- 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

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

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In the data science field require Python

Growth in demand for data science professionals

- Average Salary
### $43K - $95K Per Annum

Hiring Companies - Average Salary
### $83K - $154K Per Annum

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### What are the prerequisites to attend the Data Science with Python program?

Learners should have a basic understanding of mathematics concepts like statistics, calculus, linear algebra, and probability before taking this Data Science with Python program. Knowledge of any programming language is also beneficial.

### How long does it take to get started with Data Science with Python?

The Data Science with Python program gives you access to 8 hours of in-depth learning material that you can follow at your own pace. It is important to practice Python programming to gain a strong hold in data science and this program will help you do it in a short time.

### How do beginners learn Data Science with Python?

Beginners always look for a step-by-step guide when they want to learn data science. While online tutorials and books are good to begin with, this Data Science with Python basics program helps you learn everything from scratch.

### What is the Data Science with Python program?

This Data Science with Python program is the ideal stepping stone in your learning journey as an aspiring data scientist. It will give you an understanding of data analytics tools and techniques, data analysis, visualization, Python basics and its libraries, web scraping, and natural language processing.

### What should I learn first in Data Science?

You can begin learning Data Science by understanding the data analytics process, data types, and statistical analysis. You can then go ahead with Python programming fundamentals.

### Is the Data Science with Python program easy to learn?

The instructors who have designed this Data Science with Python program have rich teaching experience and are aware of the various learner needs. As such, professionals who don’t have any prior knowledge of data science can still get started easily with Python through this program.

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