Descriptive vs Predictive vs Prescriptive Analytics
HTML-код
- Опубликовано: 26 июл 2024
- Subscribe to RichardOnData here: / @richardondata
In this video I break down the difference between and provide examples of descriptive, predictive, and prescriptive analytics. These are terms proliferated in the business analytics, statistics, and data science worlds, typically presented in a pipeline with descriptive occurring first, predictive occurring next, and prescriptive occurring last.
These are not a comprehensive list of examples of these types of analytics, but rather intended to illustrate the scope and breadth of these forms of analytics as well as provide some helpful, practical ideas.
DESCRIPTIVE ANALYTICS:
These describe to an end-user what HAS HAPPENED IN THE PAST. This is by far and away the most common form of analytic -- and will play at least a small if not huge role in your day-to-day if you are a data scientist. Descriptive analytics include: summary metrics like means, medians, sums, totals, percents, etc., as well as graphs like bar charts, histograms, box plots, time plots, and pie charts. They also include statistical methods like confidence intervals, hypothesis tests, clustering algorithms, and linear/logistic regression when they are applied with the intention of inference. Note that these analytics are NOT necessarily predictive or prescriptive even if a human's instinct is to infer that such things will happen in the future, or make a business decision based on them.
PREDICTIVE ANALYTICS:
These provide insight into WHAT WILL HAPPEN IN THE FUTURE. As a general rule, forecasting models as well as classification or regression models where the intent is prediction rather than inference, will fall into this category. There are predictive analytic variants for spatial data as well as time-series data (e.g. ARIMA -- Autoregressive Integrated Moving Average models). Lastly, supervised learning models for regression/classification and deep learning algorithms fall in this category as well. Simpler types of predictive analytics can be built with business rules; however these are common examples because they all control for variation in the system.
PRESCRIPTIVE ANALYTICS:
These are the "holy grail" of analytics, designed to tell an end user WHAT THEY SHOULD DO. These generally operate by generating various outcomes from different scenarios, and then selecting the best one by controlling for various factors and uncertainty. Probably the most common form is a mathematical optimization model which optimizes an objective function subject to some outcome, features, and constraints. These are also a variety of simulation models: Monte Carlo simulations which use repeated random sampling to control for features which a lot of variation/uncertainty; and discrete event simulations where a real-life process and all of its actions/items and their interactions can be created and impacts compared. Again, you can make a simpler version using business rules and assumptions if time is short or one is unfamiliar with more complex methods.
Lastly, it may be a bit unrealistic to think of these as a pipeline, because in practice, utilizing them can feel a lot more like a cycle. Predictive analytics can easily lend themselves to new ideas for descriptive analytics, and so on.
#DescriptiveAnalytics #PredictiveAnalytics #PrescriptiveAnalytics
PayPal: richardondata@gmail.com
Patreon: / richardondata
BTC: 3LM5d1vibhp1F7pcxAFX8Ys1DM6XLUoNVL
ETH: 0x3CfC599C4c1040963B644780a0E62d45999bE9D8
LTC: MH8yPjvSmKvpmRRmufofjRB9hnRAFHfx32 
Наука
Shortcuts:
0:58 - Descriptive
2:51 - Predictive
5:38 - Prescriptive
Hi Richard, can you provide also their similarities.
Great explanations!
Clear, concise, and meaningful. I will never be lost on these three terms. Thanks!
Thank you, watching this video in preparation for a job interview in the field. Feel a lot more prepared for a discussion on prescriptive analytics.
Awesome man, you just make my day with one video tutorial on this topic.
Glad I could help!
A thorough explanation of types of analytics!!! Awesome! thanks!
Glad you enjoyed it!
Thank you. Very clear and concise. Keep it up my man
Appreciate it!
Thank you for the explanation, this video helps me alot
Well said. Clear & concise. Thank you.
Glad it was helpful! Thanks for watching.
Good explanation, Richard! Thanks
Very welcome!
Very clear and usefull. Thank you. Giuseppe
You are quite welcome!
Great explanations. Thank you.
Like your videos. Keep up the good work!!!
Thank you! Glad this was of interest!
Great content. Keep it coming.
