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.
Enroll now: Data Analysis With Pandas And NumPy In Python
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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