Data Analysis and Data visualization in Python – Numpy, Pandas, Seaborn for Absolute Beginner.
What you’ll learn
Data analysis using python
Data Visualization in Python
Basics of Numpy, Arrays, Lists.
Importing and creating data frame in python
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
No introductory skill level of Python programming required
Desire to learn!
Welcome! This is Data Science Prerequisites – Numpy – Pandas- Seaborn course.
The best Pandas, Numpy and Seaborn 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 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
- 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)
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!
Who this course is for:
- Data Scientists, Data Analysts.
- Students and professionals who wants to do data analysis using python.
- Students and professionals who have tried machine learning and data science but are having trouble putting the ideas down in code
- Python developer who wants to do analysis of tabular data.
- Who wants to start their data visualization journey
- Who Is curious about how to build effective and impactful graphs
- Who Is looking to break into the business intelligence, analytics or data visualization field
- Anyone who want to express thoughts, findings, insights from any kind of data using data Visualization.