1 : Data Analysis With Pandas And NumPy In Python
NumPy and Pandas for Data Analysis and Financial Applications, Examples in Trading Market Analysis
Udemy Coupon Codes
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
Who this course is for:
- Beginner in Python building Data Science skills for real world applications
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.
- How to use Pandas and NumPy in Python?