Data Science With Python (4-Course Bundle)

Learn the data life cycle-from acquisition to processing to analysis-in Python

What you'll learn

Effectively pre-process data (structured or unstructured) before doing any analysis on the dataset

Perform statistical analysis using in-built Python libraries

Learn tricks and techniques that will be invaluable throughout your data science career

Learn how to deal with missing data and outliers to resolve data inconsistencies

Enhance your programming skills and master data exploration and visualization in Python

Explore and work with different plotting libraries

Work with industry-standard tools like Matplotlib, Seaborn, and Bokeh

Gain knowledge on how to prepare data and feed it to machine learning algorithms

Requirements

Basic Python programming experience is required before undertaking the course.

Description

If you're a Python developer and looking to start your journey in data science, then this course is for you. This 5-course bundle takes you from zero experience to a complete understanding of key concepts, edge cases, and using Python for real-world application development. You'll move progressively from the basics to working with larger complex applications. After completing this course, you'll have the skills you need to dive into an existing application or start your own project.Course 1:In this course, you will gather data, prepare data for analysis, perform simple statistical analyses, create meaningful data visualizations, and more! This course will equip us with the tools and technologies, also we need to analyze the datasets using Python so that we can confidently jump into the field and enhance our skill set. The best part of this course is the takeaway code templates generated using the real-life dataset.Course 2:Next, you will learn how to prepare data for analysis, perform simple statistical analyses, create meaningful data visualizations, predict future trends from data, and more.Course 3:You'll study different types of visualizations, compare them, and find out how to select a particular type of visualization using this comparison. You'll explore different plots, including custom creations. After you get a hang of the various visualization libraries, you'll learn to work with Matplotlib and Seaborn to simplify the process of creating visualizations. You'll also be introduced to advanced visualization techniques, such as geoplots and interactive plots. You'll learn how to make sense of geospatial data, create interactive visualizations that can be integrated into any webpage, and take any dataset to build beautiful and insightful visualizations.Course 4:This course will start you on your journey to mastering topics within machine learning. These skills will help you deliver the kind of state-of-the-art predictive models that are being used to deliver value to businesses across industries.

Overview

Section 1: Data Wrangling with Python 3.x

Lecture 1 The Course Overview

Lecture 2 Installing Anaconda Navigator on Windows/Linux

Lecture 3 Importing and Parsing CSV in Python

Lecture 4 Importing and Parsing JSON in Python

Lecture 5 Scraping Data from Public Web – Part 1

Lecture 6 Scraping Data from Public Web – Part 2

Lecture 7 Importing and Parsing Excel Files – Part 1

Lecture 8 Importing and Parsing Excel Files – Part 2

Lecture 9 Manipulating PDF Files in Python – Part 1

Lecture 10 Manipulating PDF Files in Python – Part 2

Lecture 11 Difference between Relational and Non-Relational Databases

Lecture 12 Storing Data in SQLite Databases

Lecture 13 Storing Data in MongoDB

Lecture 14 Storing Data in Elasticsearch

Lecture 15 Comparative Study of Databases for Storage

Lecture 16 The Most Important Step in Data Analysis

Lecture 17 Viewing/Inspecting DataFrames

Lecture 18 Renaming/Adding/Removing the DataFrame Columns

Lecture 19 Dropping Duplicate Rows

Lecture 20 Indexing DataFrame to Retrieve Specific Columns and Rows

Lecture 21 Merging/Concatenating/Joining DataFrames

Lecture 22 Dealing with Missing Values

Lecture 23 Filtering and Sorting of DataFrame

Lecture 24 Encoding/Mapping Existing Values – Part 1

Lecture 25 Encoding/Mapping Existing Values – Part 2

Lecture 26 Rescale/Standardize Column Values

Lecture 27 Common Cleaning Operations

Lecture 28 Exporting Datasets for Future Use

Lecture 29 Different Uses of Packages (Pandas, NumPy, SciPy, and Matplotlib)

Lecture 30 Types of Column Names/Features/Attributes in Structured Data

Lecture 31 Split-Apply-Combine (Performing Group By Operation)

Lecture 32 Descriptive Statistics Using Python – Part 1

Lecture 33 Descriptive Statistics Using Python – Part 2

Lecture 34 Using Visualizations

Lecture 35 Cool Visualization of Real-World Datasets of World Population Evolution

Lecture 36 Visualizations in Python – Part 1

Lecture 37 Visualizations in Python – Part 2

Lecture 38 Exploring an Online Visualization Tool (RAWGraphs)

Section 2: Exploratory Data Analysis with Pandas and Python 3.x

Lecture 39 The Course Overview

Lecture 40 Basic Statistical Measures

Lecture 41 Variance and Standard Deviation

Lecture 42 Visualizing Statistical Measures

Lecture 43 Calculating Percentiles

Lecture 44 Quartiles and Box Plots

Lecture 45 Finding Missing Values

Lecture 46 Dealing with Missing Values

Lecture 47 Hands-on with Dealing with Missing Values

Lecture 48 Case Study: Missing Data in Titanic Dataset

Lecture 49 What are Outliers?

