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SQL : PostgreSQL

  Aggregate Functions Like most other relational database products,  PostgreSQL  supports  aggregate functions . An aggregate function computes a single result from multiple input rows. For example, there are aggregates to compute the  count ,  sum ,  avg  (average),  max  (maximum) and  min  (minimum) over a set of rows. As an example, we can find the highest low-temperature reading anywhere with: SELECT max(temp_lo) FROM weather; max ----- 46 (1 row) If we wanted to know what city (or cities) that reading occurred in, we might try: SELECT city FROM weather WHERE temp_lo = max(temp_lo); WRONG but this will not work since the aggregate  max  cannot be used in the  WHERE  clause. (This restriction exists because the  WHERE  clause determines which rows will be included in the aggregate calculation; so obviously it has to be evaluated before aggregate functions are computed.) However, as is o...
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SQL for Various Data Science Languages : UCDAVIS

  SQL for Various Data Science Languages In this class we’ve gone over relational databases and how SQL is used to retrieve data from them. However, because of the popularity and versatility of SQL, SQL is also used for many big data applications. Below are a few resources for how SQL is used with common big data and data science languages. SQL for R SQLDF Package Documentation Examples SQL for Spark Overview Documentation SQL with Hadoop Hive Overview Documentation SQL for Python Python-SQL Package Documentation
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Online resources

  GitHub Repos the-incredible-pytorch Data-science-best-resources DataScienceResources awesome-datascience data-scientist-roadmap AI-Expert-Roadmap deep-learning-roadmap Deep-Learning-Papers-Reading-Roadmap TensorFlow-Tutorials data-science-blogs Data-Science-Wiki awesome-mlops CV-pretrained-model applied-ml data-science-ipython-notebooks ds-cheatsheets Open University Courses Other Curated Lists Deep Learning Drizzle Machine Learning Toolbox The Big Bad NLP Database The Super Duper NLP Repo NLP MODEL FORGE paperswithcode Computer Vision Notebooks Free AI, ML, Deep Learning Video Lectures Online Courses The Open Source Data Science Masters Full Stack Deep Learning Introduction to Deep Learning Practical Deep Learning for Coders Deep Reinforcement Learning Happy learning :)
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SQLite tutorial

  https://www.tutorialspoint.com/sqlite/index.htm This module's primary focus was to introduce the world of SQL to you and to get you started with a few simple querying techniques. Attached to this posting, are a few good, quality overviews of SQL that go into a little bit more depth than what we can cover in our lecture videos. I encourage you to check out and review one or more of these resources. What is SQL and How is it Used? NTC Hosting: Structured Query Language (it's worth exploring this site, not just this singular posting) SQLite Tutorial We introduced ER diagrams and the concept of database modeling in this module. Naturally, these are deep topics and we barely scratched the surface in our lecture videos. I encourage you to review some of the resources below to learn more about these topics in more depth. Norwalk Aberdeen: Entity-Relationship Diagrams (9 Minute YouTube Video) Star Schema vs. Snowflake Schema Explain Star Schema & Snow Flake Design (5 Minute YouTu...
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Cloudera

  Data Extraction from Digital Images The examples involving the movie poster, and the information you obtain by reading it, invite the question: If I have a digital photograph of a movie poster, couldn't reading the photo be a form of data analysis? The answer is certainly yes, but that image processing would be a kind of data analysis that is very different from the focus of this course and specialization. This reading covers such image processing and related topics briefly. To have a computer with a digital camera "see" a movie poster, and then interpret the image in a useful way, is in the general domain of computer vision . Identifying objects or letters represented in a digital image is a kind of classification , a type of machine learning in increasing use today. A classifier is a type of computer program that can take records with potentially many data points, and can infer one or more simple categorical values that are suitable for the record. For example, given...
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Public datasets

  Exploring public datasets Open data helps create a lot of public datasets that you can access to make data-driven decisions. Here are some resources you can use to start searching for public datasets on your own: The Google Cloud Public Datasets allow data analysts access to high-demand public datasets, and make it easy to uncover insights in the cloud.  The Dataset Search can help you find available datasets online with keyword searches.  Kaggle has an Open Data search function that can help you find datasets to practice with. Finally, BigQuery hosts 150+ public datasets you can access and use.  Public health datasets Global Health Observatory data : You can search for datasets from this page or explore featured data collections from the World Health Organization.   The Cancer Imaging Archive (TCIA) dataset : Just like the earlier dataset, this data is hosted by the Google Cloud Public Datasets and can be uploaded to BigQuery. 1000 Genomes : This i...
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HOW TO R markdown

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  This is like a summary how to start to finish the R markdown notebook (RMD.file) Great! VDO https://d18ky98rnyall9.cloudfront.net/4SLdrqlNSAui3a6pTagLwA.processed/full/540p/index.webm?Expires=1635033600&Signature=CS83vA0qHrpNjPEUgCKTAVjOFFdm7eyjZdLmSxRgsUm3rj5~poqQPizOwDAxnBkTmv10ehDqXJ2rOAWPPwBNX5ZGxTdV~cE81VSR0lmvn2XPsBoHVqA5tAJlc2qI0P98IkA4C9EsKqZ0zUcrkD6RqOWEhWjYIkONHKM4nkZHA~4_&Key-Pair-Id=APKAJLTNE6QMUY6HBC5A Overview Earlier in this course, you created a visualization using the ggplot() function. You also learned how to create an R Markdown notebook. In this activity, you will combine and apply your knowledge by adding the code you used to create a visualization to a RMarkdown notebook. By the time you complete this activity, you will be able to make and format an .rmd file containing the visualizations you created using ggplot2. This will allow you to track and share analysis and also share the R code you use to create visualizations. This will prove useful if you...
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