# Data Science with Python Course

**#54**in category Data Science

Learning Experience | 9 |
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Data Science with Python course will help you to master concepts of Python programming for data science, with knowledge of data analysis & machine learning.

## About Data Science with Python Course

In this course on Data Science with Python, you will master the concepts of Python programming. Through this Python for Data Science training, you will gain knowledge in data analysis, machine learning, data visualization, web scraping, & natural language processing. After completion of this course, you will master the essential tools of Data Science with Python.

Moreover, at the end of the course, you will be subjected to a project where you can apply your knowledge thought in the class to solve real-world problems. Let’s begin with the introduction to data science and why to learn data science with python.

## What is Data Science?

*Data science* provides meaningful information based on large amounts of complex data or big data. Data science combines different fields of work in statistics and computation to interpret data for decision-making purposes. A data scientist collects, analyzes, and interprets large volumes of data, in many cases, to improve a company’s operations. Data science professionals develop statistical models that analyze data and detect patterns, trends, and relationships in data sets.

## About Python

Python is an object-oriented programming language with integrated dynamic semantics, used primarily for application and web development. The widely used language offers dynamic binding and dynamic typing options.

## Why learn Data Science with Python?

Python is one of the most popular languages in Data Science, which can be used to perform data analysis, data manipulation, and data visualization. Python offers access to a wide variety of data science libraries and it is the ideal language for implementing algorithms and the rapid development of applications

## Course Content

- Course Overview
- Program Features
- Delivery Mode
- Prerequisites
- Target Audience
- Key Learning Outcomes
- Certification Details and Criteria
- Course Curriculum
- Course End Projects
- Customer Reviews

## Syllabus

### Lesson 01- Data Science Overview

- Introduction to Data Science
- Different Sectors Using Data Science
- Purpose and Components of Python

### Lesson 02 – Data Analytics Overview

- Data Analytics Process
- Exploratory Data Analysis (EDA)
- EDA-Quantitative Technique & EDA Graphical Technique
- Data Analytics Conclusion, Communication or Predictions
- Data Types for Plotting

### Lesson 03 – Statistical Analysis and Business Applications

- Introduction to Statistics, Statistical, Non-statistical, and Analysis Major Categories of Statistics
- Statistical Analysis Considerations and Population and Sample
- Statistical Analysis Process and Data Distribution
- Dispersion and Histogram Knowledge
- Check Testing Knowledge, Correlation and Inferential Statistics

### Lesson 04 – Python Environment Setup and Essentials

- Anaconda
- Installation of Anaconda Python Distribution (contd.)
- Data Types with Python & Basic Operators and Functions

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

- Introduction to NumPy, Activity-Sequence it Right, and Creating Printing an array
- Class and Attributes of nd array & Basic Operations
- Views Mathematical Functions of NumPy

### Lesson 06 – Scientific computing with Python (SciPy)

- Introduction to SciPy
- SciPy Sub Package – Integration and Optimization
- Demo – Calculate Eigenvalues and Eigenvector with Demo & Assignment on Statistics, Weave, SciPy, and IO.

### Lesson 07 – Data Manipulation with Pandas

- Introduction to Pandas
- Understanding the Data Frame
- View and Select Data Demo
- Missing Values, Data Operations Knowledge, Check File Read and Write Support
- Pandas SQL Operation Assignment and Demos

### Lesson 08 – Machine Learning with Scikit Learn

- Understand data sets and extract its features
- Identifying problem type and learning model & Test and optimizing the model
- Supervised Learning Model Considerations with Demo & Assignment

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

- NLP Overview
- NLP Applications Knowledge and Check NLP Libraries- Scikit Extraction Considerations

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

- Introduction to Data Visualization
- Check Line Properties (x,y) Plot and Subplots Knowledge & Check Types of Plots
- Analyze the “auto mpg data” and draw a pair plot using ‘seaborn’ library for mpg, weight, and origin

### Lesson 11 – Web Scraping with Beautiful Soup

- Web Scraping and Parsing
- Understanding and Searching the Tree Navigating options
- Demo & Assignments on Navigating a Tree Knowledge & Modifying the Tree Parsing

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

- Why Big Data Solutions are Provided for Python
- Hadoop Core Components and Python Integration with HDFS using Hadoop streaming
- Using Hadoop Streaming for Calculating Word Count & Python Integration with Spark using PySpark

## What you will learn from Data Science with Python Course

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

## Pre-requisites

To best understand the data science with python course, it is recommended that you begin with the courses including, Introduction to Data Science in Python, Math Refresher, Data Science in Real Life, and Statistics Essentials for Data Science.

