- Data wrangling
- Data exploration
- Data visualization
- Mathematical computing
- Web scraping
- Hypothesis building
- Python programming concepts
- NumPy and SciPy package
- ScikitLearn package for Natural Language Processing

- Analytics Professionals
- Software Professionals
- IT Professionals
- Data Scientist
- Data Analyst

### Data Science with Python

#### Lesson 01 - Course Introduction

04:59##### 1.01 Course Introduction

03:06##### 1.02 What you will Learn

01:53

#### Lesson 02 - Introduction to Data Science

08:16##### 2.01 Introduction

00:44##### 2.02 Data Science and its Applications

02:41##### 2.03 The Data Science Process: Part 1

02:15##### 2.04 The Data Science Process: Part 2

02:02##### 2.05 Recap

00:34

#### Lesson 03 - Essentials of Python Programming

01:00:18##### 3.01 Introduction

00:58##### 3.02 Setting Up Jupyter Notebook: Part 1

02:02##### 3.03 Setting Up Jupyter Notebook: Part 2

04:14##### 3.04 Python Functions

03:57##### 3.05 Python Types and Sequences

04:50##### 3.06 Python Strings Deep Dive

07:16##### 3.07 Python Demo: Reading and Writing csv files

06:25##### 3.08 Date and Time in Python

02:34##### 3.09 Objects in Python Map

07:42##### 3.10 Lambda and List Comprehension

03:53##### 3.11 Why Python for Data Analysis?

02:09##### 3.12 Python Packages for Data Science

02:44##### 3.13 StatsModels Package: Part 1

02:38##### 3.14 StatsModels Package: Part 2

03:29##### 3.15 Scipy Package

02:47##### 3.16 Recap

00:51##### 3.17 Spotlight

01:49

#### Lesson 04 - NumPy

28:19##### 4.01 Introduction

00:51##### 4.02 Fundamentals of NumPy

02:49##### 4.03 Array shapes and axes in NumPy: Part A

03:27##### 4.04 NumPy Array Shapes and Axes: Part B

03:28##### 4.05 Arithmetic Operations

02:35##### 4.06 Conditional Logic

02:48##### 4.07 Common Mathematical and Statistical Functions in Numpy

04:29##### 4.08 Indexing And Slicing: Part 1

02:27##### 4.09 Indexing and Slicing: Part 2

02:28##### 4.10 File Handling

02:24##### 4.11 Recap

00:33

#### Lesson 05 - Linear Algebra

28:29##### 5.01 Introduction

00:51##### 5.02 Introduction to Linear Algebra

02:46##### 5.03 Scalars and Vectors

01:50##### 5.04 Dot Product of Two Vectors

02:37##### 5.05 Linear independence of Vectors

01:05##### 5.06 Norm of a Vector

01:30##### 5.07 Matrix

03:28##### 5.08 Matrix Operations

03:14##### 5.09 Transpose of a Matrix

00:59##### 5.10 Rank of a Matrix

02:11##### 5.11 Determinant of a matrix and Identity matrix or operator

02:51##### 5.12 Inverse of a matrix and Eigenvalues and Eigenvectors

02:45##### 5.13 Calculus in Linear Algebra

01:34##### 5.14 Recap

00:48

#### Lesson 06 - Statistics Fundamentals

33:50##### 6.01 Introduction

01:00##### 6.02 Importance of Statistics with Respect to Data Science

02:34##### 6.03 Common Statistical Terms

01:46##### 6.04 Types of Statistics

02:50##### 6.05 Data Categorization and Types

03:20##### 6.06 Levels of Measurement

02:37##### 6.07 Measures of Central Tendency

01:51##### 6.08 Measures of Central Tendency

01:48##### 6.09 Measures of Central Tendency

01:02##### 6.10 Measures of Dispersion

02:19##### 6.11 Random Variables

02:17##### 6.12 Sets

02:40##### 6.13 Measures of Shape (Skewness)

02:16##### 6.14 Measures of Shape (Kurtosis)

01:52##### 6.15 Covariance and Correlation

02:44##### 6.16 Recap

00:54

#### Lesson 07 - Probability Distribution

30:18##### 7.01 Introduction

01:02##### 7.02 Probability,its Importance, and Probability Distribution

03:36##### 7.03 Probability Distribution : Binomial Distribution

02:53##### 7.04 Probability Distribution: Poisson Distribution

02:29##### 7.05 Probability Distribution: Normal Distribution

04:19##### 7.06 Probability Distribution: Uniform Distribution

01:30##### 7.07 Probability Distribution: Bernoulli Distribution

03:05##### 7.08 Probability Density Function and Mass Function

02:33##### 7.09 Cumulative Distribution Function

02:26##### 7.10 Central Limit Theorem

02:57##### 7.11 Estimation Theory

02:49##### 7.12 Recap

00:39

#### Lesson 08 - Advanced Statistics

01:07:21##### 8.01 Introduction

01:07##### 8.02 Distribution

01:45##### 8.03 Kurtosis Skewness and Student's T-distribution

02:32##### 8.04 Hypothesis Testing and Mechanism

02:25##### 8.05 Hypothesis Testing Outcomes: Type I and II Errors

01:54##### 8.06 Null Hypothesis and Alternate Hypothesis

01:47##### 8.07 Confidence Intervals

02:01##### 8.08 Margins of error

01:49##### 8.09 Confidence Level

01:31##### 8.10 T - Test and P - values (Lab)

