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Gain useful insights from data by perfog popular data science techniques using Python libraries

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 b with perfog 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 eeering. His areas of interest include recommender systems, NLP, and graph analytics. In his spare , 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 learning more about the field and learning to use its emeg 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/Addiemoving the DataFrame Columns

Lecture 51 Dropping Duplicate Rows

Lecture 52 Indexing DataFrame to Retrieve Specific Columns and Rows

Lecture 53 Moncatenating/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 (Perfog 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 perfog data analysis, data wrangling, data manipulation, and data visualization on their own datasets.

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