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Ramakm / Data-Careers-Handbook-2024

Data Career Handbook for all

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datadata-analysisdata-engineeringdata-visualizationdatasciencekagglekaggle-competitonmysqlpython3sqlalgorithmsdatascience-machinelearninghacktoberfestmachine-learningdata-sciencepandaspythonstatistics

DataScience & Data Analytics & Data Engineer

kindpng_4462349

In this repo you will find valuable resources to get you started in

Data Analytics, Data Science, Data Engineering, Computer Science.

This is an open source repo. Please try to contribute if you have valuable resources.

It is a Data Science repository to learn and solve projects and problems. Kaggle Problems will be included here. A curated list of Python resources and programs would also be included. This is a space to keep the data and source code of the book contents up-to-date after writing data manipulation with Python. Because of the rapid flow of IT, you often encounter the following situations after writing a book; the internet site you want to analyze has changed. The latest version of the module has a syntax change. So, this space will not simply disclose the data and source code covered by the book, but more actively keep the source code open for readers. However, if the site you want to analyze disappears or exceptions are made when the module no longer supports version upgrades, etc.

What is Data Science?
Data Science is one of the hottest topics on the Computer and Internet farmland nowadays. People have gathered data from applications and systems until today and now is the time to analyze them. The next steps are producing suggestions from the data and predicting the future.

Most of the new comers have doubt on one thing that is:

What is the difference between Data Science and Data Analyst??

Data Science Data Analyst
Perform ad-hoc data mining and gather large sets of structured and unstructured data from several sources. Gather data from various databases and warehouses, filter and clean it.
Use various statistical methods, data visualization techniques to design and evaluate advanced statistical models from vast volumes of data. Write complex SQL queries and scripts to collect, store, manipulate, and retrieve data from RDBMS such as MS SQL Server, Oracle DB, and MySQL.
Automate tedious tasks and generate insights using machine learning models. Spot trends and patterns from complex datasets.
Build AI models using various algorithms and in-built libraries. Create different reports with the help of charts and graphs using Excel and BI tools.

RoadMap:

  1. Excel Learning
  2. Statastics
  3. Linear Algebra
  4. Calculus
  5. Python
  6. SQL
  7. Power BI
  8. Tableau
  9. EDA
  10. Cloud (AWS/Azure)
  11. Deep Learning

This is the way i approached my journey. if you feel and want to follow any other path, its upto you.

Core

Environment and Jupyter

Tutorials

Visualization Tools - Environments

Data Science with Python

Pandas Library in Python

How to contribute: (Instructions)

  • Fork this Repository using the button at the top. However, if you are interested in having contributions to this repo count toward Data Science community, Please give it a star and change the required code or upload any new files.

  • Clone your forked repository to your pc ( $ git clone "url from clone option of this repo")

  • Create a new branch for your modifications (ie. git branch new-user and check it out git checkout new-user and git checkout -b new-user)

  • Add your profile image in static/images/ ( use drag and drop option or upload by commands.)

  • Add your profile data in Contributor folder

  • Add your files (git add -A), commit (git commit -m "added myself") and push (git push origin new-user)

  • Create a pull request

  • Star this repository

  • Follow me

Code of Conduct

  • Please dont use any foul language for anyone.
  • Don't push same programs again and again

Community

  • We have a Discord server! This should be your first stop to talk with other learners. Why don't you introduce yourself right now?
  • Discord link
  • You can also interact through GitHub issues.

🔰 𝐓𝐨𝐩 𝟏𝟎 𝐟𝐫𝐞𝐞 𝐝𝐚𝐭𝐚𝐬𝐞𝐭 𝐬𝐨𝐮𝐫𝐜𝐞𝐬 𝐟𝐨𝐫 𝐃𝐚𝐭𝐚 𝐒𝐜𝐢𝐞𝐧𝐜𝐞 & 𝐀𝐧𝐚𝐥𝐲𝐬𝐭 𝐩𝐫𝐨𝐣𝐞𝐜𝐭𝐬.

🔶 𝐊𝐚𝐠𝐠𝐥𝐞 : https://www.kaggle.com/

🔶 𝐆𝐢𝐭𝐡𝐮𝐛 : https://www.github.com/

🔶 𝐖𝐨𝐫𝐥𝐝 𝐃𝐚𝐭𝐚 : https://lnkd.in/ggkvXru7

🔶 𝐆𝐨𝐯. 𝐃𝐚𝐭𝐚 : https://catalog.data.gov/

🔶 𝐕𝐢𝐬𝐮𝐚𝐥𝐃𝐚𝐭𝐚 : https://visualdata.io/

🔶 𝐆𝐨𝐨𝐠𝐥𝐞 𝐂𝐥𝐨𝐮𝐝 𝐏𝐮𝐛𝐥𝐢𝐜 𝐃𝐚𝐭𝐚𝐬𝐞𝐭 : https://lnkd.in/gP5K63cG

🔶 𝐆𝐨𝐨𝐠𝐥𝐞 𝐃𝐚𝐭𝐚𝐬𝐞𝐭 𝐒𝐞𝐚𝐫𝐜𝐡 : https://lnkd.in/gd39KBVQ

🔶 𝐑𝐞𝐝𝐝𝐢𝐭 : https://lnkd.in/gfpfpGMF

🔶 𝐐𝐮𝐚𝐧𝐝𝐥 𝐃𝐚𝐭𝐚𝐬𝐞𝐭 : https://lnkd.in/guaQz6rn

🔶 𝐔𝐂𝐈 𝐌𝐚𝐜𝐡𝐢𝐧𝐞 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐃𝐚𝐭𝐚𝐬𝐞𝐭 : https://lnkd.in/gd39KBVQ

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Data Career Handbook for all

datadata-analysisdata-engineeringdata-visualizationdatasciencekagglekaggle-competitonmysqlpython3sqlalgorithmsdatascience-machinelearninghacktoberfestmachine-learningdata-sciencepandaspythonstatistics

License:MIT License

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