Data-Scientist

An open source Data Science repository to learn and apply towards solving real world problems.

This is a shortcut path to start studying Data Science. Just follow the steps to answer the questions, "What is Data Science and what should I study to learn Data Science?"

Everything that you will need on Data Science

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 creating predictions about the future. Here you can find the biggest question for Data Science and hundreds of answers from experts.

Link	Preview
What is Data Science @ O'reilly	Data scientists combine entrepreneurship with patience, the willingness to build data products incrementally, the ability to explore, and the ability to iterate over a solution. They are inherently interdisciplinary. They can tackle all aspects of a problem, from initial data collection and data conditioning to drawing conclusions. They can think outside the box to come up with new ways to view the problem, or to work with very broadly defined problems: “here’s a lot of data, what can you make from it?”
What is Data Science @ Quora	Data Science is a combination of a number of aspects of Data such as Technology, Algorithm development, and data interference to study the data, analyse it, and find innovative solutions to difficult problems. Basically Data Science is all about Analysing data and driving for business growth by finding creative ways.
The sexiest job of 21st century	Data scientists today are akin to Wall Street “quants” of the 1980s and 1990s. In those days people with backgrounds in physics and math streamed to investment banks and hedge funds, where they could devise entirely new algorithms and data strategies. Then a variety of universities developed master’s programs in financial engineering, which churned out a second generation of talent that was more accessible to mainstream firms. The pattern was repeated later in the 1990s with search engineers, whose rarefied skills soon came to be taught in computer science programs.
Wikipedia	Data science is an inter-disciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from many structural and unstructured data. Data science is related to data mining, machine learning and big data.
How to Become a Data Scientist	Data scientists are big data wranglers, gathering and analyzing large sets of structured and unstructured data. A data scientist’s role combines computer science, statistics, and mathematics. They analyze, process, and model data then interpret the results to create actionable plans for companies and other organizations.
a very short history of #datascience	The story of how data scientists became sexy is mostly the story of the coupling of the mature discipline of statistics with a very young one--computer science. The term “Data Science” has emerged only recently to specifically designate a new profession that is expected to make sense of the vast stores of big data. But making sense of data has a long history and has been discussed by scientists, statisticians, librarians, computer scientists and others for years. The following timeline traces the evolution of the term “Data Science” and its use, attempts to define it, and related terms.

Learn Data Science

Our favorite programming language is Python nowadays for #DataScience. Python's - Pandas library has full functionalities for collecting and analyzing data. We use Anaconda to play with data and to create applications.

Algorithms

These are some Machine Learning and Data Mining algorithms and models help you to understand your data and derive meaning from it.

Supervised Learning

Regression
Linear Regression
Ordinary Least Squares
Logistic Regression
Stepwise Regression
Multivariate Adaptive Regression Splines
Locally Estimated Scatterplot Smoothing
Classification
- k-nearest neighbor
- Support Vector Machines
- Decision Trees
- ID3 algorithm
- C4.5 algorithm
Ensemble Learning
Boosting
Bagging
Random Forest
AdaBoost

Unsupervised Learning

Clustering
- Hierchical clustering
- k-means
- Fuzzy clustering
- Mixture models
Dimension Reduction
- Principal Component Analysis (PCA)
- t-SNE
Neural Networks
Self-organizing map
Adaptive resonance theory
Hidden Markov Models (HMM)

Semi-Supervised Learning

S3VM
Clustering
Generative models
Low-density separation
Laplacian regularization
Heuristic approaches

Reinforcement Learning

Q Learning
SARSA (State-Action-Reward-State-Action) algorithm
Temporal difference learning

Data Mining Algorithms

C4.5
k-Means
SVM
Apriori
EM
PageRank
AdaBoost
kNN
Naive Bayes
CART

Deep Learning architectures

Multilayer Perceptron
Convolutional Neural Network (CNN)
Recurrent Neural Network (RNN)
Boltzmann Machines
Autoencoder
Generative Adversarial Network (GAN)
Self-Organized Maps

Data Science Collected Resources

A trove of carefully curated resources and links (on the topics of software, platforms, language, techniques, etc.) related to data science, all in one place.

