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- Introduction
- Basic Engineering Skills
- Advanced Engineering Skills
- Hands On Course‚
- Case Studies
- Best Practices Cloud Platforms
- 130+ Data Sources Data Science
- 1001 Interview Questions
- Recommended Books and Courses
- What is this Cookbook
- Data Engineer vs Data Scientist
- My Data Science Platform Blueprint
- Who Companies Need
- Learn To Code
- Get Familiar With Git
- Agile Development
- Software Engineering Culture
- Learn how a Computer Works
- Data Network Transmission
- Security and Privacy
- Linux
- Docker
- The Cloud
- Security Zone Design
- Data Science Platform
- Hadoop Platforms
- Connect
- Buffer
- Processing Frameworks
- Lambda and Kappa Architecture
- Batch Processing
- Stream Processing
- Should You do Stream or Batch Processing
- Is ETL still relevant for Analytics?
- MapReduce
- Apache Spark
- What is the Difference to MapReduce?
- How Spark Fits to Hadoop
- Spark vs Hadoop
- Spark and Hadoop a Perfect Fit
- Spark on YARn
- My Simple Rule of Thumb
- Available Languages
- Spark Driver Executor and SparkContext
- Spark Batch vs Stream processing
- How Spark uses Data From Hadoop
- What are RDDs and How to Use Them
- SparkSQL How and Why to Use It
- What are Dataframes and How to Use Them
- Machine Learning on Spark (TensorFlow)
- MLlib
- Spark Setup
- Spark Resource Management
- AWS Lambda
- Apache Flink
- Elasticsearch
- Apache Drill
- StreamSets
- Store
- Visualize
- Machine Learning
- How to do Machine Learning in production
- Why machine learning in production is harder then you think
- Models Do Not Work Forever
- Where are The Platforms That Support Machine Learning
- Training Parameter Management
- How to Convince People That Machine Learning Works
- No Rules No Physical Models
- You Have The Data. Use It!
- Data is Stronger Than Opinions
- AWS Sagemaker
- What We Want To Do
- Thoughts On Choosing A Development Environment
- A Look Into the Twitter API
- Ingesting Tweets with Apache Nifi
- Writing from Nifi to Apache Kafka
- Apache Zeppelin Data Processing
- Switch Processing from Zeppelin to Spark
- Data Science @Airbnb
- Data Science @Amazon
- Data Science @Baidu
- Data Science @Blackrock
- Data Science @BMW
- Data Science @Booking.com
- Data Science @CERN
- Data Science @Disney
- Data Science @DLR
- Data Science @Drivetribe
- Data Science @Dropbox
- Data Science @Ebay
- Data Science @Expedia
- Data Science @Facebook
- Data Science @Google
- Data Science @Grammarly
- Data Science @ING Fraud
- Data Science @Instagram
- Data Science @LinkedIn
- Data Science @Lyft
- Data Science @NASA
- Data Science @Netflix
- Data Science @OLX
- Data Science @OTTO
- Data Science @Paypal
- Data Science @Pinterest
- Data Science @Salesforce
- Data Science @Siemens Mindsphere
- Data Science @Slack
- Data Science @Spotify
- Data Science @Symantec
- Data Science @Tinder
- Data Science @Twitter
- Data Science @Uber
- Data Science @Upwork
- Data Science @Woot
- Data Science @Zalando
- General And Academic
- Content Marketing
- Crime
- Drugs
- Education
- Entertainment
- Environmental And Weather Data
- Financial And Economic Data
- Government And World
- Health
- Human Rights
- Labor And Employment Data
- Politics
- Retail
- Social
- Travel And Transportation
- Various Portals
- Source Articles and Blog Posts
- Free Data Sources Data Science
If you have some cool links or topics for the cookbook, please become a contributor.
Simply pull the repo, add your ideas and create a pull request. You can also open an issue and put your thoughts there.
Please use the "Issues" function for comments.
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