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| File Name: | Numpy,pandas and data visualisation using python |
| Content Source: |  |
| Genre / Category: | Programming |
| File Size : | 3.8MB |
| Publisher: | udemy |
| Updated and Published: | June 13, 2022 |
Product Details
What you’ll learn:
- Go deeper to understand complex topics in python numpy, pandas and data visualisation
- become expert in pandas,numpy and matplotlib
- learn to analyse data in depth using pandas
- Go from absolute beginner to become a confident python numpy, pandas and matplotlib user
- Acquire the required python numpy, pandas and matplotlib knowledge you need to excel in data Science, machine Learning, ai and deep learning
- learn to visualise data like pro in matplotlib and pandas
Requirements:
No programming experience
Description:
- Nearly every scientist working in Python draws on the power of NumPy.
- NumPy brings the computational power of languages like C and Fortran to Python, a language much easier to learn and use. With this power comes simplicity: a solution in NumPy is often clear and elegant.
- Pandas is an open-source library that is made mainly for working with relational or labeled data both easily and intuitively. It provides various data structures and operations for manipulating numerical data and time series. This library is built on top of the NumPy library. Pandas is fast and it has high performance & productivity for users.
Advantages
- Fast and efficient for manipulating and analyzing data.
- Data from different file objects can be loaded.
- Easy handling of missing data (represented as NaN) in floating point as well as non-floating point data
- Size mutability: columns can be inserted and deleted from DataFrame and higher dimensional objects
- Data set merging and joining.
- Flexible reshaping and pivoting of data sets
- Provides time-series functionality.
- Powerful group by functionality for performing split-apply-combine operations on data sets.
Matplotlib is easy to use and an amazing visualizing library in Python.
It is built on NumPy arrays and designed to work with the broader SciPy stack and consists of several plots like line, bar, scatter, histogram, etc.Who this course is for:
Students interested towards data science career pathStudents interested towards data analyst career pathThis course is for you
if you want to learn NumPy, Pandas, and Matplotlib for the first time or get a deeper knowledge of NumPy and Pandas to increase your productivity with deep and Machine learning.This course is for you if you are coming from other programming languages and want to learn Python NumPy and Pandas fast and know it really well
This course is for you if you are tired of NumPy, Pandas, and Matplotlib courses that are too brief, too simple, or too complicated.
This course is for you if you want to build real-world applications using NumPy or Panda and visualize them with Matplotlib.This course is for you if you want to master the in-and-out of NumPy, Pandas, and data visualization.Existing Software Programmers who want to shift to Data Science careerThis course is for you if plan to pass an interview soon.
Who this course is for:
- Students interested towards data science career path
- Students interested towards data analyst career path
- This course is for you if you want to learn NumPys, Pandas, and Matplotlib for the first time or get a deeper knowledge of NumPy and Pandas to increase your productivity with deep and Machine learning.
- This course is for you if you are coming from other programming languages and want to learn Python NumPy and Pandas fast and know it really well.
- This course is for you if you want to build real-world applications using NumPys or Panda and visualize them with Matplotlib.
- This course is for you if you want to master the in-and-out of NumPys, Pandas, and data visualization.
- Existing Software Programmers who want to shift to Data Science career
This course is for you if plan to pass an interview soon.
