https://github.com/damisparks/become_data_analyst
Are you new to Data Analysis ? Here you will find simple notebook that will help through your journey. These are personal projects I work on and still working.
https://github.com/damisparks/become_data_analyst
data data-analysis data-visualization matplotlib numpy pandas-tutorial
Last synced: about 1 month ago
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Are you new to Data Analysis ? Here you will find simple notebook that will help through your journey. These are personal projects I work on and still working.
- Host: GitHub
- URL: https://github.com/damisparks/become_data_analyst
- Owner: damisparks
- License: mit
- Created: 2018-02-24T03:29:57.000Z (over 8 years ago)
- Default Branch: master
- Last Pushed: 2019-08-11T20:42:47.000Z (almost 7 years ago)
- Last Synced: 2026-04-17T11:42:06.617Z (about 2 months ago)
- Topics: data, data-analysis, data-visualization, matplotlib, numpy, pandas-tutorial
- Language: Jupyter Notebook
- Homepage:
- Size: 2.7 MB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Learn with Bhetey
## Introduction to Programming
### Data Science Fundamentals Specialisation.
This course for the those who are new to Data Science.
The lessons on this course has been designed to help you get up to speed in the world of data. Whether you want to be a `Data Analyst, Data Scientist, Machine Learning Engineer, AI Engineer`, this is the course for you.
## Table of Content.
- Data Science Overview
- Understanding the misconception between **`ML, DL & AL`**
- What you will learn & what you will not learn right now.
- Overview of Python (_why we use python, its history, and its transition till now_)
- [`Python is an interpreted, high-level, general-purpose programming language. Created by Guido van Rossum and first released in 1991, Python's design philosophy emphasizes code readability with its notable use of significant whitespace. Its language constructs and object-oriented approach aim to help programmers write clear, logical code for small and large-scale projects.`]()
- What is JupyterNotebook for Data Science.
- How to install Python for Windows (_version 2 or 3_)
- How to install Python for Mac (_version 2 or 3_)
- How to install JupyterNotebook using Anaconda
- Why Python ?
- few things to keep in mind :
- it is case sensitive (`helloworld` is not the same as `HelloWorld`)
- spacing is essentials.
- use error messages to your advantage.
- Data Types & Operators
- You'll learn about:
- Integers, Floats, Booleans, Strings
- Operators: Arithmetic, Assignment, Comparison Logical
- [Mathematical Order of Operations](http://mathforum.org/dr.math/faq/faq.order.operations.html)
- Built-In Functions, Type Conversion
- Whitespace and Style Guidelines
- Introduction to DataFrames and Data Series
- Data Analysis Process
If you have questions about my approach, please feel free to contact me [Email Me.](mailto:omifaredammy@gmail.com)