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: 4.0 (683 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

English [Auto],

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
Learn how variables act as containers in Python, holding integers, floats, strings, and booleans, and see how to print values and swap them with single-line or temporary variable approaches.
Conditionals & If statement4:48
Learn conditional statements and the if statement with relational operators like less than, greater than, equal to, and not equal to; evaluate conditions to run the correct block.
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
Explore how the if else statement evaluates a conditional expression and executes the if or else block, printing even or odd based on parity.
Example of If else statement1:51
Learn to use an if else statement to compare two input values, assign them to a and b, and display whether they are the same or not.
Nested If statement3:31
Explore how nested if statements work by evaluating an outer condition, then an inner condition, with examples showing numbers as positive, negative, or zero.
Example for Nested If statement3:51
Explore nested if statements by implementing a program that finds the biggest of two numbers, handling A>B, B>A, and equality with outer and inner if logic.
Elif statement3:40
Master the elif statement to choose among multiple blocks based on conditional expressions, where only one block executes. See how x and y comparisons trigger blocks or the else.
Example for Elif statement2:24
Learn how to compare two numbers in Python using if, elif, and else. See how A and B are compared to determine which is biggest or if they are equal.
While loop6:52
Explore the while loop in Python, covering its syntax, initialization, conditional expression, and update statement, with flow chart intuition and an example including an else block.
Example of while loop3:16
Count digits in a number with a while loop in Python by reading input, using mod ten to extract digits, and reducing the number with integer division.
For Loop7:36
Explore Python for loops, including for variable in iterable, range usage (single and two parameters), and summing totals, plus iterating lists and using else blocks.
Example of For Loop1:55
Write a Python program that reads N and uses a for loop with range(1, N+1) to print numbers from 1 to N, separated by a space.
Break & Continue Statement2:37
Explore how the break statement exits a for loop when a condition is met, and how the continue statement skips the current iteration, excluding five from the output.
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
Demonstrate that lists are mutable and show how to display and access elements by index, slice with ranges, use negative indexing, loop through, get length, and delete items.
List indexing and slicing9:05
Learn how to index and slice lists in Python, using zero-based and negative indices, access the first and last elements, and create sublists by specifying start, end, and step.
Working with List methods31:05
Master Python list methods including append, clear, copy, count, extend, index, insert, pop, remove, reverse, and sort through practical examples. Understand how each method alters lists.
Working with operators on lists3:37
Learn how the plus operator concatenates two lists into a new list while leaving originals intact, and how the star operator repeats a list to create longer sequences.
List Comprehension3:57
Learn Python list comprehension, using x for y in z, to build conditioned lists; illustrate with squares from zero to nine and filter for even numbers to produce even squares.
Tuple : definition3:35
Explore Python tuples, immutable containers defined with parentheses and comma separators; perform indexing and slicing like lists, see zero-based access, and learn how edits raise errors, plus multi-value assignments.
Tuples3:08
Explore Python tuples as ordered, immutable collections stored in round brackets with comma-separated items. Learn how to create tuples with numbers, strings, or mixed types and print their contents.
Tuple Indexing & Slicing18:15
Learn to index and slice tuples by identifying element positions, applying positive and negative indexing, checking membership with in, and selecting ranges with start, end, and step.
Manipulating Tuples4:55
Manipulate tuples by converting them to lists, modify the list with append and pop, then convert back to a tuple to finalize changes in Python.
Unpacking Tuples1:09
Learn how to unpack a three-item tuple by assigning each element to separate variables (name, age, university) and display the contents to verify the results.
Sets3:48
Learn Python sets: understand set syntax with braces, remove duplicates automatically, create empty sets, convert lists to sets, and use membership tests and basic set operations.
Dictionaries5:32
Dictionaries in Python are key-value pairs enclosed in curly braces with immutable keys. Access, modify, and add entries, and provide a default when a key is missing.
Basics of dictionary20:13
Explains Python dictionaries as mutable, unordered key-value stores that disallow duplicates, demonstrates creating dictionaries, accessing values by keys or get, and updating or deleting entries with pop, plus adding elements.
Accessing dictionary7:16
Iterate over dictionaries using for loops to access key-value pairs with items(), or loop over keys and values with keys() and values(), and print results.
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
Learn how to write and use functions in Python, including defining functions with parameters, using docstrings, returning values, and examples like square, multiply, and min-max with tuples.
Example program1 on Functions6:20
Define and call functions to compute the square and cube of a number, returning the results and printing the square and cube using input num.
Example program2 on functions3:21
Learn to implement a find largest function that determines the maximum of three numbers using conditional checks, input prompts, function calls, and returning the result.

Introduction to modules in python2:28
Learn how Python modules, as files of definitions, are imported by importing everything, specific names, or aliases like NumPy as np, with math pi and NumPy cos(0) examples.
Creating & Displaying 1D array1:12
Create and display a one-dimensional array with numpy, print its full content, and iterate over elements with a for loop.
Understanding 1D array Index4:17
Explore how a one-dimensional array stores elements at indices 0 to 4, handle negative indices, observe out-of-bounds errors, and print all elements with a for loop using the array length.
Creating Array of 0's and Array of 1's1:48
Learn to create arrays of five zeros and five ones in numpy using zeros and ones functions, then print the results for data science and machine learning.
Sorting elements in 1D array1:17
Learn how to sort a 1d array in numpy using the sort function; display the original and sorted content with np.sort to verify that the elements are in order.
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
Explore how to perform element-wise mathematical operations on numpy arrays, including addition, subtraction, multiplication, and division, using a simple example array.
Searching an element in a Array6:34
The lecture demonstrates searching for elements in a NumPy array and returning their indices, including positions of the value four and even values via modulo two.
Filtering an array4:20
Learn to filter an array using a boolean index list to create a new array, as shown with numpy selecting 41 and 43 from 41, 42, 43, 44.
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
Use the ndim attribute to determine an array’s dimension in numpy. The example shows a one-dimensional array and a 3x2 two-dimensional matrix, printing dimensions 1 and 2.
Size Attribute2:30
Use the numpy array size attribute to count elements in both one-dimensional and two-dimensional arrays, with concrete examples showing four, six, and eight elements as you add items.
Shape and reshape of array3:17
Explore the shape attribute to inspect and reshape matrices, changing a 3x4 array to formats like 6x2, 2x6, 1x12, or 4x3 by adjusting rows and columns with array.reshape.
Creating an Identity Matrix2:19
Import numpy and use a function called i to create an n by n identity matrix in Python, illustrating square matrix representation and the identity property.
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
Generate evenly spaced numbers for a given interval using numpy linspace, comparing it to the arrange function to build a 15-point array and print the results.
Random array1:22
Learn to generate random numbers with NumPy by using rand to create a one-dimensional array of floating-point values between 0 and 1, then print the result.
Random matrix1:17
Create a random matrix by specifying its size as three by four or five by five, and print X to view elements in the range zero to one.
Creating a diagonal matrix1:04
Create a diagonal matrix in numpy using drag function by listing diagonal values, such as 1, 2, 3, producing a 5 by 5 matrix with those elements on the diagonal.
Flatten a Matrix2:10
Master NumPy's flatten method to convert a 3x3 matrix into a one-dimensional array, illustrating the 1D array representation.
Computing Trace of a Matrix3:12
Compute the trace of a matrix by summing its diagonal elements using numpy, demonstrated on a 3x3 matrix with result 15 and an alternate diagonal-sum approach.
Finding Transpose of a Matrix4:19
Learn to compute the transpose of a matrix using numpy from a 3x2 two-dimensional array. Use built-in options like np.transpose or the A.T shortcut to obtain the 2x3 transposed matrix.
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
Explore pandas fundamentals for Python: manipulate tables with series and dataframes, built on NumPy and compatible with Matplotlib, and learn creating and indexing series and converting dictionaries.
Working with series in Pandas8:07
Create and manipulate a Pandas series with default and custom indices, perform element-wise operations like adding five, and filter data with greater than two condition.
Combining series with Numpy5:35
Learn to combine numpy arrays with pandas series by creating arrays, converting to series, applying numpy functions such as sqrt, and using custom indices to label values.
Finding number of elements in a series1:30
Learn to determine the number of elements in a pandas series using the size property, as demonstrated with a five-element series.
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
Apply the sort_values function to a given series to obtain a sorted series while preserving indices; observe that original series values are rearranged, not the indices.
Displaying Unique values in a Series2:29
Identify unique values in a Pandas series using the unique function and count distinct elements with n_unique to determine how many unique values appear.
Summary of series statistics2:12
Compute summary statistics for a pandas series using describe, including count, mean, standard deviation, minimum value, maximum value, and quartiles, demonstrated with a sample series.
Creating DataFrame From Series4:39
Learn to create an empty dataframe in pandas and display it with print. Then build a dataframe from one or more series and a dictionary, demonstrating a two dimensional structure.
Creating DataFrame from List of Dictionaries0:49
Create a data frame from a list of dictionaries to produce a two-dimensional, table-like representation with columns such as name and surname.
Data Frame access using row-wise and column-wise.4:07
Learn to access a data frame row-wise with a for loop, printing row indices and values. Switch to column-wise access with iteritems to print column indices and values.
Add, Rename and Delete Columns in a Data Frame5:33
Learn to add, rename, and delete columns in a Pandas dataframe. Create from a Series, assign column names, add a constant-valued column, and drop or pop a column.
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
Learn how to concatenate two dataframes using pandas, create df1 and df2 from dictionaries, and store the result in df3 while printing each dataframe to verify the operation.

Introduction to Matplotlib2:57
Explore Matplotlib in Python to visualize data through plotting graphs, including line, bar, scatter, pie, area, and histogram plots, with hands-on practice using pyplot.
Creating Line Graph5:30
Plot a line graph with Matplotlib in Python by initializing x and y data, labeling axes, adding a title, and showing a chart of student semester marks.
Creating Bar Graph2:20
Transform data into a bar chart in Python using matplotlib's plt.bar, updating x and y data to reflect changes in the bar graph.
Creating Scatter Graph1:07
Transform the bar graph data into a scatter chart, adding titles and labels. Observe the scatter plot and its y-axis values for each semester (85, 67, 95, 56, 73).
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
Create a Matplotlib pie chart by setting sizes with x and using explode to separate wedges; adjust values to highlight a wedge, then show the plot.
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
Learn Seaborn, a Python visualization library built on Matplotlib, import Seaborn and Pandas, load the tips dataset with seaborn.load_dataset, inspect 244-row data with head and tail.
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
Visualize a five-by-three matrix of uniform data with a Seaborn heatmap, mapping values from 0 to 1 to colors for clear graphical representation.

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: