Free Applied Data Science with Python Course

Data Science with Python

• Lesson 01: Course Introduction

  09:05

  • 1.01 Course Introduction

    05:54

  • 1.02 Demo Jupyter Lab Walk - Through

    03:11

• Lesson 02: Introduction to Data Science

  09:10

  • 2.01 Learning Objectives

    00:27

  • 2.02 Data Science Methodology

    01:20

  • 2.03 From Business Understanding to Analytic Approach

    01:02

  • 2.04 From Requirements to Collection

    01:06

  • 2.05 From Understanding to Preparation

    01:10

  • 2.06 From Modeling to Evaluation

    01:53

  • 2.07 From Deployment to Feedback

    01:52

  • 2.08 Key Takeaways

    00:20

• Lesson 03: Python Libraries for Data Science

  01:59:39

  • 3.01 Learning Objectives

    00:34

  • 3.02 Python Libraries for Data Science

    01:51

  • 3.03 Import Library into Python Program

    01:05

  • 3.04 Numpy

    04:35

  • 3.05 Demo Numpy

    05:08

  • 3.06 Fundamentals of Numpy

    02:13

  • 3.07 Numpy Array Shapes and axes Part A

    02:48

  • 3.08 Numpy Array Shapes and axes Part B

    03:22

  • 3.09 Arithmetic Operations

    02:34

  • 3.10 Conditional Statements in Python

    02:44

  • 3.11 Common Mathematical and Statistical Functions in NumPy

    04:25

  • 3.12 Indexing and Slicing in Python Part A

    02:26

  • 3.13 Indexing and Slicing in Python Part B

    02:25

  • 3.14 Introduction to Pandas

    01:41

  • 3.15 Introduction to Pandas Series

    03:37

  • 3.16 Querying a Series

    03:54

  • 3.17 Pandas Dataframe

    02:53

  • 3.18 Introduction to Pandas Panel

    01:45

  • 3.19 Common Functions in Pandas

    02:20

  • 3.20 Statistical Functions in Pandas

    01:43

  • 3.21 Date and Timedelta

    02:18

  • 3.22 IO Tools

    02:36

  • 3.23 Categorical Data

    02:09

  • 3.24 Working with Text Data

    02:34

  • 3.25 Iteration

    01:54

  • 3.26 Plotting with Pandas

    03:23

  • 3.27 Matplotlib

    06:04

  • 3.28 Demo Matplotlib

    02:09

  • 3.29 Data Visualization Libraries in Python Matplotlib

    01:30

  • 3.30 Graph Types

    01:14

  • 3.31 Using Matplotlib to Plot Graphs

    03:32

  • 3.32 Matplotlib for 3d Visualization

    02:14

  • 3.33 Using Matplotlib with Other Python Packages

    01:02

  • 3.34 Data Visualization Libraries in Python Seaborn An Introduction

    00:58

  • 3.35 Seaborn Visualization Features

    02:13

  • 3.36 Using Seaborn to Plot Graphs

    01:40

  • 3.37 Analysis using seaborn plots

    00:53

  • 3.38 Plotting 3D Graphs for Multiple Columns using Seaborn

    03:16

  • 3.39 SciPy

    05:23

  • 3.40 Demo Scipy

    01:38

  • 3.41 Scikit-learn

    02:08

  • 3.42 Scikit Models

    01:25

  • 3.43 Scikit Datasets

    01:12

  • 3.44 Preprocessing Data in Scikit Learn Part 1

    01:28

  • 3.45 Preprocessing Data in Scikit Learn Part 2

    01:45

  • 3.46 Preprocessing Data in Scikit Learn Part 3

    02:04

  • 3.47 Demo Scikit learn

    06:20

  • 3.48 Key Takeaways

    00:34

• Lesson 04: Statistics

  02:29:57

  • 4.01 Learning Objectives

    00:34

  • 4.02 Introduction to Linear Algebra

    02:09

  • 4.03 Scalars and vectors

    01:27

  • 4.04 Dot product of Two Vectors

    02:02

  • 4.05 Linear Independence of Vectors

    00:46

  • 4.06 Norm of a Vector

    01:33

  • 4.07 Matrix

    02:46

  • 4.08 Matrix Operations

    02:38

  • 4.09 Transpose of a Matrix

    00:47

  • 4.10 Rank of a Matrix

    01:45

  • 4.11 Determinant of a matrix and Identity matrix or operator

    02:15

  • 4.12 Inverse of a matrix and Eigenvalues and Eigenvectors

    02:10

  • 4.13 Calculus in Linear Algebra

    01:14

  • 4.14 Importance of Statistics for Data Science

    02:00

  • 4.15 Common Statistical Terms

    01:19

  • 4.16 Types of Statistics

    02:10

  • 4.17 Data Categorization and types of data

    02:40

  • 4.18 Levels of Measurement

    02:04

  • 4.19 Measures of central tendency mean

    01:33

  • 4.20 Measures of Central Tendency Median

    01:37

  • 4.21 Measures of Central Tendency Mode

    01:03

  • 4.22 Measures of Dispersion

