injamulhoque / python_datascience

Python for Data science, AI & Development

Python for Datascience, AI and Development

This repo arranged with a bunch of substantially important Jupyter Notebooks which contained the whole basics to advanced python for Data Science, AI, and Development, and also developed a project on “Speech to Text analysis”. This repo is based on the course “Python for Data Science, AI & Development”, offered by IBM.

Table of contents

• Introduction
• Insights
• Tool Requirements
• Usage
• Getting help
• Contributing
• License
• Authors and history
• Acknowledgments

Introduction

Kickstart your learning of Python for data science, as well as programming in general, with this beginner-friendly introduction to Python. Python is one of the world’s most popular programming languages, and there has never been greater demand for professionals with the ability to apply Python fundamentals to drive business solutions across industries.

This repository will take you from zero to programming in Python in a matter of hours—no prior programming experience necessary! You will learn Python fundamentals, including data structures and data analysis, complete hands-on exercises throughout the course modules, and create a final project to accelerate your learning skills.

By the end of the all of Jupyter Notebooks, you’ll feel comfortable creating basic programs, working with data, and solving real-world problems in Python. You’ll gain a strong foundation for more advanced learning in the field, and develop skills to help advance your career.

Insights

All the notebooks of this repo is divided into 5 modules as follows:

1. Python Basics: This module teaches the basics of Python and begins by exploring some of the different data types such as integers, real numbers, and strings. Continue with the module and learn how to use expressions in mathematical operations, store values in variables, and the many different ways to manipulate strings.

2. Python Data Structures: This module begins a journey into Python data structures by explaining the use of lists and tuples and how they are able to store collections of data in a single variable. Next learn about dictionaries and how they function by storing data in pairs of keys and values, and end with Python sets to learn how this type of collection can appear in any order and will only contain unique elements.

3. Python Programming Fundamentals: This module discusses Python fundamentals and begins with the concepts of conditions and branching. Continue through the module and learn how to implement loops to iterate over sequences, create functions to perform a specific task, perform exception handling to catch errors, and how classes are needed to create objects.

4. Working with Data in Python: This module explains the basics of working with data in Python and begins the path with learning how to read and write files. Continue the module and uncover the best Python libraries that will aid in data manipulation and mathematical operations.

5. APIs, and Data Collection: This module delves into the unique ways to collect data by the use of APIs and webscraping. It further explores data collection by explaining how to read and collect data when dealing with different file formats.

Tool Requirements

• Jupyter Notebook & Jupyter Lab: Jupyter Notebook or Jupyter Lab is the best tool to practice python and also develop any project. You can download Jupyter notebook or can use it in a web Click the Link: https://jupyter.org/

Usage

A suggested approach for using this project is as follows:

1. Fork and clone the project repo in your local computer using git bash
2. Open each Jupyter notebook using any preferable IDE like Visual Studio, Anaconda.
3. The best platform to practice those notebooks is a web jupyter notebook, where you don't need to worry about environment setup.

Getting help

Here I have given my all social id links. If you face any problem then pls don't feel hesitate to contact me.

• LinkedIn Id
• Tweeter Id
• Instagram Id

Contributing

Anyone can contribute, copy, and clone this project.

License

This project is distributed under the terms of the GPL 3.0 license. The license applies to this project in the GitHub repository.

Authors and history

The author name of this repository is Injamul Hoque. He is a data science practitioner. He recently made a DS roadmap. According to the roadmap, he is pursing the IBM data science career path, and his main goal is to play a vital role as a data scientist in any top-notch tech company like Google, Microsoft, Amazon, or IBM etc.

Acknowledgments

I have completed this repository through the course https://www.coursera.org/learn/python-for-applied-data-science-ai/ conducted by the nice instructor Joseph Santarcangelo, Ph.D., Data Scientist at IBM, offered by IBM.