Data Analysis With Pandas And NumPy In Python

Data Analysis With Pandas And NumPy In Python, available at $49.99, has an average rating of 4.38, with 53 lectures, based on 39 reviews, and has 325 subscribers.

You will learn about Data manipulation: working with data, filter, sort, and transform large datasets Data analysis: perform a wide range of data analysis tasks, including aggregating data, performing statistical calculations Data visualization: create a variety of visualizations to help understand data and communicate findings Data wrangling: cleaning and preparing data for analysis, handling missing data, merge datasets, and reshape data This course is ideal for individuals who are Beginner in Python building Data Science skills for real world applications It is particularly useful for Beginner in Python building Data Science skills for real world applications.

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Summary

Title: Data Analysis With Pandas And NumPy In Python

Price: $49.99

Average Rating: 4.38

Number of Lectures: 53

Number of Published Lectures: 53

Number of Curriculum Items: 53

Number of Published Curriculum Objects: 53

Original Price: $54.99

Quality Status: approved

Status: Live

What You Will Learn

Data manipulation: working with data, filter, sort, and transform large datasets
Data analysis: perform a wide range of data analysis tasks, including aggregating data, performing statistical calculations
Data visualization: create a variety of visualizations to help understand data and communicate findings
Data wrangling: cleaning and preparing data for analysis, handling missing data, merge datasets, and reshape data

Who Should Attend

Beginner in Python building Data Science skills for real world applications

Target Audiences

Beginner in Python building Data Science skills for real world applications

This online course is designed to equip you with the skills and knowledge needed to efficiently and effectively manipulate and analyze data using two powerful Python libraries: Pandas and NumPy.

In this course, you will start by learning the fundamentals of data wrangling, including the different types of data and data cleaning techniques. You will then dive into the NumPy library, exploring its powerful features for working with N-dimensional arrays and universal functions.

Next, you will explore the Pandas library, which offers powerful tools for data manipulation, including data structures and data frame manipulation. You will learn how to use advanced Pandas functions, manipulate time and time series data, and read and write data with Pandas.

Throughout the course, you will engage in hands-on exercises and practice problems to reinforce your learning and build your skills. By the end of the course, you will be able to effectively wrangle and analyze data using Pandas and NumPy, and create compelling data visualizations using these tools.

Whether you’re a data analyst, data scientist, or data enthusiast, this course will give you the skills you need to take your data wrangling and analysis to the next level.

Content Table:

Lesson 1: Introduction to Data Wrangling

Lesson 2: Introduction to NumPy

Lesson 3: Data structure in Pandas

Lesson 4: Pandas DataFrame Manipulation

Lesson 5: Advanced Pandas Functions

Lesson 6: Time and Time Series in Pandas

Lesson 7: Reading and Writing Data with Pandas

Lesson 8: Data Visualization with Pandas

Practice Exercises

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction

Chapter 2: NumPy or Numerical Python

Lecture 1: NumPy Installation

Lecture 2: NumPy Basic Functions

Lecture 3: NumPy Slicing

Lecture 4: NumPy Multidimentional Arrays

Lecture 5: NumPy DTypes

Lecture 6: NumPy Structured Arrays

Lecture 7: NumPy Reading And Writing Data Files

Lecture 8: NumPy Arithmetic Operations

Lecture 9: NumPy Logical Operations

Lecture 10: NumPy Array Broadcasting

Lecture 11: NumPy Conditional Indexing

Chapter 3: NumPy Exercises

Lecture 1: Exercises And Solutions

Lecture 2: Exercise 1

Lecture 3: Exercise 2

Lecture 4: Exercise 3

Lecture 5: Exercise 4

Lecture 6: Exercise 5

Lecture 7: Exercise 6

Chapter 4: Data Structure in Pandas

Lecture 1: Pandas Series

Lecture 2: Series Missing Values

Lecture 3: Applying Functions to Series

Lecture 4: Pandas DataFrames

Chapter 5: DataFrame Manipulation

Lecture 1: Columns And Indexes In Pandas

Lecture 2: Accessing DataFrames With Loc[] and iLoc[]

Lecture 3: Accessing Scalars/Values In DataFrames at[] And iat[]

Lecture 4: Filling And Replacing Values In DataFrames

Lecture 5: Arithmetic Operations On DataFrames

Lecture 6: Concatenating DataFrames

Lecture 7: Merging And Joining DataFrames

Chapter 6: Advanced Pandas Function

Lecture 1: Recap And Planning This Lesson

Lecture 2: Pivot Tables

Lecture 3: GroupBy In DataFrames

Lecture 4: Binning Values And The Cut Function

Lecture 5: MultiLevel Indexing In DataFrames

Lecture 6: Filling Missing Values

Chapter 7: Time and Time Series in Pandas

Lecture 1: Date Time In Python

Lecture 2: Time Zones And Time Deltas In Python

Lecture 3: Rolling And Shift Functions

Chapter 8: Reading and Writing Data with Pandas

Lecture 1: Reading And Writing Files With Pandas

Chapter 9: Data Visualization with Pandas

Lecture 1: Plotting Graphs Bars And Histograms

Lecture 2: Boxplots

Lecture 3: Area Plots

Lecture 4: Scatter Points

Lecture 5: Pie Charts

Lecture 6: Conclusion

Chapter 10: Pandas Exercises

Lecture 1: Pandas Exercises

Lecture 2: Exercise 1 Financial Data Analysis

Lecture 3: Exercise 2 Stacked BarPlots In Pandas

Lecture 4: Exercise 3 Dinner With Friends

Lecture 5: Exercise 4 Oil spill in water: Data cleaning example

Lecture 6: Exercise 5 Financial Trading Analysis/Prediction

Lecture 7: Exercise 6 Financial Trading: analyzing the engulfing candles

Instructors

Dr Ziad Francis
PhD, Data Scientist

Rating Distribution

1 stars: 0 votes
2 stars: 1 votes
3 stars: 4 votes
4 stars: 15 votes
5 stars: 19 votes

Frequently Asked Questions

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You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!

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