    01:56

  • 4.23 Variance

    02:14

  • 4.24 Random Variables

    01:36

  • 4.25 Sets

    02:03

  • 4.26 Measure of Shape Skewness

    01:38

  • 4.27 Measure of Shape Kurtosis

    01:20

  • 4.28 Covariance and corelation

    02:11

  • 4.29 Basic Statistics with Python Problem Statement

    00:49

  • 4.30 Basic Statistics with Python Solution

    10:30

  • 4.31 Probability its Importance and Probability Distribution

    02:49

  • 4.32 Probability Distribution Binomial Distribution

    02:13

  • 4.33 Binomial Distribution using Python

    01:31

  • 4.34 Probability Distribution Poisson Distribution

    02:08

  • 4.35 Poisson Distribution Using Python

    01:20

  • 4.36 Probability Distribution Normal Distribution

    03:17

  • 4.37 Probability Distribution Uniform Distribution

    01:03

  • 4.38 Probability Distribution Bernoulli Distribution

    02:27

  • 4.39 Probability Density Function and Mass Function

    01:57

  • 4.40 Cumulative Distribution Function

    01:52

  • 4.41 Central Limit Theorem

    02:22

  • 4.42 Bayes Theorem

    01:50

  • 4.43 Estimation Theory

    02:09

  • 4.44 Point Estimate using Python

    00:45

  • 4.45 Distribution

    01:11

  • 4.46 Kurtosis Skewness and Student's T- distribution

    01:46

  • 4.47 Hypothesis Testing and mechanism

    01:59

  • 4.48 Hypothesis Testing Outcomes Type I and II Errors

    01:28

  • 4.49 Null Hypothesis and Alternate Hypothesis

    01:27

  • 4.50 Confidence Intervals

    01:32

  • 4.51 Margin of Errors

    01:21

  • 4.52 Confidence Levels

    01:05

  • 4.53 T test and P values Using Python

    04:39

  • 4.54 Z test and P values Using Python

    05:25

  • 4.55 Comparing and Contrastin T test and Z-tests

    02:54

  • 4.56 Chi Squared Distribution

    02:32

  • 4.57 Chi Squared Distribution using Python

    03:18

  • 4.58 Chi squared Test and Goodness of Fit

    02:16

  • 4.59 ANOVA

    02:05

  • 4.60 ANOVA Terminologies

    01:31

  • 4.61 Assumptions and Types of ANOVA

    02:19

  • 4.62 Partition of Variance

    02:32

  • 4.63 F-distribution

    02:01

  • 4.64 F Distribution using Python

    03:54

  • 4.65 F-Test

    02:32

  • 4.66 Advanced Statistics with Python Problem Statement

    00:54

  • 4.67 Advanced Statistics with Python Solution

    10:06

  • 4.68 Key Takeaways

    00:38

• Lesson 05: Data Wrangling

  31:32

  • 5.01 Learning Objectives

    00:42

  • 5.02 Data Exploration Loading Files Part A

    02:53

  • 5.03 Data Exploration Loading Files Part B

    01:36

  • 5.04 Data Exploration Techniques Part A

    02:44

  • 5.05 Data Exploration Techniques Part B

    02:48

  • 5.06 Seaborn

    02:19

  • 5.07 Demo Correlation Analysis

    02:38

  • 5.08 Data Wrangling

    01:28

  • 5.09 Missing Values in a Dataset

    01:57

  • 5.10 Outlier Values in a Dataset

    01:50

  • 5.11 Demo Outlier and Missing Value Treatment

    04:12

  • 5.12 Data Manipulation

    00:49

  • 5.13 Functionalities of Data Object in Python Part A

    01:50

  • 5.14 Functionalities of Data Object in Python Part B

    01:34

  • 5.15 Different Types of Joins

    01:34

  • 5.16 Key Takeaways

    00:38

• Lesson 06: Feature Engineering

  06:57

  • 6.01 Learning Objectives

    00:28

  • 6.02 Introduction to Feature Engineering

    01:50

  • 6.03 Encoding of Catogorical Variables

    00:27

  • 6.04 Label Encoding

    01:46

  • 6.05 Techniques used for Encoding variables

    02:11

  • 6.06 Key Takeaways

    00:15

• Lesson 07: Exploratory Data Analysis

  24:58

  • 7.01 Learning Objectives

    00:33

  • 7.02 Types of Plots

    09:38

  • 7.03 Plots and Subplots

    10:06

  • 7.04 Assignment 01 Pairplot Demo

    02:28

  • 7.05 Assignment 02 Pie Chart Demo

    01:52

  • 7.06 Key Takeaways

    00:21

• Lesson 08: Feature Selection

  06:15

  • 8.01 Learning Objectives

    00:33

  • 8.02 Feature Selection

    01:28

  • 8.03 Regression

    00:54

  • 8.04 Factor Analysis

    01:58

  • 8.05 Factor Analysis Process

    01:07

  • 8.06 Key Takeaways

    00:15