Pandas library for data science (All in One)

learn pandas and it's functions by working on a dataset and by making your own dataframe

What you'll learn:

• Learn methods and attributes across numerous pandas functions
• Perform the functions of data operations in Python's popular "pandas" library including filling null values, performing statistical functions and much more!
• Defining your own datasets using pandas and numpy library
• Learn functions important for data manipulation
• Learn and master the most important Pandas functions
• Bring your Data Handling & Data Analysis skills to an outstanding level.
• Update your resume with one of the in demand skill : Data analysis Pandas
• Detect and intelligently fill missing values.

Data scientists spend only 20 percent of their time on building machine learning algorithms and 80 percent of their time finding, cleaning, and reorganizing huge amounts of data. That mostly happen because many use graphical tools such as Excel to process their data. However, if you use a programming language such as Python you can drastically reduce the time it takes for processing your data and make them ready for use in your project. This course will show how Python can be used to manage, clean, and organize huge amounts of data.

By the end of this course, you will be able to apply all majority of Data analysis function on various different datasets with built in function available in pandas

Why this course?

Data scientist is one of the hottest skill of 21st century and many organization are switching their project from Excel to Pandas the advanced Data analysis tool .

This course is basically design to get you started with Pandas library at beginner level, covering majority of important concepts of data processing data analysis and a Pandas library and make you feel confident about data processing task with Pandas at advanced level.

What is this course?

This course covers

• Basics of Pandas library

• Functions of pandas library

• making your own data frame using Numpy and pandas

• applying data manipulation functions

• finding the null values

• filling null values using various functions

• applying statistical functions