04:50##### 8.11 Z - Test and P - values

05:33##### 8.12 Comparing and Contrasting T test and Z test

03:45##### 8.13 Bayes Theorem

02:24##### 8.14 Chi Sqare Distribution

03:16##### 8.15 Chi Square Distribution : Demo

03:25##### 8.16 Chi Square Test and Goodness of Fit

02:46##### 8.17 Analysis of Variance or ANOVA

02:41##### 8.18 ANOVA Termonologies

02:08##### 8.19 Assumptions and Types of ANOVA

02:53##### 8.20 Partition of Variance using Python

03:06##### 8.21 F - Distribution

02:41##### 8.22 F - Distribution using Python

03:59##### 8.23 F - Test

03:09##### 8.24 Recap

01:19##### 8.25 Spotlight

02:35

#### Lesson 09 - Pandas

41:13##### 9.01 Introduction

00:52##### 9.02 Introduction to Pandas

02:15##### 9.03 Pandas Series

03:37##### 9.04 Querying a Series

04:01##### 9.05 Pandas Dataframes

03:05##### 9.06 Pandas Panel

01:46##### 9.07 Common Functions In Pandas

02:56##### 9.08 Pandas Functions Data Statistical Function, Windows Function

02:18##### 9.09 Pandas Function Data and Timedelta

02:57##### 9.10 IO Tools Explain all the read function

03:15##### 9.11 Categorical Data

02:52##### 9.12 Working with Text Data

03:15##### 9.13 Iteration

02:37##### 9.14 Sorting

01:19##### 9.15 Plotting with Pandas

03:23##### 9.16 Recap

00:45

#### Lesson 10 - Data Analysis

32:25##### 10.01 Introduction

00:46##### 10.02 Understanding Data

02:31##### 10.03 Types of Data Structured Unstructured Messy etc

02:35##### 10.04 Working with Data Choosing appropriate tools, Data collection, Data wrangling

02:53##### 10.05 Importing and Exporting Data in Python

02:42##### 10.06 Regular Expressions in Python

08:24##### 10.07 Manipulating text with Regular Expressions

06:04##### 10.08 Accessing databases in Python

03:32##### 10.09 Recap

00:50##### 10.10 Spotlight

02:08

#### Lesson 11 - Data Wrangling

34:24##### 11.01 Introduction

00:58##### 11.02 Pandorable or Idiomatic Pandas Code

06:21##### 11.03 Loading Indexing and Reindexing

02:45##### 11.04 Merging

05:48##### 11.05 Memory Optimization in Python

03:01##### 11.06 Data Pre Processing: Data Loading and Dropping Null Values

02:34##### 11.07 Data Pre-processing Filling Null Values

02:32##### 11.08 Data Binning Formatting and Normalization

04:46##### 11.09 Data Binning Standardization

02:19##### 11.10 Describing Data

02:17##### 11.11 Recap

01:03

#### Lesson 12 - Data Visualization

42:55##### 12.01 Introduction

00:58##### 12.02 Principles of information visualization

02:27##### 12.03 Visualizing Data using Pivot Tables

02:04##### 12.04 Data Visualization Libraries in Python Matplotlib

01:56##### 12.05 Graph Types

01:36##### 12.06 Data Visualization Libraries in Python Seaborn

01:15##### 12.07 Data Visualization Libraries in Python Seaborn

02:34##### 12.08 Data Visualization Libraries in Python Plotly

01:07##### 12.09 Data Visualization Libraries in Python Plotly

02:51##### 12.10 Data Visualization Libraries in Python Bokeh

02:16##### 12.11 Data Visualization Libraries in Python Bokeh

01:59##### 12.12 Using Matplotlib to Plot Graphs

03:32##### 12.13 Plotting 3D Graphs for Multiple Columns using Matplotlib

02:14##### 12.14 Using Matplotlib with other python packages

03:30##### 12.15 Using Seaborn to Plot Graphs

02:18##### 12.16 Using Seaborn to Plot Graphs

01:15##### 12.17 Plotting 3D Graphs for Multiple Columns Using Seaborn

03:16##### 12.18 Introduction to Plotly

03:29##### 12.19 Introduction to Bokeh

01:32##### 12.20 Recap

00:46

#### Lesson 13 - End to End Statistics Application with Python

35:34##### 13.01 Introduction

01:05##### 13.02 Basic Statistics with Python Problem Statement

01:06##### 13.03 Basic Statistics with Python Solution

11:16##### 13.04 Scipy for Statistics Problem Statement

01:11##### 13.05 Scipy For Statistics Solution

06:10##### 13.06 Advanced Statistics Python

01:10##### 13.07 Advanced Statistics with Python Solution

10:56##### 13.08 Recap

00:29##### 13.09 Spotlight

02:11

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In the data science field require Python

Growth in demand for data science professionals

- Average Salary
### $62K - $96K Per Annum

Hiring Companies - Average Salary
### $100K - $217K Per Annum

Hiring Companies

### Why is Python popular in data science?

Python's popularity in data science may be attributed to its ease of use, readability, and abundance of tools that facilitate data handling.

### Is there a cost associated with this free Applied Data Science with Python course?

No, this course is free and has no hidden charges or registration fees.

### What are the prerequisites to learn this free course?

There are no prerequisites to learning this course; the only requirement is your interest in learning.

### When can I expect to receive my certificate?

You'll receive your certificate as soon as you complete your course.

### What is the duration of my access to the course?

You will have access to the course for 90 Days.Â

### Can I learn data science only with Python?

You can learn data science only with Python.

- Disclaimer
- PMP, PMI, PMBOK, CAPM, PgMP, PfMP, ACP, PBA, RMP, SP, and OPM3 are registered marks of the Project Management Institute, Inc.