The Ultimate Guide to Getting Started in Data Science
Tags: datascience, machinelearning, python, beginners
Published: 2022-04-01 15:12:14

Interested in learning Data Science, welcome. In my simple version Data science is collecting, cleaning and analyzing data to get meaningful information that helps in providing insights. 

To succeed in Data Science path you will need:
- Passion for analysis
- Consistency and Integrity in research
- Willingness to constantly learn 
Easy Peasy!



![Image description](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/58ok0q4175my997y7mmp.jpg) Photo by Pixabay from Pexels
**The Sprint**

The current skills/ knowledge required to start are:

Git - this is a version control platform that enables collaboration with your team mates.
[Check here](https://youtu.be/SWYqp7iY_Tc)

Python - Language that is commonly used in data science and machine learning. 
[Install python ](https://www.w3schools.com/python/python_getstarted.asp)
[Learn Python](https://www.pythontutorial.net/)

Anaconda - A platform that you use for python/ R data science and machine learning projects and you work on Jupyter notebooks. 
[Anaconda install](https://www.anaconda.com/)

You will get to work various types of data, the common are structured and Unstructured data. These types of data are stored in databases. 

Databases - Where data is stored. There are relational and non-relational databases.
[Learn databases](https://youtu.be/qkod5bY10lU)

Some databases that are worth understanding from the word go:
[SQL](https://www.sqltutorial.org/)
[Postgres](https://www.postgresqltutorial.com/)
[MongoDB](https://www.mongodb.com/docs/manual/tutorial/getting-started/)
[DynamoDB](https://docs.aws.amazon.com/amazondynamodb/latest/developerguide/Introduction.html)

**Python Libraries**
Pandas - a python library for Data analysis and manipulation.
[pandas tutorial](https://medium.com/bhavaniravi/python-pandas-tutorial-92018da85a33)

Numpy - a python library that works with mathematical functions.
[Numpy tutorial](https://medium.com/nerd-for-tech/a-complete-guide-on-numpy-for-data-science-c54f47dfef8d)

Matplotlib - a python library used for visualizations.
[Matplotlib tutorial](https://medium.com/analytics-vidhya/a-beginners-guide-to-matplotlib-for-data-visualization-and-exploration-in-python-3fb32d03c3cd)

Seaborn - a python library that deals with statistical operations. 
[Seaborn tutorial](https://python.plainenglish.io/seaborn-a-step-by-step-guide-to-catch-your-audience-part-1-42d9e6e30bea)

Pyforest - a handy python library that when installed it loads several other python libraries.
[Pyforest tutorial](https://towardsdatascience.com/auto-import-python-libraries-d095a11b4cca)

Databases to python
[Postgres to python tutorial](https://medium.com/analytics-vidhya/how-to-setup-a-python-application-with-a-postgres-database-f965e7c1581e)
[SQL to python tutorial](https://medium.com/codex/connect-to-an-mysql-database-via-python-9c88ceac999a)

Boto3 - An Amazon Web Services (AWS) SDK that creates, configures and manages AWS services.
[Boto3 tutorial](https://medium.com/swlh/a-basic-introduction-to-boto3-a66df5548475)

Go through the tutorials, try to practice the examples given in the tutorials. By the end you'll have a pretty good skillset up your sleeve on how to start and perform a few basic data science projects.



![Image description](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/iyp9c7b70qlb1177gu5f.jpg) Photo by Tirachard Kumtanom from Pexels

Let me know of other tutorials you think I should add to the list that will help beginner journey in the comments below.