< Complete Python & Data Science Course for Absolute Beginners

161 casts | 26:40:19 for the total course

created by Mammoth Interactive INC

  • 1. Python Introduction
  • 2. Code Python on the Web

    • 02.01 What is Google Colab

    • 02.02 What If I Get Errors

    • 02.03 How Do I Terminate a Session

  • 3. Python Language Fundamentals: Learn Python from Scratch

    • 01. Variables

    • 02. Type Conversion Examples

    • 03. Operators

    • 04. Collections

    • 05. List Examples

    • 06. Tuples Examples

    • 07. Dictionaries Examples

    • 08. Ranges Examples

    • 09. Conditionals

    • 10. If Statement Examples

    • 11. Loops

    • 12. Functions

    • 13. Parameters And Return Values Examples

    • 14. Classes And Objects

    • 15. Inheritance Examples

    • 16. Static Members Examples

    • 17. Summary And Outro

  • 4. Data Visualization with Python and Matplotlib

    • 00. Course Intro

    • 01. Intro To Pyplot

    • 02. Installing Matplotlib

    • 03. Basic Line Plot

    • 04. Customizing Graphs

    • 05. Plotting Multiple Datasets

    • 06. Bar Chart

    • 07. Pie Chart

    • 08. Histogram

    • 09. 3D Plotting

    • 10. Course Outro

  • 5. Beginners Data Analysis with Pandas

    • 00. Panda Course Introduction

    • 01. Intro To Pandas

    • 02. Installing Pandas

    • 03. Creating Pandas Series

    • 04. Date Ranges

    • 05. Getting Elements From Series

    • 06. Getting Properties Of Series

    • 07. Modifying Series

    • 08. Operations On Series

    • 09. Creating Pandas Dataframes

    • 10. Getting Elements From Dataframes

    • 11. Getting Properties From Dataframes

    • 12. Dataframe Modification

    • 13. Dataframe Operations

    • 14 Dataframe Comparisons And Iteration

    • 15. Reading CSVs

    • 16. Summary And Outro

  • 6. Data Mining with Python! Real-Life Data Science Exercises

    • Introduction to Data Mining

  • 7. 2-1 Data Wrangling - A Complete Overview

    • Data Wrangling Demystified

  • 8. 2-2 Data Mining Fundamentals

    • 01. Cluster Analysis

    • 02. Classification and Regression

    • 03. Association and Correlation

    • 04. Dimensionality Reduction

  • 9. 2-3 Frameworks Explained - Taming Big Data with Spark

    • 01. Apache Spark - An Overview Of The Framework

    • 02. Spark Key Functions

    • 03. Spark Machine Learning

    • 04. EXAMPLES - Using The Machine Learning Pipeline

  • 10. 2-4 EXAMPLES - Mining and Storing Data

    • 01. Text Mining

    • 02. Network Mining

    • 03. Matrix

    • 04. SQL

  • 11. 2-5 NLP (Natural Language Processing)

    • 01 NLP Data Cleaning

    • 02. Count Vectorizer

    • 03. NLP Example with Spam

    • 04. Tweak Model with Spam Data

    • 05. Pipeline with Spam Data

  • 12. 2-6 Conclusion and Summary

    • 06. Conclusion and Challenge

  • 13. PySpark - Build DataFrames with Python, Apache Spark and SQL

    • 00 Project Preview

    • 02 What Are Resilient Distributed Datasets

    • 01 What Is Apache Spark

    • 03A What Is A Dataframe

    • 03B What You'll Need

  • 14. PySpark - Build DataFrames from Spreadsheets

    • 04 Start A Spark Session

    • 05 Load Data As A CSV

    • 06 Perform Basic Dataframe Operations

    • 07 Format Dataframe Table

    • 08 Perform Dataframe Math Operations

    • 09 Perform Dataframe Queries

    • 10 Build SQL Queries With Spark

  • 15. Python Data Analysis Bootcamp with Pandas and NLTK

    • 00 Project Preview

    • 01 Convert CSV File To A Python List

    • 02 Tokenize Text Data

    • 03 Find Most Popular Lemmatized Words

    • 04 Build Dataframes Per Part Of Speech

    • 05 Plot Word Frequency

  • 16. Exploratory Data Analysis Bootcamp with Python, Seaborn and Pandas

    • 00 Project Preview

    • 01 Load A Dataset

    • 02 Analyze The Main Feature

    • 03 Analyze Numerical Features

    • 04 Analyze Categorical Features

  • 17. Visualize - Exploratory Data Analysis Bootcamp with Python, Seaborn and Pandas

