Numpy And Pandas For Beginners

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 dаta: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

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