Basics Data Science with Numpy, Pandas and Matplotlib
What you'll learn
- The Student learn basic of python and data science libraries such as numpy, pandas , plotting libraries like matplotlib and seaborn , plotly and cufflinks
- Their is no prerequisites
In this course, we will learn the basics of Python Data Structures and the most important Data Science libraries like NumPy and Pandas with step by step examples!
In this course, we will learn step by step with starting with basics understanding of jupyter notebook and how to write a code in jupyter notebook
and understanding each and every function of jupyter notebook then we will learn basic pythons such as Then we will go ahead with the basic python data types like strings, numbers, and its operations. We will deal with different types of ways to assign and access strings, string slicing, replacement, concatenation, formatting, and strings.
Dealing with numbers, we will discuss the assignment, accessing, and different operations with integers and floats. The operations include basic ones and also advanced ones like exponents. Also, we will check the order of operations, increments, and decrements, rounding values, and typecasting.
Then we will proceed with basic data structures in python like Lists tuples and set. For lists, we will try different assignments, access, and slicing options. Along with popular list methods, we will also see list extension, removal, reversing, sorting, min and max, existence check, list looping, slicing, and also inter-conversion of list and strings.
For Tuples also we will do the assignment and access options and proceed with different options with set in python.
After that, we will deal with python dictionaries. Different assignment and access methods. Value update and delete methods and also looping through the values in the dictionary.
After that, we will learn how to read a different file in python such as CSV, JSON, xlv file, etc.
After that, we will explore the numpy basic operation We will try column-wise and row-wise access options, dropping rows and columns, getting the summary of data frames with methods like min, max, etc. Also, we will convert a python dictionary into a pandas data frame. In large datasets, it's common to have empty or missing data. We will see how we can manage missing data within data frames. We will see sorting and indexing operations for data frames.
And last we will learn about plotting tools such as Matplotlib and Seaborn and cufflinks and plotly in-depth with data analysis