Data Science with Python
This study covers Pandas 1.0. It gives optimal guidance on how to transition from old versions to new version 1.0. Python is a great platform & environment for data science, it allows powerful tools for data science, statistics, and machine learning. And the Pandas library is the brain of Python data science. Pandas allow you to import, clean, join, merge, concatenate, manipulate, and understand data and prepare or process data for further data presentation, statistical analysis & machine learning. In actuality, all of these tasks require high proficiency skills in Pandas. Data scientists usually spend up to 80% of their time with manipulating data in Pandas. If you’re just started learning Python language, please go to Python Beginner to Advanced Study.
Duration:
- 30+ Hours
- 150+ Exercises
Requirements:
- Machine learning skills
- Finance skills
- Seaborn skills
- A computer capable of storing and running Anaconda
- Ideally some spreadsheet basics (Microsoft Excel, Google Sheets) or programming at a basic level
What you’ll learn:
- Improve data handling & analysis skills to an outstanding level
- Practice relevant Pandas methods and workflows
- Learn Pandas based on version 1.0
- Import, clean and merge raw data and prepare data for machine learning
- Analyze, visualize and know who data with Matplotlib and Seaborn works
- Practice and master your Pandas skills with quizzes, 150+ exercises, and detailed oriented projects
- Import financial/stock data from web sources and analyze them with Pandas
- Learn how to best transition from old versions to new Pandas version
Purpose:
The goal is to bring your data handling & analysis skills to the next level and build your career in data science technology. This study is divided into 4 Parts, Pandas basics, testing your skills in a detailed project challenge that is frequently used in data science job applications/assessment centres. In the last part of this study, you will learn how to import, analyze & handle, and workflow with (financial) time series data.
Advantage:
The world is getting more and more data-driven every day. New professions like data scientist are gaining ground with $100k+ salaries. It’s time to switch from the old environment (Microsoft Excel) to a high tuned new environment (Pandas). If you essentially want to use Python for data science and to replace old environments including Microsoft Excel, then this study is a perfect match for you.
Why to take this study?
- It is the most relevant and detailed oriented study on Pandas
- It is the most up-to-date study incorporating all the latest Pandas updates. Pandas library has developed big improvements in the last months. From my experience, working and relying on old & outdated code can be painful
- It can serve as a Pandas world dictionary covering all relevant methods, properties, and workflows for realtime projects. If you have any problems with any method or workflow, you will most likely get help and find a solution in this study
- It explains comprehensive realtime data workflows. Starting from importing raw messy data, cleaning data, merging/concatenating/grouping and aggregating data, explanatory data analysis through to preparing and processing data for statistics, machine learning and data presentations
- Pandas is a very powerful technology. But it also has drawbacks that may lead to errors in your data. This study focuses on such mistakes and guides you, what and how you should do to avoid errors in your data
Who this study is for:
- Everyone who wants to step deeply into data science. Pandas is heart to everything
- Data scientists who want to improve their data handling, data analysis & manipulation skills
- Everyone who want to switch data projects from the old environment (Microsoft Excel) to new & more powerful environment used in Research & Science
- Investment and finance students & professionals
Author: Alexander Hagmann
Language: English
Size: 11.21GB
