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Data Science With Python 3.X

TOP 110


Alpha and Omega
Jan 21, 2021
Reaction score
3 years of service

Last updated 6/2019
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz
Language: English | Size: 6.04 GB | Duration: 13h 37m
Gain useful insights from data by performing popular data science techniques using Python libraries

What you'll learn
Enhance your programming skills and master data exploration and visualization in Python
Learn multidimensional analysis and reduction techniques
Master advanced visualization techniques (such as heatmaps) for better analysis and rapidly broaden your understanding
Retrieve data from different data sources (CSV, JSON, Excel, PDF) and parse them in Python to give them a meaningful shape
Perform statistical analysis using in-built Python libraries
Understand the concept of Block algorithms and how Dask leverages it to load large data.
Implement various example using Dask Arrays, Bags, and Dask Data frames for efficient parallel computing
Combine Dask with existing Python packages such as NumPy and Pandas
Implement an end-to-end Machine Learning pipeline in a distributed setting using Dask and scikit-learn
Visualize and gain insights into real-world datasets via different chart types using Matplotlib
Basic knowledge of probability/statistics and Python coding experience will assist you in understanding the concepts covered in this course.
Python is an open-source community-supported, general-purpose programming language that, over the years, has also become one of the bastions of data science. Thanks to its flexibility and vast popularity that data analysis, visualization, and machine learning can be easily carried out with Python.This practical course is designed to teach you how to perform data science tasks such as data analysis, data manipulation, and data visualization. You will begin with performing data analysis on real-world datasets. You will then work on large datasets and perform exploratory data analysis to investigate the dataset and to come up with the findings from it.You will also learn to scale your data analysis and execute distributed data science projects right from data ingestion to data manipulation and visualization using Dask. Next, you will explore Dask frameworks and see how Dask can be used with other common Python tools such as NumPy, Pandas, matplotlib, Scikit-learn, and more. Finally, you will perform data visualization using Python and Matplotlib 3.By the end of this course, you will be able to use the power of Python to analyze data, create beautiful visualizations, and use powerful machine learning algorithms.Meet Your Expert(s):We have the best work of the following esteemed author(s) to ensure that your learning journey is smooth:Mohammed Kashif works as a Data Scientist at Nineleaps, India, dealing mostly with graph data analysis. Prior to this, he worked as a Python developer at Qualcomm. He completed his Master's degree in Computer Science from IIT Delhi, with a specialization in data engineering. His areas of interest include recommender systems, NLP, and graph analytics. In his spare time, he likes to solve questions on StackOverflow and help debug other people out of their misery. He is also an experienced teaching assistant with a demonstrated history of working in the Higher-Education industry.Jamshaid Sohail is a Data Scientist who is highly passionate about Data Science, Machine learning, Deep Learning, big data, and other related fields. He spends his free time learning more about the field and learning to use its emerging tools and technologies. He is always looking for new ways to share his knowledge with other people and add value to other people's lives. He has also attended Cambridge University for a summer course in Computer Science where he studied under great professors and would like to impart this knowledge to others. He has extensive experience as a Data Scientist in a US-based company. In short, he would be extremely delighted to educate and share knowledge with other people.Harish Garg is a co-founder and software professional with more than 18 years of software industry experience. He currently runs a software consultancy that specializes in the data analytics and data science domain. He has been programming in Python for more than 12 years and has been using Python for data analytics and data science for 6 years. He has developed numerous courses in the data science domain and has also published a book involving data science with Python, including Matplotlib.
Section 1: Exploratory Data Analysis with Pandas and Python 3.x
Lecture 1 The Course Overview
Lecture 2 Basic Statistical Measures
Lecture 3 Variance and Standard Deviation
Lecture 4 Visualizing Statistical Measures
Lecture 5 Calculating Percentiles
Lecture 6 Quartiles and Box Plots
Lecture 7 Finding Missing Values
Lecture 8 Dealing with Missing Values
Lecture 9 Hands-on with Dealing with Missing Values
Lecture 10 Case Study: Missing Data in Titanic Dataset
Lecture 11 What are Outliers?
Lecture 12 Using Z-scores to Find Outliers
Lecture 13 Modified Z-scores
Lecture 14 Using IQR to Detect Outliers
Lecture 15 Types of Variables
Lecture 16 Introduction to Univariate Analysis
Lecture 17 Skewness and Kurtosis
Lecture 18 Univariate Analysis over Olympics Dataset
Lecture 19 Introduction to Bivariate Analysis
Lecture 20 Correlation Coefficient
Lecture 21 Scatter Plots and Heatmaps
Lecture 22 Bivariate Analysis: Titanic Dataset
Lecture 23 Bivariate Analysis: Video Game Sales
Lecture 24 Introduction to Multivariate Analysis
Lecture 25 Multivariate Analysis over Titanic Dataset
Lecture 26 Multivariate Analysis over Pokemon Dataset
