Course Description

  • What are the Key Learning Objectives of this course?

    Key Learning Objectives are:

    • Import data sets
    • Clean and prepare data for analysis
    • Manipulate pandas DataFrame
    • Summarize data
    • Build machine learning models using scikit-learn
    • Build data pipelines
       

Course Preview

    • Lesson 1 - Course Introduction

      • Course Objectives
      • Course Prerequisites
      • Why Python for Data Analytics?
      • Course Outline
    • Lesson 2 - Python Environment Setup and Essentials

      • Learning Objectives
      • Data Types with Python
      • Basic Operators and Functions
      • Expressions and Variables
      • String Operations
      • Lists and tuples
      • Sets
      • Dictionaries
      • Generate Report Cards for Students
    • Lesson 3 - Python Programming Fundamentals

      • Learning Objectives
      • Conditions and Branching
      • Loops
      • Functions
      • Objects and Classes
      • Tic-Tac-Toe Game
    • Lesson 4 - Data Analytics Overview

      • Learning Objectives
      • Data Analytics Process
      • Exploratory Data Analysis(EDA)
      • EDA-Quantitative Technique
      • EDA - Graphical Technique
      • Data Analytics Conclusion or Predictions
      • Data Analytics Communication
      • Data Types for Plotting
    • Lesson 5 - Statistical Computing

      • Learning Objectives
      • Introduction to Statistics
      • Statistical and Non-statistical Analysis
      • Major Categories of Statistics
      • Statistical Analysis Considerations
      • Population and Sample
      • Statistical Analysis Process
      • Data Distribution
      • Probability density function
      • Central limit theorem
      • Confidence interval
      • Null hypothesis and alternate hypothesis
      • Hypothesis test
      • Dispersion
      • What is design of experiments?
      • What is A/B testing?
      • Why A/B testing?
      • Steps involved in A/B testing?
      • When to use A/B testing and when not to use?
      • A/B Testing
    • Lesson 6 - Mathematical Computing using NumPy

      • Learning Objectives
      • Introduction to NumPy
      • Class and Attributes of ndarray
      • Copy and Views
      • Mathematical Functions of Numpy
      • Country GDP
      • Olympic 2012 Medal Tally
    • Lesson 7 - Data Manipulation with Pandas

      • Learning Objectives
      • Introduction to pandas library
      • Main data structures
      • File read and write support
      • Analyzing he Datasets
      • Analyze the Federal Aviation Authority Dataset using Pandas
    • Lesson 8 - Data visualization with Python

      • Learning objectives
      • Introduction to data visualization
      • Introduction to visualization libraries (Matplotlib, seaborn, plotly)
      • Line properties
      • Matplotlib architecture
      • Types of plots
      • Introduction to Pyplot
      • Seaborn and regression plots
      • Introduction to Folium
      • Maps with markers
      • Kernel Density Estimate Plots
      • Analyzing Variables Individually
      • Relationships between Variables
      • Visualize the Sales Data
    • Lesson 9 - Intro to Model Building

      • Learning objectives
      • Introduction to Scikit-Learn library
      • Supervised learning model considerations
      • Supervised learning Models: Linear Regression
      • Model persistence and evaluation
      • Create a Model to Predict the Sales Outcome
      • List the Glucose Levels Readings
    • Practice Project

      • Bike-Sharing Demand Analysis
    • Lesson 1 - Welcome

      02:28
      • Welcome
        02:28
      • Learning Objectives
    • Lesson 2 - Python Basics

      11:55
      • 2.1 Learning Objectives
      • 2.2 Your first program
        01:15
      • 2.3 Types
        02:57
      • 2.4 Expressions and Variables
        03:50
      • 2.5 Write your First Python Code
      • 2.6 String Operations
        03:53
      • 2.7 String Operations
    • Lesson 3 - Python Data Structures

      16:22
      • 3.1 Learning Objectives
      • 3.2 Lists and Tuples
        08:46
      • 3.3 Lists and Tuples
      • 3.4 Sets
        05:12
      • 3.5 Sets
      • 3.6 Dictionaries
        02:24
      • 3.7 Dictionaries
    • Lesson 4 - Python Programming Fundamentals

      41:08
      • 4.1 Learning Objectives
      • 4.2 Conditions and Branching
        10:13
      • 4.3 Conditions and Branching
      • 4.4 Loops
        06:40
      • 4.5 Loops
      • 4.6 Functions
        13:28
      • 4.7 Functions
      • 4.8 Objects and Classes
        10:47
      • 4.9 Objects and Classes
    • Lesson 5 - Working with Data in Python

