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...
Get link
Facebook
X
Pinterest
Email
Other Apps
six problem types
Get link
Facebook
X
Pinterest
Email
Other Apps
Data analysts typically work with six problem types
1. Making predictions
2. Categorizing things
3. Spotting something unusual
4. Identifying themes
5. Discovering connections
6. Finding patterns
A video, Common problem types, introduced the six problem types with an example for each. The examples are summarized below for review.
Making predictions
A company that wants to know the best advertising method to bring in new customers is an example of a problem requiring analysts to make predictions. Analysts with data on location, type of media, and number of new customers acquired as a result of past ads can't guarantee future results, but they can help predict the best placement of advertising to reach the target audience.
Categorizing things
An example of a problem requiring analysts to categorize things is a company's goal to improve customer satisfaction. Analysts might classify customer service calls based on certain keywords or scores. This could help identify top-performing customer service representatives or help correlate certain actions taken with higher customer satisfaction scores.
Spotting something unusual
A company that sells smart watches that help people monitor their health would be interested in designing their software to spot something unusual. Analysts who have analyzed aggregated health data can help product developers determine the right algorithms to spot and set off alarms when certain data doesn't trend normally.
Identifying themes
User experience (UX) designers might rely on analysts to analyze user interaction data. Similar to problems that require analysts to categorize things, usability improvement projects might require analysts to identify themes to help prioritize the right product features for improvement. Themes are most often used to help researchers explore certain aspects of data. In a user study, user beliefs, practices, and needs are examples of themes.
By now you might be wondering if there is a difference between categorizing things and identifying themes. The best way to think about it is: categorizing things involves assigning items to categories; identifying themes takes those categories a step further by grouping them into broader themes.
Discovering connections
A third-party logistics company working with another company to get shipments delivered to customers on time is a problem requiring analysts to discover connections. By analyzing the wait times at shipping hubs, analysts can determine the appropriate schedule changes to increase the number of on-time deliveries.
Finding patterns
Minimizing downtime caused by machine failure is an example of a problem requiring analysts to find patterns in data. For example, by analyzing maintenance data, they might discover that most failures happen if regular maintenance is delayed by more than a 15-day window.
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 ...
Comments
Post a Comment