Name		Name	Last commit message	Last commit date
Latest commit History 17 Commits
AWS		AWS
Analyzing Police Activity with pandas		Analyzing Police Activity with pandas
Case Study School Budgeting with Machine Learning in Python		Case Study School Budgeting with Machine Learning in Python
Cleaning Data in Python		Cleaning Data in Python
Cluster Analysis in Python		Cluster Analysis in Python
Commands		Commands
Data Analysis		Data Analysis
Data Cleaning in Python		Data Cleaning in Python
Data Manipulation with pandas		Data Manipulation with pandas
Data Science Resources		Data Science Resources
Data Science With Python		Data Science With Python
Data Scientist Career Path		Data Scientist Career Path
Data Scientist Roadmap		Data Scientist Roadmap
Data Visualization for Everyone		Data Visualization for Everyone
Data Visualization with Matplotlib		Data Visualization with Matplotlib
Data Visualization with Seaborn		Data Visualization with Seaborn
Data Visualization		Data Visualization
Deep Learning		Deep Learning
Exploratory Data Analysis in Python		Exploratory Data Analysis in Python
Financial Data Analysis With Python		Financial Data Analysis With Python
Intermediate Data Visualization with Seaborn		Intermediate Data Visualization with Seaborn
Intermediate Importing Data in Python		Intermediate Importing Data in Python
Intermediate Python		Intermediate Python
Intermediate R		Intermediate R
Introduction to Data Science in Python		Introduction to Data Science in Python
Introduction to Data Visualization with Matplotlib		Introduction to Data Visualization with Matplotlib
Introduction to Data Visualization with Seaborn		Introduction to Data Visualization with Seaborn
Introduction to Databases in Python		Introduction to Databases in Python
Introduction to Importing Data in Python		Introduction to Importing Data in Python
Introduction to Importing Data in R		Introduction to Importing Data in R
Introduction to Python		Introduction to Python
Joining Data with pandas		Joining Data with pandas
Kaggle		Kaggle
Linear Algebra		Linear Algebra
Machine Learning With Python		Machine Learning With Python
Machine Learning for Everyone		Machine Learning for Everyone
Machine Learning with Tree-Based Models in Python		Machine Learning with Tree-Based Models in Python
Machine Learning		Machine Learning
MapReduce		MapReduce
MatPlotLib		MatPlotLib
Math for Data Science		Math for Data Science
Maths For Machine Learning		Maths For Machine Learning
Merging DataFrames with pandas		Merging DataFrames with pandas
NLP		NLP
Numpy		Numpy
Pandas		Pandas
Parallel Programming in R		Parallel Programming in R
Projects		Projects
Python Data Science Toolbox (Part 1)		Python Data Science Toolbox (Part 1)
Python Data Science Toolbox (Part 2)		Python Data Science Toolbox (Part 2)
Python For Finance Risk And Return		Python For Finance Risk And Return
Python For Financial Analysis		Python For Financial Analysis
Python Intro		Python Intro
RegEx		RegEx
SciPy		SciPy
Scikit-Learn		Scikit-Learn
Spark		Spark
Statistical Thinking in Python (Part 1)		Statistical Thinking in Python (Part 1)
Statistical Thinking in Python (Part 2)		Statistical Thinking in Python (Part 2)
Streamlined Data Ingestion with pandas		Streamlined Data Ingestion with pandas
Supervised Learning with Scikit Learn		Supervised Learning with Scikit Learn
Time Series Analysis		Time Series Analysis
Unique Things		Unique Things
Unsupervised Learning in Python		Unsupervised Learning in Python
Working with Dates and Times in Python		Working with Dates and Times in Python
Writing Efficient Python Code		Writing Efficient Python Code
Writing Efficient R Code		Writing Efficient R Code
Writing Functions in Python		Writing Functions in Python
data		data
docs		docs
images		images
opencv		opencv
.DS_Store		.DS_Store
.gitignore		.gitignore
LICENSE		LICENSE
README.md		README.md