Thank you, glad you enjoyed, and that I will!
excellant eaplanation of the differant models. Thankyou
You are welcome!
Solid gold!
@richardondata this was a great video, thank you! Can you give a hypothetical of how Prescriptive might be used? I’m trying to understand the difference between prescriptive and predictive a bit more. Thanks!
Super useful. Many thanks!
Glad it was helpful!
dude please come back to your channel and resume making videos for us!
Brilliant Richard. very well explained. can you also help in understanding your pricing predictive model would work and how to create in python. thanks
Here's a pretty good tutorial on price prediction from Towards Data Science with snippets of Python code: towardsdatascience.com/mercari-price-suggestion-97ff15840dbd
Great video, very helpful.
Glad to hear it!
thank you so much!
My pleasure!
Thank you!!!
Very nice!
Any book recommendations with the use of Excel to understand and practice predictive and prescriptive analytics?
Hie how can analytics and metrics help to cope up with increasing market turbulence
If we bring a solution based on past data and going to implement in future data to avoid issues then it becomes prescriptive.?
Nope. It's descriptive analysis that I think provides a very nice starting point and bedrock for predictive or prescriptive types, all of which is certainly valuable. But if the analysis doesn't incorporate various scenarios, providing expectations with what will happen in those scenarios, it's not prescriptive.
@@RichardOnData Thanks for your response.Yes I am planning to do descriptive analytics in the initial phase to understand the data what happened in past and why it happened.Based that I am planning to propose the solution for each scenarios
Thanks
Thanks a lot 👍🏻
Can I regarded data tuning as descriptive analysis?
I wouldn't necessarily anything on the back-end/database side of things to be descriptive analytics per se. Not to say it's not important, of course, but descriptive analytics typically broadly describe the current state of a business problem/what has happened in the past.
Ok mr, richard that ‘s right .
Please could you give me example of prescriptive analysis ? Thx in advance
@@vianadnanferman9752 I give some examples in the video, but most of them fall under the category of mathematical optimization or simulation models. An optimization model say in the healthcare industry that can specify, given various constraints, the optimal number of staff to have in order to balance care delivery and costs. Another, very different example would be self-driving cars. They operate through a model's decisions of what to do, based on environmental variables.
@@RichardOnData so can i regard tuning hyperparmeters in search-grid as example of prespective analysis? Thx a lot 👍🏻
Nah, I wouldn't regard that as a type of analytic in and of itself. A component of predictive analytics I'd say would be the best description.
Hi how i can contact you for help regarding dataset
👏👏👏👏
Excellent video, but Descriptive Analytics is in no way the same as Exploratory Data Analysis, the latter being far more complex statistically. Descriptive Analytics is simply Business Intelligence, Market Intelligence, Industry Intelligence, does not require sophisticated mathematical or statistical tools, mainly producing data visualization and reporting, not relying so heavily on statistical modelling. That Predictive Analytics does not involve historical data and the rigorous separation that you make with respect to Descriptive Analytics is an extremely debatable position, somewhat extreme let me say. The generalized conception is that Predictive Analytics involves Descriptive Analytics, and Prescriptive Analytics involves both.
It seems to me that a more rigorous classification of Analytics is simply between Traditional and Modern, focusing Traditional Analytics on Business Intelligence (and therefore little or no use of sophisticated statistical tools) and Modern Analytics on sophisticated statistical and computational tools, modeling, algorithms, stochastic statistics and obviously the automation of analytical program stages (fundamentally the so-called Predictive Modelling, since Target Definition and Features Engineering - 90% of the work in data science- continues to be manual)
Incidentally, despite how powerful Predictive Analytics and Prescriptive Analytics are, the vast majority of data science in current market and business intelligence agencies, consultancies, etc., is Descriptive Analytics and data visualization, and it doesn't involve statistical modeling, sophisticated computational tools and maths, much less Machine Learning, AI, etc. Predictive and prescriptive Analytics are usually used rather on highly sophisticated and cutting-edge industries, such as biotech and drug development, physics, the realm of scientific research and academia, the advertising industry, certain government agencies... it is not well rooted in business or market intelligence, business process optimization and decision making, etc., fields where analytics historically arises.
goat