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Data Science Prerequisites - Numpy - Pandas- Seaborn

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4.6

Data Science Prerequisites - Numpy - Pandas- Seaborn

Data Analysis and Data visualization in Python - Numpy, Pandas, Seaborn for Absolute Beginner.

updated on icon Updated on Jun, 2024

language icon Language - English

person icon ADITYA

English [CC]

category icon Data Science,Development

Lectures -45

Duration -4.5 hours

4.6

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Course Description

Welcome! This is Data Science Prerequisites - Numpy - Pandas- Seaborn course.

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 spreadsheet

  • Ordered and unordered (not necessarily fixed-frequency) time series data.

  • Arbitrary matrix data (homogeneously typed or heterogeneous) with row and column labels

  • Any other form of observational / statistical data sets. The data need not be labeled at all to be placed into a pandas data structure

Data 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.

In this course, you're going to learn about the Theory and Foundations of Data Visualization so that you can create amazing charts that are informative, true to the data, and communicatively effective.

"A picture is worth a thousand words". We are all familiar with this expression. It especially applies when trying to explain the insight obtained from the analysis of increasingly large datasets. Data visualization plays an essential role in the representation of both small and large-scale data.

This course is designed to teach analysts, students interested in data science, statisticians, and data scientists how to analyze real-world data by creating professional-looking charts and using numerical descriptive statistics techniques in Python 3.

We'll teach you how to program with Python, how to analyze and create amazing data visualizations with Python! You can use this course as your ready-to-go reference for your own project.

Who this course is for:

  • Programmers / Researchers / Designers that want to learn how to produce top-quality plots

  • Anyone who has to present data at some point!

  • Data Scientists

  • Academic scientists having to publish in scientific journals

  • Journalists / Data Journalists

  • Communication experts

  • Also the general public: you should know how graphs work because they're everywhere!

WHAT YOU WILL LEARN

  • Describe what makes a good or bad visualization

  • Understand best practices for creating basic charts

  • Identify the functions that are best for particular problems

  • Create a visualization seaborn

  • Distribution Plot

  • Histograms

  • KDE Plots

  • Scatter Plot

  • Rug Plot

  • Joint Plot

  • Pair Plot

  • Bar Plot

  • Count Plot

  • Box Plot

  • Violin Plot

  • Strip Plot

  • Swarm Plot

  • Heat Map

  • Pair Plot

  • Sub Plot

SKILLS YOU WILL GAIN

  • Python Programming

  • Data Virtualization

  • Data Visualization (DataViz)

  • seaborn

 It's not a difficult topic, and we will start from the basics. You don't need any previous knowledge. I'll teach you everything you need to know along the way and we'll go straight to the point. No rambling. I really hope to see you in class!

Goals

  • Data analysis using python
  • Data Visualization in Python
  • Basics of Numpy, Arrays, Lists.
  • Importing and creating data frame in python
  • Data cleaning
  • Numpy, Pandas, Seaborn
  • Distribution Plot, Histograms, KDE Plots, Scatter Plot, Rug Plot, Joint Plot, Pair Plot, Bar Plot, Count Plot, Box Plot, Violin Plot, Strip Plot, Swarm Plot, Heat Map, Pair Plot, Sub Plot


Prerequisites

  • No introductory skill level of Python programming required
  • Desire to learn!
Data Science Prerequisites - Numpy - Pandas- Seaborn

Curriculum

Check out the detailed breakdown of what’s inside the course

Introduction
1 Lectures
  • play icon Introduction 04:36 04:36
NumPy and it's Applications
1 Lectures
Tutorialspoint
Initializing an Array
2 Lectures
Tutorialspoint
Accessing/Changing Specific Elements, Rows, Columns, etc
1 Lectures
Tutorialspoint
Initializing Different Arrays (1s, 0s, full, random, etc)
1 Lectures
Tutorialspoint
Basic Mathematics (arithmetic, trigonometry, etc.)
1 Lectures
Tutorialspoint
Linear Algebra and Statistics
1 Lectures
Tutorialspoint
Reorganizing Arrays
1 Lectures
Tutorialspoint
Load data in from a file
1 Lectures
Tutorialspoint
Advanced Indexing and Boolean Masking
1 Lectures
Tutorialspoint
Introduction of Pandas
3 Lectures
Tutorialspoint
Creating a DataFrame
3 Lectures
Tutorialspoint
Data Cleaning
1 Lectures
Tutorialspoint
Dealing with Empty cells
2 Lectures
Tutorialspoint
Dealing with wrong data
2 Lectures
Tutorialspoint
Dealing with wrong data type
1 Lectures
Tutorialspoint
Dealing with duplicate data
1 Lectures
Tutorialspoint
Correlation
2 Lectures
Tutorialspoint
Seaborn
1 Lectures
Tutorialspoint
Distribution Plot
2 Lectures
Tutorialspoint
KDE PLOT
3 Lectures
Tutorialspoint
Scatter Plot
1 Lectures
Tutorialspoint
Rug Plot
1 Lectures
Tutorialspoint
Joint Plot
1 Lectures
Tutorialspoint
Pair Plot
1 Lectures
Tutorialspoint
Bar Plot
1 Lectures
Tutorialspoint
Count Plot
1 Lectures
Tutorialspoint
Box Plot
1 Lectures
Tutorialspoint
Violin Plot
1 Lectures
Tutorialspoint
Strip Plot
1 Lectures
Tutorialspoint
Swarm Plot
1 Lectures
Tutorialspoint
Heat Map
1 Lectures
Tutorialspoint
Pair Grid
1 Lectures
Tutorialspoint
Sub Plots
1 Lectures
Tutorialspoint

Instructor Details

ADITYA

ADITYA


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