    • 01 Find Relationships Between Numerical Features

    • 02 Find Relationships Between Categorical Features

    • 03 Build Conditional Plots

  • 18. Overview - Introduction to Databases with Python SQL

    • 00 Course Overview

    • 01 What You'll Need

  • 19. 01 Introduction to data

    • 01 Why You Must Know How To Work With Data

  • 20. 02 Entity Relationship Modeling (ERM)

    • 01 How To Read An ER Model

  • 21. 03 Introduction to databases and relational databases

    • 01 What Is A Database

    • 02 What Is A Relational Database

  • 22. 04 How to build an organized database

    • 01 How To Design Columns And Data Types

    • 02 Use Normal Forms To Check Your Design

  • 23. 05 Build a SQLite database with Python

    • 01 Build A Sqlite Database With Python

    • 02 Add An Entry To The Table With SQL

    • 03 Add More Records To The Table

    • 04 Build A Second Table For Cross-Referencing

    • 05 Select Rows That Meet Conditions

  • 24. Feature Analysis and Data Science with Stocks for Beginners

    • Course Overview

    • 01 Load And Create Data

    • 02 Perform Exploratory Data Analysis

    • 03 Visualize Data With Different Plots

    • 04 Analyze Features With More Plots

    • 05 Build Plots With Seaborn

    • 06 Build A Bokeh Plot

    • 07 Build A 3D Scatter Plot

    • 08 Rank Feature Importance

    • 09 Compare Positive And Negative Returns

  • 25. The Definitive Python Time Series Analysis Masterclass

    • 00 Project Preview

    • 01 Load Crypto Prices Dataset

    • 02 Visualize Bitcoin Price Trend

    • 03 Predict Price With Facebook Prophet

    • 04 Analyze Model Performance

    • 05 Visualize Model Results

    • 06 Predict Monthly Trend

    • 07 Predict Weekly Trend

    • 08 Compare Final Stock Price Of Different Strategies

  • 26. 1). Stock Market Data Analysis and Visualization

    • 00 Project Preview

    • 01 Fetch Stock Data

    • 02 Visualize Stock Data Features

    • 03 Calculate Daily Return

    • 04 Compare Returns Of Different Stocks

    • 05 Compare Closing Prices

  • 27. 2). Stock Market Data Analysis and Visualization

    • 01 Visualize Standard Deviation And Expected Returns

    • 02 Calculate Value At Risk

    • 03 Monte Carlo Analysis To Estimate Risk

    • 04 Visualize Price Distribution

  • 28. Scrape the Web - Python and Beautiful Soup Bootcamp

    • 00 What Is Web Scraping

    • 01 What You'll Need

    • 02 Build An Html Webpage To Scrape

    • 03 Select Data Structures From A Webpage

    • 04 Extract URLsAnd Text

    • 05 Work With Tags

    • 06 Work With Attributes

    • 07 Add Navigation To A String

    • 08 Navigate Html Contents

    • 09 Find All Filter

  • 29. Build Interactive Python Dashboards with Plotly and Dash

    • 01 Project Preview

    • 02 What Is Plotly And Dash

    • 03 What You'll Need

    • 1-01 Build A Dash App

    • 1-02 Build A Graph In The Dash App

    • 2-01 Load Data From Vega Datasets

    • 2-02 Build The Layout

    • 2-03 Build A Chart With Altair

  • 30. Data Mining with Python and NumPy - Build a Video Recommender System

    • 00 Project Preview

    • 01 Build A Dataset

    • 02 Compute Support And Confidence - If A Person Watches X, They Will Watch Y

    • 03 Compute Support And Confidence For All Channels

    • 04 Determine Which Videos Are Best To Recommend

00. Introduction

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