      12:35
      • 5.1 Learning Objectives
      • 5.2 Reading files with open
        03:38
      • 5.3 Reading Files
      • 5.4 Writing files with open
        02:49
      • 5.5 Writing Files
      • 5.6 Loading data with Pandas
        04:07
      • 5.7 Working with and Saving data with Pandas
        02:01
      • 5.8 Loading Data and Viewing Data
    • Lesson 6 - Working with Numpy Arrays

      18:26
      • 6.1 Learning Objectives
      • 6.2 Numpy One-Dimensional Arrays
        11:18
      • 6.3 Working with One-Dimensional Numpy Arrays
      • 6.4 Numpy Two-Dimensional Arrays
        07:08
      • 6.5 Working with Two-Dimensional Numpy Arrays
    • Lesson 7 - Course Summary

      01:13
      • Course Summary
        01:13
      • Unlocking IBM Certificate
    • Lesson 1 Welcome

      03:01
      • Learning Objectives
      • Welcome
        03:01
    • Lesson 2 Introduction to Visualization Tools

      22:05
      • Learning Objectives
      • Introduction to Data Visualization
        04:36
      • Introduction to Matplotlib
        06:26
      • Basic Plotting with Matplotlib
        04:39
      • Dataset on Immigration to Canada
        02:43
      • Line Plots
        03:41
      • Arrays & Matrices
    • Lesson 3 Basic Visualization Tools

      13:13
      • Learning Objectives
      • Area Plots
        04:45
      • Histograms
        04:58
      • Bar Charts
        03:30
      • Area Plots, Histograms, and Bar Charts
    • Lesson 4 Specialized Visualization Tools

      12:13
      • Learning Objectives
      • Pie Charts
        04:14
      • Box Plots
        03:42
      • Scatter Plots
        04:17
      • Pie Charts, Box Plots, Scatter Plots, and Bubble Plots
    • Lesson 5 Advanced Visualization Tools

      05:22
      • Learning Objectives
      • Waffle Charts
        01:28
      • Word Clouds
        01:28
      • Seaborn Regression Plots
        02:26
      • Waffle Charts, Word Clouds, and Regression Plots
    • Lesson 6 Creating Maps and Visualizing Geospatial Data

      09:17
      • Learning Objectives
      • Introduction to Folium
        02:35
      • Maps with Markers
        02:21
      • Choropleth Maps
        04:21
      • Generating Maps in Python
      • Unlocking IBM Certificate
    • Lesson 1 Learning Objectives

      • Learning Objectives
    • Lesson 2_Introduction

      17:53
      • Learning Objectives
      • Introduction to Data Analysis with Python
        00:50
      • The Problem
        01:51
      • Understanding the Data
        02:26
      • Python Packages for Data Science
        02:28
      • Importing and Exporting Data in Python
        04:13
      • Getting Started Analyzing Data in Python
        04:14
      • Introduction
    • Lesson 3 Data Wrangling

      18:50
      • Learning Objectives
      • Pre-processing Data in Python
        02:09
      • Dealing with Missing Values in Python
        05:57
      • Data Formatting in Python
        03:23
      • Data Normalization in Python
        03:34
      • Binning in Python
        01:47
      • Turning Categorical into Quantitative Variables
        02:00
      • REVIEW DATA WRANGLING
    • Lesson 4 Exploratory Data Analysis

      18:23
      • Learning Objectives
      • Exploratory Data Analysis
        01:20
      • Descriptive Statistics
        04:39
      • GroupBy in Python
        03:20
      • Analysis of Variance (ANOVA)
        03:58
      • Correlation
        02:29
      • Correlation - Statistics
        02:37
      • REVIEW: EXPLORATORY DATA ANALYSIS
    • Lesson 5 Model Development

      26:07
      • Learning Objectives
      • Introduction
        01:44
      • Simple and Multiple Linear Regression
        06:34
      • Model Evaluation using Visualization
        04:44
      • Polynomial Regression and Pipelines
        04:25
      • Measures for In-Sample Evaluation
        03:37
      • Prediction and Decision Making
        05:03
      • REVIEW-MODEL DEVELOPMENT
    • Lesson 6 Model Evaluation

      21:10
      • Learning Objectives
      • Model Evaluation and Refinement
        00:21
      • Model Evaluation
        07:30
      • Overfitting Underfitting and Model Selection
        04:20
      • Ridge Regression
        04:26
      • Grid Search
        04:33
      • MODEL EVALUATION AND REFINEMENT
      • Unlocking IBM Certificate
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    • Disclaimer
    • PMP, PMI, PMBOK, CAPM, PgMP, PfMP, ACP, PBA, RMP, SP, and OPM3 are registered marks of the Project Management Institute, Inc.