Data Science Cheat sheet 2.0

A helpful 5-page data science cheatsheet to assist with exam reviews, interview prep, and anything in-between. It covers over a semester of introductory machine learning, and is based on MIT's Machine Learning courses 6.867 and 15.072. The reader should have at least a basic understanding of statistics and linear algebra, though beginners may find this resource helpful as well.

Creator: Aaron Wang
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https://github.com/aaronwangy/Data-Science-Cheatsheet

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Some Helpful Data Science Projects for Beginners

https://www.kaggle.com/c/house-prices-advanced-regression-techniques

https://www.kaggle.com/c/digit-recognizer

https://www.kaggle.com/c/titanic

Intermediate Level Data science Projects

Black Friday Data : https://www.kaggle.com/sdolezel/black-friday

Human Activity Recognition Data : https://www.kaggle.com/uciml/human-activity-recognition-with-smartphones

Trip History Data : https://www.kaggle.com/pronto/cycle-share-dataset

Million Song Data : https://www.kaggle.com/c/msdchallenge

Census Income Data : https://www.kaggle.com/c/census-income/data

Movie Lens Data : https://www.kaggle.com/grouplens/movielens-20m-dataset

Twitter Classification Data : https://www.kaggle.com/c/twitter-sentiment-analysis2

Text mining : https://www.kaggle.com/kanncaa1/applying-text-mining

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What is data science

Data science, in its most basic terms, can be defined as obtaining insights and information, really anything of value, out of data. Like any new field, it's often tempting but counterproductive to try to put concrete bounds on its definition. This is data science. This is not. In reality, data science is evolving so fast and has already shown such enormous range of possibility that a wider definition is essential to understanding it.

And while it's hard to pin down a specific definition, it's quite easy to see and feel its impact. Data science, when applied to different fields can lead to incredible new insights. And the folks that are using it are already reaping the benefits…

It has become ubiquitous, even more so for people who work in tech. We've gone so far as to personify data in everyday conversation. We ask what it means, what it says. But do we even know what it is?

In the context of data science, the only form of data that matters is digital data.

Digital data is information that is not easily interpreted by an individual but instead relies on machines to interpret, process, and alter it. The words you are reading on your computer screen are an example of this. These digital letters are actually a systematic collection of ones and zeros that encodes to pixels in various hues and at a specific density.

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Learn Statistical Data Analysis with Python

Perform Statistical Data Analysis Techniques with the Python Programming Language. Practice Notebook included.

Rating ⭐️: 4.1 out 5
Students 👨‍🎓 : 4,234
Duration ⏰ : 1hr 2min of on-demand video
Created by 👨‍🏫: Valentine Mwangi

🔗 Course Link


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transaction-fraud-detection

A data science project to predict whether a transaction is a fraud or not.

Creator: juniorcl
Stars ⭐️: 118
Forked By: 65
https://github.com/juniorcl/transaction-fraud-detection

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5 Best beginner-friendly data science projects!

1-Loan Approval Prediction
2-Credit Card Fraud Detection
3-Netflix Movies and TV Shows Analysis
4-Sentiment Analysis of Tweets 5-Weather Data Analysis

These projects are ideal for beginners who want to grasp the fundamentals and get closer to solving real-life projects.

How to choose the right portfolio project?

Here are my best tips:

Pick What You Like: Choose a topic you enjoy to keep the project fun.

Show Your Skills: Make sure your project shows off what you can do, like organizing data or making charts.

Keep It Simple: Start with a simple project that you can expand later.

Use Available Data: Choose a project with easy-to-find data.

Data Science Full Course For Beginners 2024

Fundamentals of Data Science: Understand the basics, including data types, data collection, and data cleaning.
Statistics & Probability: Dive into the math that powers data analysis.
Data Visualization: Learn to create insightful visual representations of data.
Machine Learning: Get hands-on with algorithms and models that make predictions based on data.
Tools & Technologies: Master the use of Python, R, SQL, and key data science libraries and frameworks.
Real-World Projects: Apply your knowledge on real data science problems and solutions.

🆓 Free Online Course
🎬 video lesson
🏃‍♂️ Self paced
Duration ⏰: 6-7 hours worth of material
Source: simplilearn
🔗 Course Link

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Python for Data Visualization: The Complete Masterclass

Transforming Data into Insights: A Comprehensive Guide to Python-based Data Visualization

Rating ⭐️: 4.6 out 5
Students 👨‍🎓 : 29,613
Duration ⏰ : 3.5 hours on-demand video
Created by 👨‍🏫: Meta Brains

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⚠️ Its free for first 1000 enrollments only!


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What is PCA

PCA is a commonly used tool in statistics for making complex data more manageable. Here are some essential points to get started with PCA in R:

🔹 What is PCA? PCA transforms a large set of variables into a smaller one that still contains most of the information in the original set. This process is crucial for analyzing data more efficiently.

🔸 Why R? R is a statistical powerhouse, favored for its versatility in data analysis and visualization capabilities. Its comprehensive packages and functions make PCA straightforward and effective.

🔹 Getting Started: Utilize R's prcomp() function to perform PCA. This function is robust, offering a standardized method to carry out PCA with ease, providing you with principal components, variance captured, and more.

🔸 Visualizing PCA Results: With R, you can leverage powerful visualization libraries like ggplot2 and factoextra. Visualize your PCA results through scree plots to decide how many principal components to retain, or use biplots to understand the relationship between variables and components.

🔹 Interpreting Results: The output of PCA in R includes the variance explained by each principal component, helping you understand the significance of each component in your analysis. This is crucial for making informed decisions based on your data.

🔸 Applications: Whether it's in market research, genomics, or any field dealing with large data sets, PCA in R can help you identify patterns, reduce noise, and focus on the variables that truly matter.

🔹 Key Packages: Beyond base R, packages like factoextra offer additional functions for enhanced PCA analysis and visualization, making your data analysis journey smoother and more insightful.

Embark on your PCA journey in R and transform vast, complicated data sets into simplified, insightful information. Ready to go from data to insights? Our comprehensive course on PCA in R programming covers everything from the basics to advanced applications.