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Master Python, NumPy & Pandas for Data Science in a fun and interesting manner

What you will learn

Learn to use Pandas for Data Analysis

Learn to work with numerical data in Python

Learn statistics and math with Python

Learn how to code in Jupiter Notebook

Learn how to install packages in Python

Description

When it comes to being attractive, data scientists are already there. In a highly competitive job market, it is tough to keep them after they have been hired. People with a unique mix of scientific training, computer expertise, and analytical abilities are hard to find.

Like the Wall Street “quants” of the 1980s and 1990s, modern-day data scientists are expected to have a similar skill set. People with a background in physics and mathematics flocked to investment banks and hedge funds in those days because they could come up with novel algorithms and data methods.

That being said, data science is becoming one of the most well-suited occupations for success in the twenty-first century. It is computerized, programming-driven, and analytical in nature. Consequently, it comes as no surprise that the need for data scientists has been increasing in the employment market over the last several years.

The supply, on the other hand, has been quite restricted. It is challenging to get the knowledge and abilities required to be recruited as a data scientist.

Lots of resources for learning Python are available online. Because of this, students frequently get overwhelmed by Python’s high learning curve.

It’s a whole new ball game in here! Step-by-step instruction is the hallmark of this course. Throughout each subsequent lesson, we continue to build on what we’ve previously learned. Our goal is to equip you with all the tools and skills you need to master Python, Numpy & Pandas.


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You’ll walk away from each video with a fresh idea that you can put to use right away!

All skill levels are welcome in this course, and even if you have no prior programming or statistical experience, you will be able to succeed!

English
language

Content

Python Quick Refresher
Introduction to Python
Setting up Python
What is Jupyter?
Anaconda Installation: Windows, Mac & Ubuntu
How to implement Python in Jupyter?
Managing Directories in Jupyter Notebook
Input/Output
Working with different datatypes
Variables
Arithmetic Operators
Comparison Operators
Logical Operators
Conditional statements
Loops
Sequences: Lists
Sequences: Dictionaries
Sequences: Tuples
Functions: Built-in Functions
Functions: User-defined Functions
Essential python libraries for data science
Installing Libraries
Importing Libraries
Pandas Library for Data Science
NumPy Library for Data Science
Pandas vs NumPy
Matplotlib Library for Data Science
Seaborn Library for Data Science
Fundamental NumPy Properties
Introduction to NumPy arrays
Creating NumPy arrays
Indexing NumPy arrays
Array shape
Iterating Over NumPy Arrays
Mathematics for Data Science
Basic NumPy arrays: zeros()
Basic NumPy arrays: ones()
Basic NumPy arrays: full()
Adding a scalar
Subtracting a scalar
Multiplying by a scalar
Dividing by a scalar
Raise to a power
Transpose
Element wise addition
Element wise subtraction
Element wise multiplication
Element wise division
Matrix multiplication
Statistics
Python Pandas DataFrames & Series
What is a Python Pandas DataFrame?
What is a Python Pandas Series?
DataFrame vs Series
Creating a DataFrame using lists
Creating a DataFrame using a dictionary
Loading CSV data into python
Changing the Index Column
Inplace
Examining the DataFrame: Head & Tail
Statistical summary of the DataFrame
Slicing rows using bracket operators
Indexing columns using bracket operators
Boolean list
Filtering Rows
Filtering rows using & and | operators
Filtering data using loc()
Adding and deleting rows and columns
Sorting Values
Exporting and saving pandas DataFrames
Concatenating DataFrames
groupby()
 

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