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## Description

## About Data Science with Python Course

In this course on Data Science with Python, you will master the concepts of Python programming. Through this Python for Data Science training, you will gain knowledge in data analysis, machine learning, data visualization, web scraping, & natural language processing. After completion of this course, you will master the essential tools of Data Science with Python.

Moreover, at the end of the course, you will be subjected to a project where you can apply your knowledge thought in the class to solve real-world problems. Let’s begin with the introduction to data science and why to learn data science with python.

## What is Data Science?

*Data science* provides meaningful information based on large amounts of complex data or big data. Data science combines different fields of work in statistics and computation to interpret data for decision-making purposes. A data scientist collects, analyzes, and interprets large volumes of data, in many cases, to improve a company’s operations. Data science professionals develop statistical models that analyze data and detect patterns, trends, and relationships in data sets.

## About Python

Python is an object-oriented programming language with integrated dynamic semantics, used primarily for application and web development. The widely used language offers dynamic binding and dynamic typing options.

## Why learn Data Science with Python?

Python is one of the most popular languages in Data Science, which can be used to perform data analysis, data manipulation, and data visualization. Python offers access to a wide variety of data science libraries and it is the ideal language for implementing algorithms and the rapid development of applications

## Course Content

- Course Overview
- Program Features
- Delivery Mode
- Prerequisites
- Target Audience
- Key Learning Outcomes
- Certification Details and Criteria
- Course Curriculum
- Course End Projects
- Customer Reviews

## Syllabus

### Lesson 01- Data Science Overview

- Introduction to Data Science
- Different Sectors Using Data Science
- Purpose and Components of Python

### Lesson 02 – Data Analytics Overview

- Data Analytics Process
- Exploratory Data Analysis (EDA)
- EDA-Quantitative Technique & EDA Graphical Technique
- Data Analytics Conclusion, Communication or Predictions
- Data Types for Plotting

### Lesson 03 – Statistical Analysis and Business Applications

- Introduction to Statistics, Statistical, Non-statistical, and Analysis Major Categories of Statistics
- Statistical Analysis Considerations and Population and Sample
- Statistical Analysis Process and Data Distribution
- Dispersion and Histogram Knowledge
- Check Testing Knowledge, Correlation and Inferential Statistics

### Lesson 04 – Python Environment Setup and Essentials

- Anaconda
- Installation of Anaconda Python Distribution (contd.)
- Data Types with Python & Basic Operators and Functions

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

- Introduction to NumPy, Activity-Sequence it Right, and Creating Printing an array
- Class and Attributes of nd array & Basic Operations
- Views Mathematical Functions of NumPy

### Lesson 06 – Scientific computing with Python (SciPy)

- Introduction to SciPy
- SciPy Sub Package – Integration and Optimization
- Demo – Calculate Eigenvalues and Eigenvector with Demo & Assignment on Statistics, Weave, SciPy, and IO.

### Lesson 07 – Data Manipulation with Pandas

- Introduction to Pandas
- Understanding the Data Frame
- View and Select Data Demo
- Missing Values, Data Operations Knowledge, Check File Read and Write Support
- Pandas SQL Operation Assignment and Demos

### Lesson 08 – Machine Learning with Scikit Learn

- Understand data sets and extract its features
- Identifying problem type and learning model & Test and optimizing the model
- Supervised Learning Model Considerations with Demo & Assignment

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

- NLP Overview
- NLP Applications Knowledge and Check NLP Libraries- Scikit Extraction Considerations

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

- Introduction to Data Visualization
- Check Line Properties (x,y) Plot and Subplots Knowledge & Check Types of Plots
- Analyze the “auto mpg data” and draw a pair plot using ‘seaborn’ library for mpg, weight, and origin

### Lesson 11 – Web Scraping with Beautiful Soup

- Web Scraping and Parsing
- Understanding and Searching the Tree Navigating options
- Demo & Assignments on Navigating a Tree Knowledge & Modifying the Tree Parsing

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

- Why Big Data Solutions are Provided for Python
- Hadoop Core Components and Python Integration with HDFS using Hadoop streaming
- Using Hadoop Streaming for Calculating Word Count & Python Integration with Spark using PySpark

## What you will learn from Data Science with Python Course

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

## Pre-requisites

To best understand the data science with python course, it is recommended that you begin with the courses including, Introduction to Data Science in Python, Math Refresher, Data Science in Real Life, and Statistics Essentials for Data Science.

*Note: Your review matters*

* **If you have already done the data science with python course, kindly drop your review in our reviews section. It would help others to get useful information and better insight into the course offered.*

FAQ

## Specification:

- Simplilearn
- Online Course
- Self-paced
- Advanced
- 1-4 Weeks
- Paid Course (Paid certificate)
- English
- Data Analysis Data Science Data Science with 'Python' Hadoop Machine learning Natural language processing Spark

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