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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The structure of data
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The structure of data
Data is everywhere and it can be stored in lots of ways. Two general categories of data are:
Structured data: Organized in a certain format, such as rows and columns.
Unstructured data: Not organized in any easy-to-identify way.
For example, when you rate your favorite restaurant online, you're creating structured data. But when you use Google Earth to check out a satellite image of a restaurant location, you're using unstructured data.
Here's a refresher on the characteristics of structured and unstructured data:
Structured data:
- Defined data types
- Most often quantitative data
- Easy to organize
- Easy to search
- Easy to analyze
- Stored in relational databases
- Contained in rows and columns
- Examples: Excel, Google Sheets, SQL, customer data, phone records, transaction history
Unstructured data:
- Varied data types
- Most often qualitative data
- Difficult to search
- Provides more freedom for analysis
- Stored in data lakes and NoSQL databases
- Can't be put in rows and columns
- Examples: Text messages, social media comments, phone call transcriptions, various log files, images, audio, video
Structured data
As we described earlier, structured data is organized in a certain format. This makes it easier to store and query for business needs. If the data is exported, the structure goes along with the data.
Unstructured data
Unstructured data can’t be organized in any easily identifiable manner. And there is much more unstructured than structured data in the world. Video and audio files, text files, social media content, satellite imagery, presentations, PDF files, open-ended survey responses, and websites all qualify as types of unstructured data.
The fairness issue
The lack of structure makes unstructured data difficult to search, manage, and analyze. But recent advancements in artificial intelligence and machine learning algorithms are beginning to change that. Now, the new challenge facing data scientists is making sure these tools are inclusive and unbiased. Otherwise, certain elements of a dataset will be more heavily weighted and/or represented than others. And as you're learning, an unfair dataset does not accurately represent the population, causing skewed outcomes, low accuracy levels, and unreliable analysis.
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...
https://azure.microsoft.com/en-us/products/azure-sql/#product-overview https://cloud.google.com/bigquery/docs Using BigQuery BigQuery is a data warehouse on Google Cloud that data analysts can use to query, filter large datasets, aggregate results, and perform complex operations. An upcoming activity is performed in BigQuery. This reading provides instructions to create your own BigQuery account, select public datasets, and upload CSV files. At the end of this reading, you can confirm your access to the BigQuery console before you move on to the activity, Note: Additional getting started resources for a few other SQL database platforms are also provided at the end of this reading if you choose to work with them instead of BigQuery. Types of BigQuery accounts There are two different types of accounts: sandbox and free trial. A sandbox account allows you to practice queries and explore public datasets for free, but has additional restrictions on top of the standard quotas and lim...
The R-versus-Python debate People often wonder which programming language they should learn first. You might be wondering about this, too. This certificate teaches the open-source programming language, R. R is a great starting point for foundational data analysis, and it has helpful packages that beginners can apply to projects. Python isn’t covered in the curriculum, but we encourage you to explore Python after completing the certificate. If you are curious about other programming languages, make every effort to continue learning. Any language a beginner starts to learn will have some advantages and challenges. Let’s put this into context by looking at R and Python. The following table is a high-level overview based on a sampling of articles and opinions of those in the field. You can review the information without necessarily picking a side in the R vs. Python debate. In fact, if you check out RStudio’s blog article in the Additional resources section, it’s actually more about ...
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