Lecture 50 Using Z-scores to Find Outliers

Lecture 51 Modified Z-scores

Lecture 52 Using IQR to Detect Outliers

Lecture 53 Types of Variables

Lecture 54 Introduction to Univariate Analysis

Lecture 55 Skewness and Kurtosis

Lecture 56 Univariate Analysis over Olympics Dataset

Lecture 57 Introduction to Bivariate Analysis

Lecture 58 Correlation Coefficient

Lecture 59 Scatter Plots and Heatmaps

Lecture 60 Bivariate Analysis: Titanic Dataset

Lecture 61 Bivariate Analysis: Video Game Sales

Lecture 62 Introduction to Multivariate Analysis

Lecture 63 Multivariate Analysis over Titanic Dataset

Lecture 64 Multivariate Analysis over Pokemon Dataset

Lecture 65 Simpson’s Paradox

Lecture 66 Correlation Is Not Causation

Lecture 67 Wine Data Analysis: Initial Setup

Lecture 68 Red Wine Analysis

Lecture 69 White Wine Analysis

Lecture 70 White Wine versus Red Wine: Analysis

Section 3: Data Visualization with Python

Lecture 71 Course Overview

Lecture 72 Installation and Setup

Lecture 73 Introduction

Lecture 74 Overview of Statistics

Lecture 75 NumPy

Lecture 76 pandas

Lecture 77 Lesson Summary

Lecture 78 Lesson Overview

Lecture 79 Comparison Plots

Lecture 80 Relation Plots

Lecture 81 Composition Plots

Lecture 82 Distribution Plots

Lecture 83 Geo Plots

Lecture 84 What Makes a Good Visualization?

Lecture 85 Lesson Summary

Lecture 86 Lesson Overview

Lecture 87 Overview of Plots in Matplotlib

Lecture 88 Basic Text and Legend Functions

Lecture 89 Basic Plots

Lecture 90 Layouts

Lecture 91 Images

Lecture 92 Writing Mathematical Expressions

Lecture 93 Lesson Summary

Lecture 94 Lesson Overview

Lecture 95 Controlling Figure Aesthetics

Lecture 96 Color Palettes

Lecture 97 Interesting Plots in seaborn

Lecture 98 Multi-plots in seaborn

Lecture 99 Regression Plots

Lecture 100 Squarify

Lecture 101 Lesson Summary

Lecture 102 Lesson Overview

Lecture 103 Geoplotlib Basics

Lecture 104 Tile Providers

Lecture 105 Custom Layers

Lecture 106 Lesson Summary

Lecture 107 Lesson Overview

Lecture 108 Bokeh Basics

Lecture 109 Adding Widgets

Lecture 110 Lesson Summary

Section 4: Data Science Projects with Python

Lecture 111 Course Overview

Lecture 112 Installation and Setup

Lecture 113 Lesson Overview

Lecture 114 Python and the Anaconda Package Management System

Lecture 115 Different Types of Data Science Problems

Lecture 116 Loading the Case Study Data with Jupyter and pandas

Lecture 117 Getting Familiar with Data and Performing Data Cleaning

Lecture 118 Boolean Masks

Lecture 119 Data Quality Assurance and Exploration

Lecture 120 Deep Dive: Categorical Features

Lecture 121 Exploring the Financial History Features in the Dataset

Lecture 122 Lesson Summary

Lecture 123 Lesson Overview

Lecture 124 Exploring the Response Variable and Concluding the Initial Exploration

Lecture 125 Introduction to Scikit-Learn

Lecture 126 Model Performance Metrics for Binary Classification

Lecture 127 True Positive Rate, False Positive Rate, and Confusion Matrix

Lecture 128 Obtaining Predicted Probabilities from a Trained Logistic Regression Model

Lecture 129 Lesson Summary

Lecture 130 Lesson Overview

Lecture 131 Examining the Relationships between Features and the Response

Lecture 132 Finer Points of the F-test: Equivalence to t-test for Two Classes and Cautions

Lecture 133 Univariate Feature Selection: What It Does and Doesn't Do

Lecture 134 Generalized Linear Models (GLMs)

Lecture 135 Lesson Summary

Lecture 136 Lesson Overview

Lecture 137 Estimating the Coefficients and Intercepts of Logistic Regression

Lecture 138 Assumptions of Logistic Regression

Lecture 139 How Many Features Should You Include?

Lecture 140 Lasso (L1) and Ridge (L2) Regularization

Lecture 141 Cross Validation: Choosing the Regularization Parameter and Other Hyperparameter

Lecture 142 Reducing Overfitting on the Synthetic Data Classification Problem

Lecture 143 Options for Logistic Regression in Scikit-Learn

Lecture 144 Lesson Summary

Lecture 145 Lesson Overview

Lecture 146 Decision Trees

Lecture 147 Training Decision Trees: Node Impurity

Lecture 148 Using Decision Trees: Advantages and Predicted Probabilities

Lecture 149 Random Forests: Ensembles of Decision Trees

Lecture 150 Fitting a Random Forest

Lecture 151 Lesson Summary

Lecture 152 Lesson Overview

Lecture 153 Review of Modeling Results

Lecture 154 Dealing with Missing Data: Imputation Strategies

Lecture 155 Cleaning the Dataset

Lecture 156 Mode and Random Imputation of PAY_1

Lecture 157 A Predictive Model for PAY_1

Lecture 158 Using the Imputation Model and Comparing it to Other Methods

Lecture 159 Financial Analysis

Lecture 160 Final Thoughts on Delivering the Predictive Model to the Client

Lecture 161 Lesson Summary

This course is for Python developers, data analysts, and IT professionals who want to progress in their careers as fully-fledged data scientists/analytics experts.,Also, anyone who wants to use data analytics/machine learning to enrich their current personal or professional projects will also benefit from the course.