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1. Python
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4. Data Science: R Basics
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5. Excel and PowerBI
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6. Data Science: Visualization
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8. R
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9. Tableau
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10. PowerBI
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11. Data Science: Productivity Tools
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12. Data Science: Probability
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13. Mathematics
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14. Statistics
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15. Data Visualization
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16. Machine Learning
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17. Deep Learning
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18. Data Science: Linear Regression
🔗https://pll.harvard.edu/course/data-science-linear-regression/2023-10

19. Data Science: Wrangling
🔗https://edx.org/learn/data-science/harvard-university-data-science-wrangling

20. Linear Algebra
🔗 https://pll.harvard.edu/course/data-analysis-life-sciences-2-introduction-linear-models-and-matrix-algebra

21. Probability
🔗 https://pll.harvard.edu/course/data-science-probability

22. Introduction to Linear Models and Matrix Algebra
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23. Data Science: Capstone
🔗 https://edx.org/learn/data-science/harvard-university-data-science-capstone

24. Data Analysis
🔗 https://pll.harvard.edu/course/data-analysis-life-sciences-4-high-dimensional-data-analysis

25. IBM Data Science Professional Certificate
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26. Neural Networks and Deep Learning
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27. Supervised Machine Learning: Regression and Classification
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When applying multiple filters to a series in Pandas, it's better to break the condition into several lines:

s = pd.Series([10, 15, 20, 25, 30])

s.loc[
    (s > 20) &
    (s % 2 == 1)
]

Such code is easier to read, write, and maintain. 🛠️✨

As a result, the value:
25
will be selected,
since it is both greater than 20 and an odd number. 🎯

#Pandas #Python #DataScience #Coding #Programming #DataAnalysis

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