Link	Description
The Data Science Lifecycle Process	The Data Science Lifecycle Process is a process for taking data science teams from Idea to Value repeatedly and sustainably. The process is documented in this repo
Data Science Lifecycle Template Repo	Template repository for data science lifecycle project
RexMex	A general purpose recommender metrics library for fair evaluation.
ChemicalX	A PyTorch based deep learning library for drug pair scoring.
PyTorch Geometric Temporal	Representation learning on dynamic graphs.
Little Ball of Fur	A graph sampling library for NetworkX with a Scikit-Learn like API.
Karate Club	An unsupervised machine learning extension library for NetworkX with a Scikit-Learn like API.
ML Workspace	All-in-one web-based IDE for machine learning and data science. The workspace is deployed as a Docker container and is preloaded with a variety of popular data science libraries (e.g., Tensorflow, PyTorch) and dev tools (e.g., Jupyter, VS Code)
Neptune.ai	Community-friendly platform supporting data scientists in creating and sharing machine learning models. Neptune facilitates teamwork, infrastructure management, models comparison and reproducibility.
steppy	Lightweight, Python library for fast and reproducible machine learning experimentation. Introduces very simple interface that enables clean machine learning pipeline design.
steppy-toolkit	Curated collection of the neural networks, transformers and models that make your machine learning work faster and more effective.
Datalab from Google	easily explore, visualize, analyze, and transform data using familiar languages, such as Python and SQL, interactively.
Hortonworks Sandbox	is a personal, portable Hadoop environment that comes with a dozen interactive Hadoop tutorials.
R	is a free software environment for statistical computing and graphics.
RStudio	IDE – powerful user interface for R. It’s free and open source, works on Windows, Mac, and Linux.
Python - Pandas - Anaconda	Completely free enterprise-ready Python distribution for large-scale data processing, predictive analytics, and scientific computing
Pandas GUI	Pandas GUI
Scikit-Learn	Machine Learning in Python
NumPy	NumPy is fundamental for scientific computing with Python. It supports large, multi-dimensional arrays and matrices and includes an assortment of high-level mathematical functions to operate on these arrays.
Vaex	Vaex is a Python library that allows you to visualize large datasets and calculate statistics at high speeds.
SciPy	SciPy works with NumPy arrays and provides efficient routines for numerical integration and optimization.
Data Science Toolbox	Coursera Course
Data Science Toolbox	Blog
Wolfram Data Science Platform	Take numerical, textual, image, GIS or other data and give it the Wolfram treatment, carrying out a full spectrum of data science analysis and visualization and automatically generating rich interactive reports—all powered by the revolutionary knowledge-based Wolfram Language.
Datadog	Solutions, code, and devops for high-scale data science.
Variance	Build powerful data visualizations for the web without writing JavaScript
Kite Development Kit	The Kite Software Development Kit (Apache License, Version 2.0) , or Kite for short, is a set of libraries, tools, examples, and documentation focused on making it easier to build systems on top of the Hadoop ecosystem.
Domino Data Labs	Run, scale, share, and deploy your models — without any infrastructure or setup.
Apache Flink	A platform for efficient, distributed, general-purpose data processing.
Apache Hama	Apache Hama is an Apache Top-Level open source project, allowing you to do advanced analytics beyond MapReduce.
Weka	Weka is a collection of machine learning algorithms for data mining tasks.
Octave	GNU Octave is a high-level interpreted language, primarily intended for numerical computations.(Free Matlab)
Apache Spark	Lightning-fast cluster computing
Hydrosphere Mist	a service for exposing Apache Spark analytics jobs and machine learning models as realtime, batch or reactive web services.
Data Mechanics	A data science and engineering platform making Apache Spark more developer-friendly and cost-effective.
Caffe	Deep Learning Framework
Torch	A SCIENTIFIC COMPUTING FRAMEWORK FOR LUAJIT
Nervana's python based Deep Learning Framework	.
Skale	High performance distributed data processing in NodeJS
Aerosolve	A machine learning package built for humans.
Intel framework	Intel® Deep Learning Framework
Datawrapper	An open source data visualization platform helping everyone to create simple, correct and embeddable charts. Also at github.com
Tensor Flow	TensorFlow is an Open Source Software Library for Machine Intelligence
Natural Language Toolkit	An introductory yet powerful toolkit for natural language processing and classification
nlp-toolkit for node.js	.
Julia	high-level, high-performance dynamic programming language for technical computing
