Numpy And Pandas For Beginners

Data Analysis with Pandas in Python and NumPy for Data Science and Machine Learning in Python

What you'll learn
Data analysis using python
Basics of Numpy, Arrays, Lists.
Accessing/Changing Specific Elements, Rows, Columns, etc
Initializing Different Arrays (1s, 0s, full, random, etc)
Basic Mathematics (arithmetic, trigonometry, etc.)
Linear Algebra and Statistics
Reorganizing Arrays
Load data in from a file
Advanced Indexing and Boolean Masking
Importing and creating data frame in python
Data cleaning
Requirements
Basic Python
Description
Welcome! This is Numpy and Pandas for Beginners course.The most comprehensive Pandas and Numpy course available on Udemy! An excellent choice for both beginners and experts looking to expand their knowledge on one of the most popular Python libraries in the world!Pandas for Data Analysis in Python offers  in-depth video tutorials on the most powerful data analysis toolkit Why learn pandas?If you've spent time in a spreadsheet software like MS Excel or Google Sheets and want to take your data analysis skills to the next level, this course is for you! Pandas is a Python package providing fast, flexible, and expressive data structures designed to make working with “relational” or “labeled” data both easy and intuitive. It aims to be the fundamental high-level building block for doing practical, real-world data analysis in Python.Pandas is the most powerful and flexible open source data analysis/manipulation tool available in any language.pandas is well suited for many different kinds of data:Tabular data with heterogeneously-typed columns, as in an SQL table or Excel spreadsheetOrdered and unordered (not necessarily fixed-frequency) time series data.Arbitrary matrix data (homogeneously typed or heterogeneous) with row and column labelsAny other form of observational / statistical data sets. The data need not be labeled at all to be placed into a pandas data structureData Analysis with Pandas and Python is bundled with dozens of datasets for you to use. Dive right in and follow along with my lessons to see how easy it is to get started with pandas!One question or concern I get a lot is that people want to learn deep learning and data science, so they take these courses, but they get left behind because they don’t know enough about the Numpy stack in order to turn those concepts into code.Even if I write the code in full, if you don’t know Numpy, then it’s still very hard to read.This course is designed to remove that obstacle - to show you how to do things in the Numpy stack that are frequently needed in deep learning and data science.So what are those things?Numpy. This forms the basis for everything else. The central object in Numpy is the Numpy array, on which you can do various operations.The key is that a Numpy array isn’t just a regular array you’d see in a language like Java or C++, but instead is like a mathematical object like a vector or a matrix.That means you can do vector and matrix operations like addition, subtraction, and multiplication.The most important aspect of Numpy arrays is that they are optimized for speed. So we’re going to do a demo where I prove to you that using a Numpy vectorized operation is faster than using a Python list.Then we’ll look at some more complicated matrix operations, like products, inverses, determinants, and solving linear systems.

Overview

Section 1: Introduction

Lecture 1 Introduction

Section 2: NumPy and it's Applications

Lecture 2 Tour

Lecture 3 NumPy and it's Applications

Section 3: Initializing an Array

Lecture 4 Initializing an Array

Lecture 5 NumPy Datatypes

Section 4: Accessing/Changing Specific Elements, Rows, Columns, etc

Lecture 6 Accessing/Changing Specific Elements

Section 5: Initializing Different Arrays (1s, 0s, full, random, etc)

Lecture 7 Initializing Different Arrays

Section 6: Basic Mathematics (arithmetic, trigonometry, etc.)

Lecture 8 Basic Mathematics

Section 7: Linear Algebra and Statistics

Lecture 9 Linear Algebra and Statistics

Section 8: Reorganizing Arrays

Lecture 10 Reorganizing Arrays

Section 9: Load data in from a file

Lecture 11 Load data using NumPy

Section 10: Advanced Indexing and Boolean Masking

Lecture 12 Advanced Indexing and Boolean Masking

Section 11: Data Description

Lecture 13 Data Description

Lecture 14 Importing the data

Section 12: Creating a DataFrame

Lecture 15 Series

Lecture 16 Creating a DataFrame

Lecture 17 Homework

Section 13: Data Cleaning

Lecture 18 Data Cleaning

Section 14: Dealing with Empty cells

Lecture 19 Mean Median Mode

Lecture 20 Empty cells

Section 15: Dealing with wrong data

Lecture 21 Wrong Data

Section 16: Dealing with wrong data type

Lecture 22 Wrong Datatype

Section 17: Dealing with duplicate data

Lecture 23 Duplicates

Section 18: Correlation

Lecture 24 Correlation Introduction

Lecture 25 Correlation

Section 19: Certificate

Lecture 26 Certificate

Students and professionals who wants to do data analysis using python.,Python developer who wants to do analysis of tabular data.,Students and professionals with little Numpy experience who plan to learn deep learning and machine learning later,Students and professionals who have tried machine learning and data science but are having trouble putting the ideas down in code