- 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 00 - Course Overview

04:34##### 0.001 Course Overview

04:34

#### Lesson 01 - Data Science Overview

20:27##### 1.001 Introduction to Data Science

08:42##### 1.002 Different Sectors Using Data Science

05:59##### 1.003 Purpose and Components of Python

05:02##### 1.4 Quiz

##### 1.005 Key Takeaways

00:44

#### Lesson 02 - Data Analytics Overview

18:20##### 2.001 Data Analytics Process

07:21##### 2.2 Knowledge Check

##### 2.3 Exploratory Data Analysis(EDA)

##### 2.4 EDA-Quantitative Technique

##### 2.005 EDA - Graphical Technique

00:57##### 2.006 Data Analytics Conclusion or Predictions

04:30##### 2.007 Data Analytics Communication

02:06##### 2.8 Data Types for Plotting

##### 2.009 Data Types and Plotting

02:29##### 2.11 Quiz

##### 2.012 Key Takeaways

00:57##### 2.10 Knowledge Check

#### Lesson 03 - Statistical Analysis and Business Applications

23:53##### 3.001 Introduction to Statistics

01:31##### 3.2 Statistical and Non-statistical Analysis

##### 3.003 Major Categories of Statistics

01:34##### 3.4 Statistical Analysis Considerations

##### 3.005 Population and Sample

02:15##### 3.6 Statistical Analysis Process

##### 3.007 Data Distribution

01:48##### 3.8 Dispersion

##### 3.9 Knowledge Check

##### 3.010 Histogram

03:59##### 3.11 Knowledge Check

##### 3.012 Testing

08:18##### 3.13 Knowledge Check

##### 3.014 Correlation and Inferential Statistics

02:57##### 3.15 Quiz

##### 3.016 Key Takeaways

01:31

#### Lesson 04 - Python Environment Setup and Essentials

23:58##### 4.001 Anaconda

02:54##### 4.2 Installation of Anaconda Python Distribution (contd.)