IJulia	a Julia-language backend combined with the Jupyter interactive environment
Apache Zeppelin	Web-based notebook that enables data-driven, interactive data analytics and collaborative documents with SQL, Scala and more
Featuretools	An open source framework for automated feature engineering written in python
Optimus	Cleansing, pre-processing, feature engineering, exploratory data analysis and easy ML with PySpark backend.
Albumentations	А fast and framework agnostic image augmentation library that implements a diverse set of augmentation techniques. Supports classification, segmentation, detection out of the box. Was used to win a number of Deep Learning competitions at Kaggle, Topcoder and those that were a part of the CVPR workshops.
DVC	An open-source data science version control system. It helps track, organize and make data science projects reproducible. In its very basic scenario it helps version control and share large data and model files.
Lambdo	is a workflow engine which significantly simplifies data analysis by combining in one analysis pipeline (i) feature engineering and machine learning (ii) model training and prediction (iii) table population and column evaluation.
Feast	A feature store for the management, discovery, and access of machine learning features. Feast provides a consistent view of feature data for both model training and model serving.
Polyaxon	A platform for reproducible and scalable machine learning and deep learning.
LightTag	Text Annotation Tool for teams
UBIAI	Easy-to-use text annotation tool for teams with most comprehensive auto-annotation features. Supports NER, relations and document classification as well as OCR annotation for invoice labeling
Trains	Auto-Magical Experiment Manager, Version Control & DevOps for AI
Hopsworks	Open-source data-intensive machine learning platform with a feature store. Ingest and manage features for both online (MySQL Cluster) and offline (Apache Hive) access, train and serve models at scale.
MindsDB	MindsDB is an Explainable AutoML framework for developers. With MindsDB you can build, train and use state of the art ML models in as simple as one line of code.
Lightwood	A Pytorch based framework that breaks down machine learning problems into smaller blocks that can be glued together seamlessly with an objective to build predictive models with one line of code.
AWS Data Wrangler	An open-source Python package that extends the power of Pandas library to AWS connecting DataFrames and AWS data related services (Amazon Redshift, AWS Glue, Amazon Athena, Amazon EMR, etc).
Amazon Rekognition	AWS Rekognition is a service that lets developers working with Amazon Web Services add image analysis to their applications. Catalog assets, automate workflows, and extract meaning from your media and applications.
Amazon Textract	Automatically extract printed text, handwriting, and data from any document.
Amazon Lookout for Vision	Spot product defects using computer vision to automate quality inspection.Identify missing product components, vehicle and structure damage, and irregularities for comprehensive quality control.
Amazon CodeGuru	Automate code reviews and optimize application performance with ML-powered recommendations.
CML	An open source toolkit for using continuous integration in data science projects. Automatically train and test models in production-like environments with GitHub Actions & GitLab CI, and autogenerate visual reports on pull/merge requests.
Dask	An open source Python library to painlessly transition your analytics code to distributed computing systems (Big Data)
Statsmodels	A Python-based inferential statistics, hypothesis testing and regression framework
Gensim	An open-source library for topic modeling of natural language text
spaCy	A performant natural language processing toolkit
Grid Studio	Grid studio is a web-based spreadsheet application with full integration of the Python programming language.
Python Data Science Handbook	Python Data Science Handbook: full text in Jupyter Notebooks
Shapley	A data-driven framework to quantify the value of classifiers in a machine learning ensemble.
DAGsHub	A platform built on open source tools for data, model and pipeline management.
Deepnote	A new kind of data science notebook. Jupyter-compatible, with real-time collaboration and running in the cloud.
Valohai	An MLOps platform that handles machine orchestration, automatic reproducibility and deployment.
PyMC3	A Python Library for Probabalistic Programming (Bayesian Inference and Machine Learning)
PyStan	Python interface to Stan (Bayesian inference and modeling)
hmmlearn	Unsupervised learning and inference of Hidden Markov Models
Chaos Genius	ML powered analytics engine for outlier/anomaly detection and root cause analysis
Nimblebox	A full-stack MLOps platform designed to help data scientists and machine learning practitioners around the world discover, create, and launch multi-cloud apps from their web browser.

