2024 Master class on Data Science using Python A-Z for ML
- Description
- Curriculum
- FAQ
- Reviews
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.
-
1
Variables in Python -
2
Conditionals & If statement
-
3
Example for If statement
-
4
If else statement
-
5
Example of If else statement
-
6
Nested If statement
-
7
Example for Nested If statement
-
8
Elif statement
-
9
Example for Elif statement
-
10
While loop
-
11
Example of while loop
-
12
For Loop
-
13
Example of For Loop
-
14
Break & Continue Statement
-
15
Introduction to containers
-
16
Creating and accessing lists in Python
-
17
List indexing and slicing
-
18
Working with List methods
-
19
Working with operators on lists
-
20
List Comprehension
-
21
Tuple : definition
-
22
Tuples
-
23
Tuple Indexing & Slicing
-
24
Manipulating Tuples
-
25
Unpacking Tuples
-
26
Sets
-
27
Dictionaries
-
28
Basics of dictionary
-
29
Accessing dictionary
-
30
len, str & type functions in dictionary
-
31
Functions in python
-
32
Example program1 on Functions
-
33
Example program2 on functions
-
34
Introduction to modules in python
-
35
Creating & Displaying 1D array
-
36
Understanding 1D array Index
-
37
Creating Array of 0's and Array of 1's
-
38
Sorting elements in 1D array
-
39
Slicing a 1D array
-
40
Mathematical Operations on Array
-
41
Searching an element in a Array
-
42
Filtering an array
-
43
Checking whether given array is empty or not ?
-
44
Creating & Displaying 2D array
-
45
ndim Attribute
-
46
Size Attribute
-
47
Shape and reshape of array
-
48
Creating an Identity Matrix
-
49
arange()
-
50
linspace()
-
51
Random array
-
52
Random matrix
-
53
Creating a diagonal matrix
-
54
Flatten a Matrix
-
55
Computing Trace of a Matrix
-
56
Finding Transpose of a Matrix
-
57
Negative indexing to access elements in a 2D array
-
58
Introduction to Pandas
-
59
Working with series in Pandas
-
60
Combining series with Numpy
-
61
Finding number of elements in a series
-
62
Computing mean, max and min in a series
-
63
Sorting a Series
-
64
Displaying Unique values in a Series
-
65
Summary of series statistics
-
66
Creating DataFrame From Series
-
67
Creating DataFrame from List of Dictionaries
-
68
Data Frame access using row-wise and column-wise.
-
69
Add, Rename and Delete Columns in a Data Frame
-
70
Deleting rows and cols using drop()
-
71
Boolean Indexing in DataFrames
-
72
Concatenating DataFrames
