Foundation of Data Science
68 videos • 312 views • by Fatemeh Yarahmadi
1
Foundation of Data Science- import pandas as pd
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2
Foundation of Data Science- Raw Data Reading using .read_csv() method
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3
Foundation of Data Science. Make a Copy of your Original Data Using .copy()
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4
Foundation of Data Science- Observe all rows and columns in Data Frame .options.display.max_
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5
Foundation of Data Science- Find Any Missing Values Using .info()
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6
Foundation of Data Science- df.isnull().sum() for the number of missing values
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7
Foundation of Data Science- Independent and Dependent Variables
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8
Foundation of Data Science- Drop a column .drop(['name of column'], axis=1)
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9
Make removing a column permanent in the data frame df=df.drop(['column name], axis=1)
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10
Foundation of Data Science. MNIST Dataset Overview
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11
Foundation of Data Science- MNIST- Single image visualization using plt.imshow
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Foundation of Data Science- Tensor Slicing Operation
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13
Foundation of Data Science- MNIST- Reshape the Data
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14
Foundation of Data Science. MNIST to_categorical
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15
Foundation of Data Science. MNIST- Sequential and Layers
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16
Why Linear Algebra?
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17
What is data science?
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Why Data Science?
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19
[Private video]
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20
Cause and Effect: Introduction to Dot Plot
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21
DS Terminology: Dependent and Independent Variables
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22
DS Terminology: Association Between Variables
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23
Association vs Cause-and-Effect vs Correlation
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24
Why Python? What is a Python Library?
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25
Download Anaconda for Python
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26
Checkout Jupyter Notebook!
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27
Python Arithmetic Operations: Including Floor Division
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28
What are Python Objects: Numbers including Integers and Decimals (floating point numbers)
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29
Python Mod Operator % Returns The Remainder
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30
Python Double Asterisk or Raise to Some Powers or Exponents
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31
Python Simple Variable Assignment
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32
Python “+=” operator, which is shorthand for saying “increase a number by some amount”
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33
Python “-=” operator, which is shorthand for saying decrease a number by some amount”
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34
What are Python Strings?
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35
Python String Using Single Quotes and Apostrophe: Syntax Error: unterminated string literal
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36
Python Length Function Error Analysis len(1) vs len('1') or len("1")
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37
Python Indexing Definition: It's like counting elements from the beginning starting with zero
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38
Python Reverse Indexing: The last element is indexed as -1, the second-to-last as -2, and so on.
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39
Python Slicing sequence[start:stop:step]. Step indicates how many elements to skip between
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40
List vs Array. NumPy and np.mean() to Calculate The Mean of Coffee Consumption Per Week
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41
Data Science Workshop: Overview of DS_ML_AI Concepts, Techniques, and Applications
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42
Pandas and Matplotlib Brief Introduction
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43
Create pandas DataFrame from a Table
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44
Bar Chart with Python. Set Horizontal Alignment To Avoid Labels Overlap
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45
From Statistics Histogram to Matplotlib Python Histogram
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46
Foundation of DS .describe() Method to Find Descriptive Statistics
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47
Finding The Mean of Discrete Probability Distribution
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48
Calculate Probability Using the norm sub-library of scipy.stats
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49
Calculate Probability Using the norm.cdf and norm.sf survival function of scipy.stats
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50
Confidence Interval for the Mean Basics (point estimate, confidence level, critical value)
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51
[Private video]
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52
CI for Mean when sigma is missing (Student T-Distribution)
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53
Basics of Hypothesis Testing
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54
Type I and Type II Error in Salmonella Contamination Case
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55
Hypothesis Testing for Mean. Pit Stop Example using P-Value
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56
Confidence Interval for Population Proportion with Python
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57
Introduction to Regression Line
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58
Introduction to Correlation Coeffient
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59
Introduction to Scatter Plots
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60
Correlation and Regression Line Formulas and Examples
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61
Computer Science for Beginners (UC or CSU/ Networking/ Graduate School or Bachelor/ AI/ and More)
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62
Basics of Machine Learning
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63
Machine Learning Prediction Housing Project
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64
Machine Learning Prediction Basics
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65
Machine Learning Prediction Project Customer
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66
Google Colab and MAC reading CSV Files pd.read_csv
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67
Define Function and Add More Complexity to It Using Conditional Statements and For Loops
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68
Generate Random Values Using range np.arange np.zeros np.linspace
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