Twitter	Description
Big Data Combine	Rapid-fire, live tryouts for data scientists seeking to monetize their models as trading strategies
Big Data Mania	Data Viz Wiz , Data Journalist , Growth Hacker , Author of Data Science for Dummies (2015)
Big Data Science	Big Data, Data Science, Predictive Modeling, Business Analytics, Hadoop, Decision and Operations Research.
Charlie Greenbacker	Director of Data Science at @ExploreAltamira
Chris Said	Data scientist at Twitter
Clare Corthell	Dev, Design, Data Science @mattermark #hackerei
DADI Charles-Abner	#datascientist @Ekimetrics. , #machinelearning #dataviz #DynamicCharts #Hadoop #R #Python #NLP #Bitcoin #dataenthousiast
Data Science Central	Data Science Central is the industry's single resource for Big Data practitioners.
Data Science London	Data Science. Big Data. Data Hacks. Data Junkies. Data Startups. Open Data
Data Science Renee	Documenting my path from SQL Data Analyst pursuing an Engineering Master's Degree to Data Scientist
Data Science Report	Mission is to help guide & advance careers in Data Science & Analytics
Data Science Tips	Tips and Tricks for Data Scientists around the world! #datascience #bigdata
Data Vizzard	DataViz, Security, Military
DataScienceX
deeplearning4j
DJ Patil	White House Data Chief, VP @ RelateIQ.
Domino Data Lab
Drew Conway	Data nerd, hacker, student of conflict.
Emilio Ferrara	#Networks, #MachineLearning and #DataScience. I work on #Social Media. Postdoc at @IndianaUniv
Erin Bartolo	Running with #BigData--enjoying a love/hate relationship with its hype. @iSchoolSU #DataScience Program Mgr.
Greg Reda	Working @ GrubHub about data and pandas
Gregory Piatetsky	KDnuggets President, Analytics/Big Data/Data Mining/Data Science expert, KDD & SIGKDD co-founder, was Chief Scientist at 2 startups, part-time philosopher.
Hadley Wickham	Chief Scientist at RStudio, and an Adjunct Professor of Statistics at the University of Auckland, Stanford University, and Rice University.
Hakan Kardas	Data Scientist
Hilary Mason	Data Scientist in Residence at @accel.
Jeff Hammerbacher	ReTweeting about data science
John Myles White	Scientist at Facebook and Julia developer. Author of Machine Learning for Hackers and Bandit Algorithms for Website Optimization. Tweets reflect my views only.
Juan Miguel Lavista	Principal Data Scientist @ Microsoft Data Science Team
Julia Evans	Hacker - Pandas - Data Analyze
Kenneth Cukier	The Economist's Data Editor and co-author of Big Data (http://www.big-data-book.com/).
Kevin Davenport	Organizer of https://www.meetup.com/San-Diego-Data-Science-R-Users-Group/
Kevin Markham	Data science instructor, and founder of Data School
Kim Rees	Interactive data visualization and tools. Data flaneur.
Kirk Borne	DataScientist, PhD Astrophysicist, Top #BigData Influencer.
Linda Regber	Data story teller, visualizations.
Luis Rei	PhD Student. Programming, Mobile, Web. Artificial Intelligence, Intelligent Robotics Machine Learning, Data Mining, Natural Language Processing, Data Science.
Mark Stevenson	Data Analytics Recruitment Specialist at Salt (@SaltJobs) Analytics - Insight - Big Data - Datascience
Matt Harrison	Opinions of full-stack Python guy, author, instructor, currently playing Data Scientist. Occasional fathering, husbanding, organic gardening.
Matthew Russell	Mining the Social Web.
Mert Nuhoğlu	Data Scientist at BizQualify, Developer
Monica Rogati	Data @ Jawbone. Turned data into stories & products at LinkedIn. Text mining, applied machine learning, recommender systems. Ex-gamer, ex-machine coder; namer.
Noah Iliinsky	Visualization & interaction designer. Practical cyclist. Author of vis books: https://www.oreilly.com/pub/au/4419
Paul Miller	Cloud Computing/ Big Data/ Open Data Analyst & Consultant. Writer, Speaker & Moderator. Gigaom Research Analyst.
Peter Skomoroch	Creating intelligent systems to automate tasks & improve decisions. Entrepreneur, ex Principal Data Scientist @LinkedIn. Machine Learning, ProductRei, Networks
Prash Chan	Solution Architect @ IBM, Master Data Management, Data Quality & Data Governance Blogger. Data Science, Hadoop, Big Data & Cloud.
Quora Data Science	Quora's data science topic
R-Bloggers	Tweet blog posts from the R blogosphere, data science conferences and (!) open jobs for data scientists.
Rand Hindi
Randy Olson	Computer scientist researching artificial intelligence. Data tinkerer. Community leader for @DataIsBeautiful. #OpenScience advocate.
Recep Erol	Data Science geek @ UALR
Ryan Orban	Data scientist, genetic origamist, hardware aficionado
Sean J. Taylor	Social Scientist. Hacker. Facebook Data Science Team. Keywords: Experiments, Causal Inference, Statistics, Machine Learning, Economics.
Silvia K. Spiva	#DataScience at Cisco
Harsh B. Gupta	Data Scientist at BBVA Compass
Spencer Nelson	Data nerd
Talha Oz	Enjoys ABM, SNA, DM, ML, NLP, HI, Python, Java. Top percentile kaggler/data scientist
Tasos Skarlatidis	Complex Event Processing, Big Data, Artificial Intelligence and Machine Learning. Passionate about programming and open-source.
Terry Timko	InfoGov; Bigdata; Data as a Service; Data Science; Open, Social & Business Data Convergence
Tony Baer	IT analyst with Ovum covering Big Data & data management with some systems engineering thrown in.
Tony Ojeda	Data Scientist , Author , Entrepreneur. Co-founder @DataCommunityDC. Founder @DistrictDataLab. #DataScience #BigData #DataDC
Vamshi Ambati	Data Science @ PayPal. #NLP, #machinelearning; PhD, Carnegie Mellon alumni (Blog: https://allthingsds.wordpress.com )
Wes McKinney	Pandas (Python Data Analysis library).
WileyEd	Senior Manager - @Seagate Big Data Analytics @McKinsey Alum #BigData + #Analytics Evangelist #Hadoop, #Cloud, #Digital, & #R Enthusiast
WNYC Data News Team	The data news crew at @WNYC. Practicing data-driven journalism, making it visual and showing our work.
Alexey Grigorev	Data science author

