The Complete Python Data Analysis and Visualization Course

The Complete Python Data Analysis and Visualization Course, available at $44.99, has an average rating of 4.55, with 59 lectures, 4 quizzes, based on 23 reviews, and has 90 subscribers.

You will learn about Fundamentals of Python Programming Learn Exploratory Data Analysis Use Python Numpy library to adding support for large, multi-dimensional arrays and matrices Use the Python Pandas library to create and structure data Create data visualizations using Python matplotlib and the seaborn libraries 20-Data Analysis Interview Questions 20-Python Interview Questions Use of Jupyter Notebook Environment This course is ideal for individuals who are Beginners Data Scientists. or IT Managers eager to learn Data Analysis and Visualization. or Anyone interested in learning more about Python, Data science, or Data visualizations. or Anyone interested about the rapidly expanding world of data science! or Anyone want to spend leisure time in expanding his/her Data analysis and visualization skills. It is particularly useful for Beginners Data Scientists. or IT Managers eager to learn Data Analysis and Visualization. or Anyone interested in learning more about Python, Data science, or Data visualizations. or Anyone interested about the rapidly expanding world of data science! or Anyone want to spend leisure time in expanding his/her Data analysis and visualization skills.

Enroll now: The Complete Python Data Analysis and Visualization Course

Summary

Title: The Complete Python Data Analysis and Visualization Course

Price: $44.99

Average Rating: 4.55

Number of Lectures: 59

Number of Quizzes: 4

Number of Published Lectures: 59

Number of Published Quizzes: 4

Number of Curriculum Items: 63

Number of Published Curriculum Objects: 63

Original Price: $19.99

Quality Status: approved

Status: Live

What You Will Learn

Fundamentals of Python Programming

Learn Exploratory Data Analysis

Use Python Numpy library to adding support for large, multi-dimensional arrays and matrices

Use the Python Pandas library to create and structure data

Create data visualizations using Python matplotlib and the seaborn libraries

20-Data Analysis Interview Questions

20-Python Interview Questions

Use of Jupyter Notebook Environment

Who Should Attend

Beginners Data Scientists.

IT Managers eager to learn Data Analysis and Visualization.

Anyone interested in learning more about Python, Data science, or Data visualizations.

Anyone interested about the rapidly expanding world of data science!

Anyone want to spend leisure time in expanding his/her Data analysis and visualization skills.

Target Audiences

Beginners Data Scientists.

IT Managers eager to learn Data Analysis and Visualization.

Anyone interested in learning more about Python, Data science, or Data visualizations.

Anyone interested about the rapidly expanding world of data science!

Anyone want to spend leisure time in expanding his/her Data analysis and visualization skills.

This course will give you the resources to learn python and effectively use it analyze and visualize data! Start your career in Data Science!

You’ll get a full understanding of how to program with Python and how to use it in conjunction with scientific computing modules and libraries to analyze data.

By the end of this course you will:

Have an understanding of how to program in Python.

Know how to create and manipulate arrays using Numpy and Python.

Know how to use Pandas to create and analyze data sets.

Know how to use Matplotlib and Seaborn libraries to create beautiful data visualization.

Grasp the theoretical and practical concepts parallel

Have hands-on experience on KFC data set

Have an amazing portfolio of example python data analysis projects!

With 30+ lectures and over 10+ labs, you will be excellently prepared for a future in data science!

So what are you waiting for? Get Enrolled in this course

Course Curriculum

Chapter 1: UNBOXING DATA ANALYSIS AND PYTHON

Lecture 1: What is Data Analysis and Data Visualization?

Lecture 2: Why Python and Data Analysis?

Lecture 3: What is Data Wrangling and Data Cleaning?

Chapter 2: SETTING UP PYTHON ENVIRONMENT

Lecture 1: Installing Python on Windows

Lecture 2: Installing Python on Linux

Lecture 3: Installing Python on Mac

Lecture 4: Installing Anaconda on Windows

Chapter 3: PYTHON PRIMER

Lecture 1: Syntax

Lecture 2: Data Types

Lecture 3: Variables

Lecture 4: Comments

Lecture 5: Exception Handling

Chapter 4: WORKING WITH DATA STRUCTURES

Lecture 1: Standard Data Structures

Lecture 2: List

Lecture 3: Tuple

Lecture 4: Dictionary

Lecture 5: Set

Chapter 5: DATA FORMATS AND SOURCES

Lecture 1: Data Formats

Lecture 2: Importing Data Sets From Public Sources

Lecture 3: Importing Data Sets From Facebook

Chapter 6: DATA PREPARATION (NUMPY)

Lecture 1: Introduction to Numpy

Lecture 2: Data Types

Lecture 3: Arrays

Lecture 4: Array Functions

Lecture 5: Operations on Arrays

Lecture 6: LAB: Numpy

Chapter 7: DATA PREPARATION (PANDAS)

Lecture 1: Data Preparation

Lecture 2: Loading and Saving Data

Lecture 3: Pandas Functions

Lecture 4: Regular Expression

Lecture 5: LAB: Pandas

Lecture 6: LAB: Regular Expression

Chapter 8: EXPLORATORY DATA ANALYSIS

Lecture 1: Introduction to Exploratory Data Analysis

Lecture 2: Types of Statistical Data

Lecture 3: Data Profiling

Lecture 4: Data Cleaning

Lecture 5: Missing Values

Lecture 6: LAB: EDA and Missing Values

Lecture 7: Outliers

Lecture 8: LAB: Outliers

Lecture 9: Statistical Methods

Lecture 10: Indexing and Retrieving

Lecture 11: Data Normalization

Lecture 12: Binning and Correlation

Lecture 13: LAB: Binning

Lecture 14: LAB: Correlation

Lecture 15: Grouping Data

Lecture 16: LAB: Grouping

Lecture 17: LAB: Creating Columns

Chapter 9: DATA VISUALIZATION

Lecture 1: Data Visualization

Lecture 2: Matplotlib

Lecture 3: LAB: Matplotlib

Lecture 4: Seaborn

Lecture 5: LAB: Seaborn

Lecture 6: LAB: Exploratory Data Analysis on Titanic Data set

Chapter 10: GETTING READY FOR DA AND PYTHON WORLD

Lecture 1: Career Path and Road map

Lecture 2: 20 Data Analysis Interview Questions

Lecture 3: 20 Python Interview Questions

Chapter 11: CAPSTONE PROJECT

Lecture 1: KFC

Instructors

SKILL CURB
TECHNOLOGY MADE EASY

Rating Distribution

1 stars: 1 votes

2 stars: 2 votes

3 stars: 1 votes

4 stars: 6 votes

5 stars: 13 votes

Frequently Asked Questions

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