Welcome to 2024 Master class on Data Science using Python.
NumPy is a leading scientific computing library in Python while Pandas is for data manipulation and analysis. Also, learn to use Matplotlib for data visualization. Whether you are trying to go into Data Science, dive into machine learning, or deep learning, NumPy and Pandas are the top Modules in Python you should understand to make the journey smooth for you. In this course, we are going to start from the basics of Python NumPy and Pandas to the advanced NumPy and Pandas. This course will give you a solid understanding of NumPy, Pandas, and their functions.
At the end of the course, you should be able to write complex arrays for real-life projects, manipulate and analyze real-world data using Pandas.
WHO IS THIS COURSE FOR?
√ This course is for you if you want to master the in-and-out of NumPy, Pandas, and data visualization.
√ This course is for you if you want to build real-world applications using NumPy or Panda and visualize them with Matplotlib and Seaborn.
√ This course is for you if you want to learn NumPy, Pandas, Matplotlib and Seaborn for the first time or get a deeper knowledge of NumPy and Pandas to increase your productivity with deep and Machine learning.
√ This course is for you if you are coming from other programming languages and want to learn Python NumPy and Pandas fast and know it really well.
√ This course is for you if you are tired of NumPy, Pandas, Matplotlib and Seaborn courses that are too brief, too simple, or too complicated.
√ This course is for you if you have to get the prerequisite knowledge to understanding Data Science and Machine Learning using NumPy and Pandas.
√ This course is for you if you want to learn NumPy and Pandas by doing exciting real-life challenges that will distinguish you from the crowd.
√ This course is for you if plan to pass an interview soon.
Data Handling using Numpy
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1Variables in Python
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2Conditionals & If statement
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3Example for If statement
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4If else statement
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5Example of If else statement
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6Nested If statement
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7Example for Nested If statement
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8Elif statement
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9Example for Elif statement
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10While loop
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11Example of while loop
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12For Loop
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13Example of For Loop
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14Break & Continue Statement
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15Introduction to containers
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16Creating and accessing lists in Python
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17List indexing and slicing
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18Working with List methods
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19Working with operators on lists
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20List Comprehension
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21Tuple : definition
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22Tuples
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23Tuple Indexing & Slicing
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24Manipulating Tuples
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25Unpacking Tuples
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26Sets
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27Dictionaries
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28Basics of dictionary
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29Accessing dictionary
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30len, str & type functions in dictionary
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31Functions in python
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32Example program1 on Functions
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33Example program2 on functions
Data Handling using Pandas
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34Introduction to modules in python
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35Creating & Displaying 1D array
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36Understanding 1D array Index
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37Creating Array of 0's and Array of 1's
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38Sorting elements in 1D array
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39Slicing a 1D array
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40Mathematical Operations on Array
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41Searching an element in a Array
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42Filtering an array
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43Checking whether given array is empty or not ?
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44Creating & Displaying 2D array
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45ndim Attribute
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46Size Attribute
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47Shape and reshape of array
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48Creating an Identity Matrix
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49arange()
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50linspace()
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51Random array
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52Random matrix
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53Creating a diagonal matrix
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54Flatten a Matrix
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55Computing Trace of a Matrix
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56Finding Transpose of a Matrix
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57Negative indexing to access elements in a 2D array
Data Visualization using Matplotlib in Python
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58Introduction to Pandas
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59Working with series in Pandas
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60Combining series with Numpy
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61Finding number of elements in a series
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62Computing mean, max and min in a series
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63Sorting a Series
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64Displaying Unique values in a Series
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65Summary of series statistics
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66Creating DataFrame From Series
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67Creating DataFrame from List of Dictionaries
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68Data Frame access using row-wise and column-wise.
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69Add, Rename and Delete Columns in a Data Frame
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70Deleting rows and cols using drop()
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71Boolean Indexing in DataFrames
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72Concatenating DataFrames