Preview	Description
	Key differences of a data scientist vs. data engineer
	A visual guide to Becoming a Data Scientist in 8 Steps by DataCamp (img)
	Mindmap on required skills (img)
	Swami Chandrasekaran made a Curriculum via Metro map.
	by @kzawadz via twitter
	By Data Science Central
	Data Science Wars: R vs Python
	How to select statistical or machine learning techniques
	Choosing the Right Estimator
	The Data Science Industry: Who Does What
	Data Science ~~Venn~~ Euler Diagram
	Different Data Science Skills and Roles from this article by Springboard
	A simple and friendly way of teaching your non-data scientist/non-statistician colleagues how to avoid mistakes with data. From Geckoboard's Data Literacy Lessons.

Course	Slides	Dataset	Notes	Solutions
Introduction to Python	-	-	-	-
Intermediate Python	-	-	-	-
PROJECT TV, Halftime Shows, and the Big Game	-	-	-	-
Data Manipulation with pandas	-	-	-	-
PROJECT The Android App Market on Google Play	-	-	-	-
Merging DataFrames with pandas	-	-	-	-
PROJECT The GitHub History of the Scala Language	-	-	-	-
Introduction to Data Visualization with Matplotlib	-	-	-	-
Introduction to Data Visualization with Seaborn	-	-	-	-
Python Data Science Toolbox (Part 1)	-	-	link	-
Python Data Science Toolbox (Part 2)	-	-	-	-
Intermediate Data Visualization with Seaborn	-	-	-	-
PROJECT A Visual History of Nobel Prize Winners	-	-	-	-
Introduction to Importing Data in Python	-	-	-	-
Intermediate Importing Data in Python	-	-	-	-
Importing & Cleaning Data with Python	-	-	-	-
Cleaning Data in Python	-	-	-	-
Working with Dates and Times in Python	-	-	-	-
Writing Functions in Python	-	-	-	-
Exploratory Data Analysis in Python	-	-	-	-
Analyzing Police Activity with pandas	-	-	-	-
Statistical Thinking in Python (Part 1)	-	-	-	-
Statistical Thinking in Python (Part 2)	-	-	-	-
PROJECT Dr. Semmelweis and the Discovery of Handwashing	-	-	-	-
Supervised Learning with scikit-learn	-	-	-	-
PROJECT Predicting Credit Card Approvals	-	-	-	-
Unsupervised Learning in Python	-	-	-	-
Machine Learning with Tree-Based Models in Python	-	-	-	-
Case Study: School Budgeting with Machine Learning in Python	-	-	-	-
Cluster Analysis in Python	-	-	-	-

