
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28Matrices
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29Building Your First Matrix
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30Dictionaries in Python
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31Matrix Operations
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32Your first visualization
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33Expanded Visualization
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34Creating Your First Function
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35Advanced Function Design
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36Basketball Insights
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37Section Recap
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38HOMEWORK: Basketball free throws
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39Matrices
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40Importing data into Python
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41Exploring your dataset
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42Renaming Columns of a Dataframe
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43Subsetting dataframes in Pandas
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44Basic operations with a Data Frame
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45Filtering a Data Frame
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46Using .at() and .iat() (advanced tutorial)
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47Introduction to Seaborn
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48Visualizing With Seaborn: Part 1
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49Keyword Arguments in Python (advanced tutorial)
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50Section Recap
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51HOMEWORK: World Trends
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52Data Frames
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53What is a Category data type?
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54Working with JointPlots
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55Histograms
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56Stacked histograms in Python
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57Creating a KDE Plot
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58Working with Subplots()
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59Violinplots vs Boxplots
Here you will see the difference between violinplots and boxplots, will know what they used for and what executives prefer in their analytics!
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60Creating a Facet Grid
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61Coordinates and Diagonals
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62EXTRA: Building Dashboards in Python
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63EXTRA: Styling Tips
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64EXTRA: Finishing Touches
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65Section Recap
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66HOMEWORK: Movie Domestic % Gross
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67Advanced Visualization
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68Homework Solution Section 2: Law Of Large Numbers
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69Homework Solution Section 3: Financial Statement Analysis (Part 1)
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70Homework Solution Section 3: Financial Statement Analysis (Part 2)
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71Homework Solution Section 4: Basketball Free Throws
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72Homework Solution Section 5: World Trends (Part 1)
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73Homework Solution Section 5: World Trends (Part 2)
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74Homework Solution Section 6: Movie Domestic % Gross (Part 1)
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75Homework Solution Section 6: Movie Domestic % Gross (Part 2)
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76THANK YOU Video