Data-Science-Resources

A guide to getting started with Data Science and ML.

Last updated Jul 25, 2024

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#csi #csi-sfit #csisfit #data-analysis #data-science #datascience #hacktoberfest #machine-learning #machinelearning #math #numpy #pandas #python #statistics

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README

⚡ Data Science Resources

A guide to getting started with Data Science and ML
(Deep Learning not included)

MATH

For Data Analysis knowledge of Statistics is enough but for building ML models Calculus, Linear Algebra and Probability also plays a huge role.

Reading thoeritical books might be getting too involved, if your goal is to make ML models to just fulfill your applications. But for people who'd like to understand deep learning algorithms and the math behind it, this is a short list of resources.

How do I learn mathematics for machine learning?

This quora answer gives a detailed 5 month roadmap (which can and should be extended according to your comfort) for learning the math behind machine learning and math that every engineer must knof of in general.

Maths for Machine Learning

This book brings the mathematical foundations of basic machine learning concepts to the fore and collects the information in a single place. This book is intended to be a guidebook to the vast mathematical literature that forms the foundations of modern machine learning.

Data Analysis

Data analysis is a process of inspecting, cleansing, transforming and modeling data with the goal of discovering useful information, informing conclusions and supporting decision-making. Data analysis has multiple facets and approaches, encompassing diverse techniques under a variety of names, and is used in different business, science, and social science domains. In today's business world, data analysis plays a role in making decisions more scientific and helping businesses operate more effectively

Numpy
A very useful library for math and Scientific Computing

Pandas
Most used Python library for Data Analysis

Data Visualization

SQL

Big Data Analytics

Big Data is a massive amount of data sets that cannot be stored, processed, or analyzed using traditional tools. Big Data analytics is a process used to extract meaningful insights, such as hidden patterns, unknown correlations, market trends, and customer preferences. Big Data analytics provides various advantages—it can be used for better decision making, preventing fraudulent activities, among other things.

Tools Used in Big Data Analytics

Here are some popular tools used in Big Data analytics:

Hadoop - helps in storing and analyzing data
Spark - used for real-time processing and analyzing large amounts of data
Kafka - a distributed streaming platform that is used for fault-tolerant storage
Cassandra - a distributed database used to handle chunks of data

Big Data Courses

ML Courses

Practical (More bent towards Programming)

Theoritical (More in-depth Math Concepts)

Books

For absolute beginners

For intermediates

Approaching almost any ML problem (Abhishek Thakur)

Websites

Notes

YouTube Channels

Best Websites to get free datasets

How to Contribute

Clone repo and create a new branch: $ git checkout https://github.com/CSI-SFIT/Data-Science-Resources -b namefornew_branch.
Make changes and test.
Submit Pull Request with comprehensive description of changes.

Acknowledgements

CSI SFIT Tech Team 2020 - 2021 :

Joint Tech Head : @kaifkohari
Tech Executive : @EktaMasrani

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