Course	Solutions
Introduction to SQL Server	link
Introduction to Relational Databases in SQL	link
Intermediate SQL Server	link
Time Series Analysis in SQL Server	-
Functions for Manipulating Data in SQL Server	-
Database Design	link
Hierarchical and Recursive Queries in SQL Server	-
Transactions and Error Handling in SQL Server	-
Writing Functions and Stored Procedures in SQL Server	-
Building and Optimizing Triggers in SQL Server	link
Improving Query Performance in SQL Server	-

License

shreejitverma/Data-Scientist

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History

Repository files navigation

Data-Scientist

What is Data Science?

Learn Data Science

Algorithms

Supervised Learning

Unsupervised Learning

Semi-Supervised Learning

Reinforcement Learning

Data Mining Algorithms

Deep Learning architectures

Contents

Career Tracks

Data Science Collected Resources

Please feel free to connect with me here on LinkedIn if you are interested in data science and would like to connect

Artificial Intelligence related

AWS related

Blogs, StacksExchanges

Books, Courses, Repos

Git and Github

Interesting Articles

MOOC related

SQL

Statistics

Visualizations (and image processing related)

Neural Network

Videos

Keras

TensorFlow

Apache Spark

Cloud computing

Computation, Computing

Data Mining

Data wrangling related

Docker, Containers

Interview related

Web Technologies

REST, API, Microservice

JSON, XML, HTML

CSS

JavaScript

LaTeX, Markdown, reST

Linux, OS

Time series

Interesting Articles

Julia

Machine Learning

Experiment tracking

Fairness and bias

Deployment of ML

Genetic Algorithm

Keras

Neural Network

Videos

NLP

Reinforcement Learning

TensorFlow

Optimization and ML

Kaggle

Python

Tutorials

Jupyter and IDE related

Matplotlib, Seaborn, Visualization

MOOC courses

NumPy and SciPy

Pandas

Setup, PyPi, Creating your own packages

Spark and AWS

Projects

Tools and Utilities

Web Data Analytics

R related

COLLEGES

Intensive Programs

MOOC's

Packages