##### 4.003 Data Types with Python

13:28##### 4.004 Basic Operators and Functions

06:26##### 4.5 Quiz

##### 4.006 Key Takeaways

01:10

#### Lesson 05 - Mathematical Computing with Python (NumPy)

30:31##### 5.001 Introduction to Numpy

05:30##### 5.2 Activity-Sequence it Right

##### 5.003 Demo 01-Creating and Printing an ndarray

04:50##### 5.4 Knowledge Check

##### 5.5 Class and Attributes of ndarray

##### 5.006 Basic Operations

07:04##### 5.7 Activity-Slice It

##### 5.8 Copy and Views

##### 5.009 Mathematical Functions of Numpy

05:01##### 5.010 Analyse GDP of Countries

##### 5.011 Assignment 01 Demo

03:55##### 5.012 Analyse London Olympics Dataset

##### 5.013 Assignment 02 Demo

03:16##### 5.14 Quiz

##### 5.015 Key Takeaways

00:55

#### Lesson 06 - Scientific computing with Python (Scipy)

23:32##### 6.001 Introduction to SciPy

06:57##### 6.002 SciPy Sub Package - Integration and Optimization

05:51##### 6.3 Knowledge Check

##### 6.4 SciPy sub package

##### 6.005 Demo - Calculate Eigenvalues and Eigenvector

01:36##### 6.6 Knowledge Check

##### 6.007 SciPy Sub Package - Statistics, Weave and IO

05:46##### 6.008 Solving Linear Algebra problem using SciPy

##### 6.009 Assignment 01 Demo

01:20##### 6.010 Perform CDF and PDF using Scipy

##### 6.011 Assignment 02 Demo

00:52##### 6.12 Quiz

##### 6.013 Key Takeaways

01:10

#### Lesson 07 - Data Manipulation with Pandas

47:34##### 7.001 Introduction to Pandas

12:29##### 7.2 Knowledge Check

##### 7.003 Understanding DataFrame

05:31##### 7.004 View and Select Data Demo

05:34##### 7.005 Missing Values

03:16##### 7.006 Data Operations

09:56##### 7.7 Knowledge Check

##### 7.008 File Read and Write Support

00:31##### 7.9 Knowledge Check-Sequence it Right

##### 7.010 Pandas Sql Operation

02:00##### 7.011 Analyse the Federal Aviation Authority Dataset using Pandas

##### 7.012 Assignment 01 Demo

04:09##### 7.013 Analyse NewYork city fire department Dataset

##### 7.014 Assignment 02 Demo

02:34##### 7.15 Quiz

##### 7.016 Key Takeaways

01:34

#### Lesson 08 - Machine Learning with Scikit–Learn

01:01:55##### 8.001 Machine Learning Approach

03:57##### 8.002 Steps One and Two

01:00##### 8.3 Steps Three and Four

##### 8.004 How it Works

01:24##### 8.005 Steps Five and Six

01:54##### 8.006 Supervised Learning Model Considerations

00:30##### 8.008 ScikitLearn

02:10##### 8.010 Supervised Learning Models - Linear Regression

11:19##### 8.011 Supervised Learning Models - Logistic Regression

08:43##### 8.012 Unsupervised Learning Models

10:40##### 8.013 Pipeline

02:38##### 8.014 Model Persistence and Evaluation

05:45##### 8.15 Knowledge Check

##### 8.016 Analysing Ad Budgets for different media channels

##### 8.017 Assignment One

05:45##### 8.018 Building a model to predict Diabetes

##### 8.019 Assignment Two

04:58##### Knowledge Check

##### 8.021 Key Takeaways

01:12

#### Lesson 09 - Natural Language Processing with Scikit Learn

49:03##### 9.001 NLP Overview

10:42##### 9.2 NLP Applications

##### 9.3 Knowledge Check

##### 9.004 NLP Libraries-Scikit

12:29##### 9.5 Extraction Considerations

##### 9.006 Scikit Learn-Model Training and Grid Search

10:17##### 9.007 Analysing Spam Collection Data

##### 9.008 Demo Assignment 01

06:32##### 9.009 Sentiment Analysis using NLP

##### 9.010 Demo Assignment 02

08:00##### 9.11 Quiz

##### 9.012 Key Takeaways

01:03

#### Lesson 10 - Data Visualization in Python using matplotlib

32:43##### 10.001 Introduction to Data Visualization

08:01##### 10.2 Knowledge Check

##### 10.3 Line Properties

##### 10.004 (x,y) Plot and Subplots

10:01##### 10.5 Knowledge Check

##### 10.006 Types of Plots

09:32##### 10.007 Draw a pair plot using seaborn library

##### 10.008 Assignment 01 Demo

02:23##### 10.009 Analysing Cause of Death

##### 10.010 Assignment 02 Demo

01:47##### 10.11 Quiz

##### 10.012 Key Takeaways

00:59

#### Lesson 11 - Web Scraping with BeautifulSoup

52:26##### 11.001 Web Scraping and Parsing

12:50##### 11.2 Knowledge Check

##### 11.003 Understanding and Searching the Tree

12:56##### 11.4 Navigating options

##### 11.005 Demo3 Navigating a Tree

04:22##### 11.6 Knowledge Check

##### 11.007 Modifying the Tree

05:37##### 11.008 Parsing and Printing the Document

09:05##### 11.009 Web Scraping of Simplilearn Website

##### 11.010 Assignment 01 Demo

01:55##### 11.011 Web Scraping of Simplilearn Website Resource page

##### 11.012 Assignment 02 demo

04:57##### 11.13 Quiz

##### 11.014 Key takeaways

00:44

#### Lesson 12 - Python integration with Hadoop MapReduce and Spark

40:39##### 12.001 Why Big Data Solutions are Provided for Python

04:55##### 12.2 Hadoop Core Components

##### 12.003 Python Integration with HDFS using Hadoop Streaming

07:20##### 12.004 Demo 01 - Using Hadoop Streaming for Calculating Word Count

08:52##### 12.5 Knowledge Check

##### 12.006 Python Integration with Spark using PySpark

07:43##### 12.007 Demo 02 - Using PySpark to Determine Word Count

04:12##### 12.8 Knowledge Check

##### 12.009 Determine the wordcount

##### 12.010 Assignment 01 Demo

02:47##### 12.011 Display all the airports based in New York using PySpark

##### 12.012 Assignment 02 Demo

03:30##### 12.13 Quiz

##### 12.014 Key takeaways

01:20

#### Practice Projects

##### IBM HR Analytics Employee Attrition Modeling.

Share your certificate with prospective employers and your professional network on LinkedIn.

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

Hiring Companies

### 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.