Data Analysis With Pandas And Numpy In Python
Published 3/2023
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz
Language: English | Size: 1.48 GB | Duration: 4h 46m

NumPy and Pandas for Data Analysis and Financial Applications, Examples in Trading Market Analysis[/center]

What you'll 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
Requirements
Python basics, for loops, condition statements, python containers; lists, sets, tuples and dictionnaries.
Description
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 WranglingLesson 2: Introduction to NumPyLesson 3: Data structure in PandasLesson 4: Pandas DataFrame ManipulationLesson 5: Advanced Pandas FunctionsLesson 6: Time and Time Series in PandasLesson 7: Reading and Writing Data with PandasLesson 8: Data Visualization with PandasPractice Exercises
Overview
Section 1: Introduction
Lecture 1 Introduction
Section 2: NumPy or Numerical Python
Lecture 2 NumPy Installation
Lecture 3 NumPy Basic Functions
Lecture 4 NumPy Slicing
Lecture 5 NumPy Multidimentional Arrays
Lecture 6 NumPy DTypes
Lecture 7 NumPy Structured Arrays
Lecture 8 NumPy Reading And Writing Data Files
Lecture 9 NumPy Arithmetic Operations
Lecture 10 NumPy Logical Operations
Lecture 11 NumPy Array Broadcasting
Lecture 12 NumPy Conditional Indexing
Section 3: NumPy Exercises
Lecture 13 Exercises And Solutions
Lecture 14 Exercise 1
Lecture 15 Exercise 2
Lecture 16 Exercise 3
Lecture 17 Exercise 4
Lecture 18 Exercise 5
Lecture 19 Exercise 6
Section 4: Data Structure in Pandas
Lecture 20 Pandas Series
Lecture 21 Series Missing Values
Lecture 22 Applying Functions to Series
Lecture 23 Pandas DataFrames
Section 5: DataFrame Manipulation
Lecture 24 Columns And Indexes In Pandas
Lecture 25 Accessing DataFrames With Loc[] and iLoc[]
Lecture 26 Accessing Scalars/Values In DataFrames at[] And iat[]
Lecture 27 Filling And Replacing Values In DataFrames
Lecture 28 Arithmetic Operations On DataFrames
Lecture 29 Concatenating DataFrames
Lecture 30 Merging And Joining DataFrames
Section 6: Advanced Pandas Function
Lecture 31 Recap And Planning This Lesson
Lecture 32 Pivot Tables
Lecture 33 GroupBy In DataFrames
Lecture 34 Binning Values And The Cut Function
Lecture 35 MultiLevel Indexing In DataFrames
Lecture 36 Filling Missing Values
Section 7: Time and Time Series in Pandas
Lecture 37 Date Time In Python
Lecture 38 Time Zones And Time Deltas In Python
Lecture 39 Rolling And Shift Functions
Section 8: Reading and Writing Data with Pandas
Lecture 40 Reading And Writing Files With Pandas
Section 9: Data Visualization with Pandas
Lecture 41 Plotting Graphs Bars And Histograms
Lecture 42 Boxplots
Lecture 43 Area Plots
Lecture 44 Scatter Points
Lecture 45 Pie Charts
Lecture 46 Conclusion
Section 10: Pandas Exercises
Lecture 47 Pandas Exercises
Lecture 48 Exercise 1 Financial Data Analysis
Lecture 49 Exercise 2 Stacked BarPlots In Pandas
Lecture 50 Exercise 3 Dinner With Friends
Lecture 51 Exercise 4 Oil spill in water: Data cleaning example
Lecture 52 Exercise 5 Financial Trading Analysis/Prediction
Lecture 53 Exercise 6 Financial Trading: analyzing the engulfing candles
Beginner in Python building Data Science skills for real world applications

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