Lecture 27 Simpson's Paradox
Lecture 28 Correlation Is Not Causation
Lecture 29 Wine Data Analysis: Initial Setup
Lecture 30 Red Wine Analysis
Lecture 31 White Wine Analysis
Lecture 32 White Wine versus Red Wine: Analysis
Section 2: Data Wrangling with Python 3.x
Lecture 33 The Course Overview
Lecture 34 Installing Anaconda Navigator on Windows/Linux
Lecture 35 Importing and Parsing CSV in Python
Lecture 36 Importing and Parsing JSON in Python
Lecture 37 Scraping Data from Public Web - Part 1
Lecture 38 Scraping Data from Public Web - Part 2
Lecture 39 Importing and Parsing Excel Files - Part 1
Lecture 40 Importing and Parsing Excel Files - Part 2
Lecture 41 Manipulating PDF Files in Python - Part 1
Lecture 42 Manipulating PDF Files in Python - Part 2
Lecture 43 Difference between Relational and Non-Relational Databases
Lecture 44 Storing Data in SQLite Databases
Lecture 45 Storing Data in MongoDB
Lecture 46 Storing Data in Elasticsearch
Lecture 47 Comparative Study of Databases for Storage
Lecture 48 The Most Important Step in Data Analysis
Lecture 49 Viewing/Inspecting DataFrames
Lecture 50 Renaming/Adding/Removing the DataFrame Columns
Lecture 51 Dropping Duplicate Rows
Lecture 52 Indexing DataFrame to Retrieve Specific Columns and Rows
Lecture 53 Merging/Concatenating/Joining DataFrames
Lecture 54 Dealing with Missing Values
Lecture 55 Filtering and Sorting of DataFrame
Lecture 56 Encoding/Mapping Existing Values - Part 1
Lecture 57 Encoding/Mapping Existing Values - Part 2
Lecture 58 Rescale/Standardize Column Values
Lecture 59 Common Cleaning Operations
Lecture 60 Exporting Datasets for Future Use
Lecture 61 Different Uses of Packages (Pandas, NumPy, SciPy, and Matplotlib)
Lecture 62 Types of Column Names/Features/Attributes in Structured Data
Lecture 63 Split-Apply-Combine (Performing Group By Operation)
Lecture 64 Descriptive Statistics Using Python - Part 1
Lecture 65 Descriptive Statistics Using Python - Part 2
Lecture 66 Using Visualizations
Lecture 67 Cool Visualization of Real-World Datasets of World Population Evolution
Lecture 68 Visualizations in Python - Part 1
Lecture 69 Visualizations in Python - Part 2
Lecture 70 Exploring an Online Visualization Tool (RAWGraphs)
Section 3: Scalable Data Analysis in Python with Dask
Lecture 71 The Course Overview
Lecture 72 Introduction to Dask
Lecture 73 Features of Dask
Lecture 74 Limitations of Dask
Lecture 75 Setting Up Dask
Lecture 76 Introduction to Blocked Algorithms
Lecture 77 Hands-On with Dask Arrays
Lecture 78 Digging Deeper into Dask Arrays
Lecture 79 Performance Comparison with NumPy Arrays
Lecture 80 Creating Universal NumPy Functions with Dask
Lecture 81 Limitations of Dask Arrays
Lecture 82 Lazy Evaluation
Lecture 83 Using dask.delayed
Lecture 84 Understanding Task Graphs
Lecture 85 Performance Analysis with dask.delayed
Lecture 86 Introduction to Dask Dataframes
Lecture 87 Exploring Dask Dataframes
Lecture 88 Creating Dask Dataframes
Lecture 89 Loading Large Datasets with Dask Dataframes
Lecture 90 Analyzing Data with Dask Dataframes
Lecture 91 Limitations of Dask Dataframes
Lecture 92 Introduction to Dask Bags
Lecture 93 Creating and Storing Dask Bags
Lecture 94 Manipulating Dask Bags
Lecture 95 Word Count Example Using Dask Bags
Lecture 96 Manipulating JSON Data Using Dask Bags
Lecture 97 Limitations of Dask Bags
Lecture 98 Overview of Distributed Computing with Dask
Lecture 99 Setting Up Your Dask Cluster
Lecture 100 Understanding Dask Schedulers
Lecture 101 Exploring Dask Dashboard UI
Lecture 102 Limitations of dask.distributed
Lecture 103 Persisting Data
Lecture 104 Combining Dask with Futures
Lecture 105 Best Practices for Dask
Lecture 106 Introduction to Dask-ML
Lecture 107 Using Dask-ML for Regression
Lecture 108 Using Dask-ML for Classification
Lecture 109 Hyper-Parameter Tuning Using Dask
Section 4: Data Visualization Recipes with Python and Matplotlib 3
Lecture 110 Course Overview
Lecture 111 Getting Data into Matplotlib
Lecture 112 Drawing Scatter Plots
Lecture 113 Creating Line Plots
Lecture 114 Visualizing Data with Bar Charts
Lecture 115 Drawing Subplots
Lecture 116 Creating Histograms
Lecture 117 Building Heatmaps
Lecture 118 Plotting Data on Box Plots
Lecture 119 Drawing Pie Charts
Lecture 120 Customizing Labels, Titles, and Legends
Lecture 121 Adding Grids and Customizing Ticks
Lecture 122 Using Matplotlib Styles
Lecture 123 Creating Custom Styles
Lecture 124 Plot Annotation
Lecture 125 Build Plots from the Ground-Up Using Plot Scaffolding
Lecture 126 Building Custom Plots Using Figures
Lecture 127 Customizing Plot Axes
Lecture 128 Building 3D Graphs Using Wireframe
Lecture 129 Creating 3D Scatter Plots
Lecture 130 Drawing 3D Bar Charts
Lecture 131 Customizing Wireframes
Lecture 132 Drawing Animated Graphs
Lecture 133 Building an Animated Histogram
Lecture 134 Creating Animated subplots
Lecture 135 Adding Interactivity to Plots
Lecture 136 Creating Visualizations that Update Interactively with Data
Lecture 137 Change the Plot Sizes
Lecture 138 Save Plot Image to a File
Lecture 139 Create Legend Outside the Plot
Lecture 140 Display Plots Inline in a Notebook
Lecture 141 Clear a Plot
Lecture 142 Change Font Sizes of Plot Elements
Lecture 143 Troubleshoot Value Errors
This course is for Python developers, data analysts, and IT professionals who wish to explore the world of data science by performing data analysis, data wrangling, data manipulation, and data visualization on their own datasets.

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KatzSec DevOps

Alpha and Omega
Jan 17, 2022
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2 years of service
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