2025 Master class on Data Science using Python A-Z for ML

Name: 2025 Master class on Data Science using Python A-Z for ML
Rating: 3.7 (681 reviews)

Python NumPy, Pandas, Matplotlib and Seaborn for Data Analysis, Data Science and ML. Pre-machine learning Analysis.

Created byToppers Bootcamp

Last updated 3/2025

English

What you'll learn

Students will learn how to create and manipulate arrays, perform mathematical operations on arrays, and use functions such as sorting, searching, and statistics
Students will learn how to create and manipulate Series and Data Frames.
Students will learn how to create plots and charts, customize the appearance of visualizations, and add annotations and labels.
NumPy, Pandas, and Matplotlib will typically teach students how to use these tools to analyze and visualize data.

Course content

8 sections • 87 lectures • 6h 15m total length

Variables in Python4:35
Conditionals & If statement4:48
Example for If statement3:36
Write a Python program that reads marks from the user and uses an if statement to validate that 0 to 100, inclusive, and prints invalid marks when out of range.
If else statement2:02
Example of If else statement1:51
Nested If statement3:31
Example for Nested If statement3:51
Elif statement3:40
Example for Elif statement2:24
While loop6:52
Example of while loop3:16
For Loop7:36
Example of For Loop1:55
Break & Continue Statement2:37
Introduction to containers17:31
Explore containers in Python, focusing on lists, their mutable properties, and indexable slicing, plus list operations like append, extend, insert, pop, count, index, reverse, sort.
Creating and accessing lists in Python7:06
List indexing and slicing9:05
Working with List methods31:05
Working with operators on lists3:37
List Comprehension3:57
Tuple : definition3:35
Tuples3:08
Tuple Indexing & Slicing18:15
Manipulating Tuples4:55
Unpacking Tuples1:09
Sets3:48
Dictionaries5:32
Basics of dictionary20:13
Accessing dictionary7:16
len, str & type functions in dictionary4:15
Learn to find a dictionary’s length with len, convert it to str, and determine its type with type, showing a class dictionary and its three key-value pairs.
Functions in python4:28
Example program1 on Functions6:20
Example program2 on functions3:21

Introduction to modules in python2:28
Creating & Displaying 1D array1:12
Understanding 1D array Index4:17
Creating Array of 0's and Array of 1's1:48
Sorting elements in 1D array1:17
Slicing a 1D array10:29
Master array slicing in numpy by specifying start, end, and optional step to extract one-dimensional slices, as shown with examples from a one-dimensional array.
Mathematical Operations on Array4:22
Searching an element in a Array6:34
Filtering an array4:20
Checking whether given array is empty or not ?2:54
Learn to check if a numpy array is empty by using np.array and array.size, and print 'array is empty' or 'array is not empty' based on the size.
Creating & Displaying 2D array1:03
Learn to access and display the contents of a numpy 2d array by printing the full array and rendering it row by row with a for loop.
ndim Attribute2:19
Size Attribute2:30
Shape and reshape of array3:17
Creating an Identity Matrix2:19
arange()2:43
Explore how numpy arange creates arrays using a start, stop, and optional step, with examples from 1 to 10, step 2, and negative or floating point values.
linspace()1:29
Random array1:22
Random matrix1:17
Creating a diagonal matrix1:04
Flatten a Matrix2:10
Computing Trace of a Matrix3:12
Finding Transpose of a Matrix4:19
Negative indexing to access elements in a 2D array3:27
Explore negative indexing in a two dimensional array with numpy, accessing elements like a[1, -1] to retrieve the last column value, 10, in the example.

Introduction to Pandas4:46
Working with series in Pandas8:07
Combining series with Numpy5:35
Finding number of elements in a series1:30
Computing mean, max and min in a series3:38
Compute mean, max, and min of a pandas series with S1 and print the results, including mean 30, max 50, and min 10.
Sorting a Series2:49
Displaying Unique values in a Series2:29
Summary of series statistics2:12
Creating DataFrame From Series4:39
Creating DataFrame from List of Dictionaries0:49
Data Frame access using row-wise and column-wise.4:07
Add, Rename and Delete Columns in a Data Frame5:33
Deleting rows and cols using drop()3:39
Learn to delete columns using the drop method (axis 1) and delete rows using axis 0, including deleting specific indices, with practical results.
Boolean Indexing in DataFrames2:46
Learn how to use boolean indexing in dataframes by creating a dataframe from a dictionary, assigning boolean indices, and selecting rows with true using df.loc.
Concatenating DataFrames2:26

Introduction to Matplotlib2:57
Creating Line Graph5:30
Creating Bar Graph2:20
Creating Scatter Graph1:07
Creating Histogram Graph2:01
Learn how to create a histogram in Python with Matplotlib, set bins using the square root rule, enforce monotonic bin order, and add a legend to the graph.
Creating Pie Chart2:24
Creating 3D Plot1:23
Create 3d plots by importing matplotlib.pyplot as plt, building a figure with a 3d projection, and running the code to observe the initial 3d projection.
Creating 3D Line graph0:34
Explore how a 3D line graph sits inside a 3D plot, then project the X, y, z axes to produce the resultant output.

Understanding a sample Dataset (Downloadable)2:32
Explore Seaborn to plot swarm plots, violin plots, facet grids, and heatmaps using the tips dataset, with columns like total bill, tip, sex, smoker, day, time, and size.
Introduction to Seaborn2:58
Swarm Plot4:07
Explore swarm plots in seaborn to visualize tip values by day (x axis) and by sex (y axis) using tips.csv, with color palettes for male and female.
Violin Plot1:48
Explore violin plots to visualize density for numerical data, using seaborn to compare categories like male versus female total bill, with clear density on each side.
Facet Grids4:31
Explore facet grids in seaborn to plot histograms or scatter plots for the total bill and tip data, using hue for male and female across time columns and sex rows.
Heatmap1:48

Requirements

Little knowledge in Python will be an added advantage.
If you are new to python then don't worry you can still learn python basics from the BONUS section of this course.

Description

Welcome to 2025 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.

Who this course is for:

Students who want to learn data science using Python.
Anyone with an interest in data science and machine learning

2025 Master class on Data Science using Python A-Z for ML

What you'll learn

Explore related topics

Course content

BONUS : Python Crash Course33 lectures • 3hr 31min

Data Handling using Numpy24 lectures • 1hr 12min

Data Handling using Pandas15 lectures • 55min

Data Visualization using Matplotlib in Python8 lectures • 18min

Data Visualization using Seaborn in Python6 lectures • 18min

Problem Solving Assignments0

Projects0

Bonus Lecture1 lecture • 1min

Requirements

Description

Who this course is for: