Data Analysis, Simulation, Visualization And Exploration
Published 12/2024
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 6.21 GB | Duration: 16h 22m
Published 12/2024
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 6.21 GB | Duration: 16h 22m
A deep dive in data analysis, simulation and visualization for Data Science, Machine Learning and Business Students
What you'll learn
Masters Python Data Types and Numpy
A deep dive in Pandas library for learning Data Analysis techniques
Masters Matplotlib, most powerful Python library for data plotting and visualization
Learn advanced customization like subplots, styling, and object oriented programming with Matplotlib
A deep dive in Seaborn, a library for Data Visualization and Inference.
Learn Scatterplots, Categorical Plot, Distribution Plots, Comparison plots, Seaborn Grids, Pie plots Matrix plots,
Learn Data and Visual Analytics by completing a project
Monte Carlo Simulations and Visualization with lots of Experiments and Exercises
Learn to Create and Visualize different time series data such as impulse , square, triangular , sinusoidal , and synthetic time series to mimic real world data.
Learn to create images and draw different shapes on images such as lines, edges, corners, rings, circles
Learn how to create your own custom image dataset
Requirements
High school mathematics is recommended but not necessary
Description
DescriptionThis is a complete and comprehensive course on Data Analysis, Simulation, Visualization and Exploration. It is hands-on course design to make you expert by solving projects and exercises by using Python’s most important packages and libraries such NumPy, Pandas, Matplotlib and Seaborn.Course OutlineNumPy and Python RefresherIn this section of the course, we learn basic and fundamentals of python. If you don’t know anything about the python, even then you do not need to worry about, this section will provide you all the fundamentals.Pandas ( Data Analysis )In this section we deep dive into Pandas to learn Pandas Series, Pandas Data Frames, groupby method, pivot table, conditional filtering with Pandas, Combining Data Frames and Other advanced data analysis techniquesMatplotlib ( Data Plotting and Visualization )We learn everything of Matplotlib from fundamental to advanced. We learn how to customize the figures and plots from 1D to 3D.Seaborn ( Data Visualization and Inference )In this section, we dive deep into Seaborn. It is the most important Visualization package for data visualization Inference. It provides the plots, charts and visualization in such manners that other than visualizing we can also infer the statistical information from the data such as the data distribution, mean, median, Interquartile range etc.Project ( Data and Visual Analytics )After learning Pandas, Matplotlib and Seaborn, now its time to do a project that require the knowledge from above three sections. In this section you will have to solve the project to get expertise in Data Analysis and Visualization.Montecarlo Simulations and VisualizationWe Perform Monte Carlo Simulation when we are not sure about the outcome of some process. In this section we perform several experiments of Montecarlo Simulations and then at the end you will solve exercise to solidify your conceptsTime Series Generation and VisualizationTime Series is a very important type of data with numerous practical applications. In this section you will learn how to create different types of time series data. At the end of this section, you will solve an exercise to get command on time series generation and visualizationImage Creation and VisualizationImage is another very import data type. This section is dedicated to make you understand how to create your own color and gray scale images. We will also learn how to draw lines, edges, corners, rings and circles on the images. We will also learn how to create our own custom image dataset. At the end of this section, you will solve an exercise to get the full understanding of image creation and visualization
Overview
Section 1: Introduction of the Course
Lecture 1 Introduction
Lecture 2 Course Material
Lecture 3 How to succeed in this course
Section 2: Introduction to Google Colab
Lecture 4 Introduction of the section
Lecture 5 Mounting the drive and reading dataset
Lecture 6 Mounting the drive and reading, displaying an image
Lecture 7 Reading More datasets
Lecture 8 Uploading Course Material to Google Drive
Section 3: NumPy and Python Refresher
Lecture 9 Introduction of the section
Lecture 10 Arithmetic Operations With Python
Lecture 11 Comparison and Logical Operators
Lecture 12 Conditional Staements
Lecture 13 NumPy Part01 : Creating Arrays
Lecture 14 NumPy Part02 : Methods and Attributes
Lecture 15 NumPy Part03 : Indexing and Slicing
Lecture 16 NumPy Part04 : Broad Casting With NumPy Arrays
Lecture 17 Lists in Python
Lecture 18 For Loop Part01
Lecture 19 For Loop Part02
Lecture 20 While Loop
Lecture 21 Strings in Python
Lecture 22 Print Formatting With Strings
Lecture 23 Dictionaries Part01
Lecture 24 Dictionaries Part02
Lecture 25 Functions in Python Part01
Lecture 26 Functions in Python Part02
Lecture 27 Tuples
Lecture 28 Lambda function
Lecture 29 Map function
Lecture 30 Reduce function
Lecture 31 Filter function
Lecture 32 Zip function
Lecture 33 Join function
Section 4: Pandas ( Data Analysis )
Lecture 34 Introduction of the section
Lecture 35 Creating Pandas Series
Lecture 36 Operations on Pandas Series
Lecture 37 Creating Pandas Data Frame
Lecture 38 Working with rows and columns of Data Frames
Lecture 39 Dealing with missing values
Lecture 40 Pandas Filtering
Lecture 41 Pandas Methods Part01
Lecture 42 Pandas Methods Part02
Lecture 43 Combining Data Frames
Lecture 44 Pandas groupby method
Lecture 45 Pandas Multilevel Indexing
Lecture 46 Pandas date time method part01
Lecture 47 Pandas date time method part02
Lecture 48 Pandas Pivot table
Section 5: Matplotlib ( Data Plotting and Visualization )
Lecture 49 Introduction of the section
Lecture 50 First plot with Matplotlib
Lecture 51 Two plots on same figure
Lecture 52 Changing the color of the line of plot
Lecture 53 Changing the size of the figure
Lecture 54 Changing the width of the line of plot
Lecture 55 Label the axes of the plot
Lecture 56 Adding legend and title to the plot
Lecture 57 Setting limits for X-axis and Y-axis of the plot
Lecture 58 Adding ticks on X-axis and Y-axis
Lecture 59 Changing line style and adding more customizations
Lecture 60 Setting styles with Matplotlib
Lecture 61 Subplotting in Matplotlib
Lecture 62 rc Parameters of Matplotlib
Lecture 63 Creating Figure Object - OOP in Matplotlib
Lecture 64 Subplotting with figure object
Lecture 65 Advanced Matplotlib Plots Part01
Lecture 66 Advanced Matplotlib Plots Part02
Section 6: Seaborn ( Data Visualization and Inference )
Lecture 67 Introduction of the section
Lecture 68 Scatter plots
Lecture 69 Scatter plots in Seaborn
Lecture 70 Distribution Plots
Lecture 71 Distribution plots in Seaborn Part01
Lecture 72 Distribution plots in Seaborn Part02
Lecture 73 Categorical Plots
Lecture 74 Categorical plots ( countplot and barplot in Seaborn )
Lecture 75 Categorical plots ( boxplot and violin plot in Seaborn )
Lecture 76 Categorical plots ( swarmplot and boxenplot in Seaborn )
Lecture 77 Comparison plots ( jointplot and pairplot in Seaborn )
Lecture 78 Seaborn Grids
Lecture 79 Matrix Plots ( heatmap and cluster map in Seaborn )
Lecture 80 Linear Model ( lm ) plots in Seaborn
Lecture 81 Pieplot
Section 7: Project ( Data and Visual Analytics )
Lecture 82 Project Exercise Overview
Lecture 83 Project Exercise Solution Part01
Lecture 84 Project Exercise Solution Part02
Section 8: Monte Carlo Simulations and Visualization
Lecture 85 Introduction of the section
Lecture 86 Flipping a coin and pair of coins
Lecture 87 Monte Carlo Simulations - Flipping a single coin
Lecture 88 Monte Carlo Simulations : Flipping a pair of coins
Lecture 89 Rolling a die and pair of dice
Lecture 90 Monte Carlo Simulations - Rolling a single die
Lecture 91 Montecarlo Simulations-Rolling a Pair of die and getting two sixes
Lecture 92 Montecarlo Simulations-Rolling a Pair of die and getting two after six
Lecture 93 Exercise 01-Overview
Lecture 94 Exercise 01-Solution
Lecture 95 Exercise 02-Overview
Lecture 96 Exercise 02-Solution
Section 9: Time Series Generation and Visualization
Lecture 97 Introduction of the section
Lecture 98 Impulse series
Lecture 99 Generating and Visualizing Impulse Series
Lecture 100 Generating and Visualizing Square Wave Series
Lecture 101 Generating and Visualizing Triangular Series
Lecture 102 Sinusoidal Time Series
Lecture 103 Generating and Visualizing Sinusoidal Time Series
Lecture 104 Exponential Time Series
Lecture 105 Generating and Visualizing Exponential Time Series
Lecture 106 Generating and Visualizing Chirp Time Series
Lecture 107 Generating and Visualizing Synthetic Time Series
Lecture 108 Exercise Overview
Lecture 109 Exercise Solution
Section 10: Image Creation and Visualization
Lecture 110 Introduction of the section
Lecture 111 Image Fundamentals with NumPy and Matplotlib
Lecture 112 Line, Edge and Corner of an image
Lecture 113 Drawing and Visualizing line on an image
Lecture 114 Creating and Visualizing Edge and Corner on an image
Lecture 115 Creating and Visualizing Ring and Circle on the image
Lecture 116 Creating our own custom image dataset containing 4000 images
Lecture 117 Exercise Overview
Lecture 118 Exercise Solution
Section 11: Bonus Lecture
Lecture 119 Introduction
Data Scientists who want to learn data analysis and visualization tools,Any one who wants to learn how to simulate and visualize data,Anyone who wants to improve Python Programming skills,Engineers,Statisticians,Computer Scientists,Business Analysts