My team is doing a quantitative research about factors affecting university students when opting for secondhand clothes via the e-commerce platforms like Shopee, Lazada, etc. I'm taking charge of dealing with the data generated into Google Sheet from people filling the research form. Thus, my questions are:
How to load data into SPSS
What aspects do I have to analyze?
What to conclude after testing the hypothesises?
I haven't tried anything yet, and don't really know which direction I would opt for when analyzing the data.
Related
In reference to this infographic about the data science industry.
I have a question about the relationship between HTML and data analysis!
Is there really a relationship between them?
If so, what is this relationship?
How can we use HTML in data analysis?
HTML is not a programming language. It is a document markup language. It really can't do much on its own. It can present results but offers limited interactivity. I think when people say HTML they really mean the combination of HTML/CSS/JavaScript.
With JavaScript added, you could technically code anything, though, in practical terms, it's only going to be suited for so many things due to performance concerns and the like.
As Mark_M said, it's mostly used for the presentation layer. This is especially important in the context of business. Let's go through an example. Say you build a model that predicts which of your companies product an individual customer or prospective customer would be most likely to purchase. What are you, as the analyst, going to do with that information? Do you care personally that Sally M wants to buy widget A and Frank R wants to buy widget B? Sure your company cares. Sure your pay is loosely based on the performance of the company. But you have plenty of other things to analyze. So now what?
Do you know who really cares about that information? The Sales Team. That's their whole job. Their pay is based on commission. They know how to act on that information. You let the sales team know this, they do their thing, you get your own performance pay, the company does well, everyone wins.
Now, how do you tell the Sales team this information? Do you let them call you every time they need a lead? Do you give them some sloppily written code in MATLAB or a command line Python client? Do you give them the math equations and tell to figure it out?
Now, remember, a lot of the sales team are not very technically savvy. Even if they sell technology, they barely know enough about it to sell it. The Sales team doesn't know what a -345 error code means. They interface with the customer and ask a member of the support or engineering team for help as needed. The sales team can barely handle the basics of Excel. I would bet you that 90% of the sales team doesn't even know what a "for loop", but that's ok, you don't know how to get that guy on the fence to hand over his signature. People have different strengths.
So what now? You need Bob the salesman to have all the information he needs without bothering you while you code up the next analysis. If you make it too hard, they won't use it. That's just the way life works.
Do you know what's really easy to use? Web applications. Bob the salesman can figure out any web application, especially if it's well designed. So you code up your analysis in the form of a web application. Maybe you have a web design team at your company to redesign your web application, to make it more user-friendly, but they don't know the details of your model. You must code up a prototype web application to allow other people to run and use your model.
Data analysts and data scientist (some at least) are expected to write prototype web applications. You might hear these things called "dashboards." It isn't very hard actually. There are technologies such as R Shiny and Bokeh to make it easy peasy, at least compared to the harder parts of your job.
The application, of course, might not make it out to the general internet, or it might. But it could very well at least make it onto your company intranet. The old way of doing things is companies keeping all their analysis in Excel files and passing those around. The new way is web applications on the intranet.
Your models are totally useless if no one turns them into actionable information and then actually acts on them. The company isn't paying you to build models. They are paying you to tell them what they should do to make more money. You must make it easy enough for them to achieve that.
That's the relationship. In short, it's the presentation layer, ha.
I wanted to learn to predict future events like......being able to predict number of plane crashes in 2018 using past two decades of plane crash data.....or.....predict how many tee-shirts with justin beibers face on it will be sold by 2018 depending upon fan base from previuos data..........or how many iphones 8's and samsungs s9's will be sold if they decide to launch on the same exact date....predicting somewhat accurate whole sale market.....stuff like that....please suggest a book...i really love head first series....is head first data analysis right for me? ....I dont lnow if i can ask questions other than programming here or not.....but here i am.....By the way does big data have anything to do with this?
it all falls in the category of data science (which is big data and data analysis). What you need for predictions and such stuff is some machine learning approach to data you have or can access about stuff you want to predict.
I'd recommend this, newest series of articles: https://medium.com/machine-learning-for-humans/why-machine-learning-matters-6164faf1df12
Apart from really nice intro, you'll find lots of resources for further learning there.
Also I highly recommend deeplearning.ai and machine learning course from Stanford you can find on Coursera.
Cheers!
I think most of the scenarios that you have asked are a case of Supervised Learning which is a type of machine learning, wherein you have previous data to train your machine learning model with the input and output values and once you have trained a model you feed new input values and it gives you the output which is the prediction.
I would highly recommend the following Machine Learning course by Andrew NG which on Coursera which covers all the basics of ML including Supervised and Unsupervised Learning.
https://www.coursera.org/learn/machine-learning
As for the books the following link from Analytics Vidya is a great place to start with, you can go through the books as they can give you some good basics of statistics and data sciences.
https://www.analyticsvidhya.com/blog/2015/10/read-books-for-beginners-machine-learning-artificial-intelligence/
As for the differences between Data Science, Data Analytics and Big Data. Data science and data analytics are similar in the sense that they both try to find patterns in data and based on those pattern you derive some insights.
Big data on the other hand is basically Data of huge size which is distributed across multiple machines, so you can store and compute large amount of data simultaneously and in parallel.
So you may ask how is big data and machine learning related? well the answer lies in the training of machine learning model, since the accuracy of prediction is to a certain extent depends on the amount of data you train it on. So more the training data better the predictions and in terms of quantity big data way ahead of others, hence the relation.
I need to query AutoCAD models to extract structures and connections (e.g., power, data) between them, for storage in a database. I know from experience and research that handling native AutoCAD .dwg is problematic due to the proprietary nature of the format. I see that AutoCAD features a .NET API, but still the problem looks daunting since I have essentially no knowledge a priori of the content of the models. The problem seems like a PhD thesis, as my initial googling hasn't yielded much in the way of tools support or examples.
Are there tools / libraries / examples available to help make AutoCAD extraction feasible in a compressed schedule on a small budget?
I have essentially no knowledge a
priori of the content of the models.
The library is not your problem. Your problem is how the data is modelled, or isn't modelled. You may need to cater for a range of things from attributed blocks to seemingly-unrelated, random bits of text scattered in the proximity of the items in question. If there is no standard for the way this information is stored in the drawing then there is no standard way of machine-reading it. It will probably take longer than a PHD thesis to extract it. You cannot guarantee any sort of comprehensive accuracy either.
AutoCAD (full version, not LT) has Data Extraction routine to extract blocks and attributes from a drawing. If that is how the data is presented then you might be in luck.
It might be cheaper and quicker to hire a team of Muppets to go through the drawings and type the data into something like Excel. You'd be (un)surprised how widespread this sort of practise is, even these days.
As for an inexpensive library, try the Open Design alliance, as mentioned in other AutoCAD questions on this site.
This is a mini project for DBMS course. My task is to develop a Database for management of passenger trains.
I'm designing tables for Customers, Trains, Ticket Booking (via Telephone & Internet), Origins and Destinations.
He said, we are free to incorporate other features in our Database Model. Some of the features that we can include are as listed:
Ad-hoc Querying
Data Mining
Demographic Passenger Mapping
Origin and Destination Mapping
I've no clue about what these features mean. I know about datamining but unable to apply it in this context. Can any one kindly expand these features or suggest new ideas?
EDIT: What is Ad-hoc Querying? Give an example in this context.
Data mining would incorporate extracting useful facts/figures out of the data obtained by your system & stored in the database. For example, data mining might discover that trains between city x and y are always 5 minutes late, or is never at more than 50% capacity, etc. So you may wish to develop some tools or scripts that automatically run and generate statistics (graphs are best) which display this information and highlight unusual trends. In the given example, the schedulers could then analyse why the trains are always late (e.g., maybe the train speedos are wrong?).
Both points 3. and 4. are a subset of data mining imo. There is a huge amount of metrics you could try to measure, it is just really whatever you can think of. If you specify what type of data you are going to collect, that will make making suggestions easier.
Basically, data mining just means "sort the data to find interesting facts".
Based on comment below you could look for,
% of internet vs. phone sales
popular destinations & origins
customers age/sex/location
usage vs. time of day
...
A little background: I have the opportunity to present the idea of a public API to the management of a large car sharing company in my country. Currently, the only options to book a car are a very slow web interface and a hard to reach call center. So I'm excited of the possiblity of writing my own search interface, integrating this functionality into other products and applications etc.
The problem: Due to the special nature of this company, I'll first have to get my proposal trough a comission, which is entirely made up of non-technical and rather conservative people. How do I explain the concept of an API to such an audience?
Don't explain technical details like an API. State the business problem and your solution to the business problem - and how it would impact their bottom line.
For years, sales people have based pitches on two things: Features and Benefit. Each feature should have an associated benefit (to somebody, and preferably everybody). In this case, you're apparently planning to break what's basically a monolithic application into (at least) two pieces: a front end and a back end. The obvious benefits are that 1) each works independently, so development of each is easier. 2) different people can develop the different pieces, 3) it's easier to increase capacity by simply buying more hardware.
Though you haven't said it explicitly, I'd guess one intent is to publicly document the API. This allows outside developers to take over (at least some) development of the front-end code (often for free, no less) while you retain control over the parts that are crucial to your business process. You can more easily [allow others to] add new front-end code to address new market segments while retaining security/certainty that the underlying business process won't be disturbed in the process.
HardCode's answer is correct in that you should really should concentrate on the business issues and benefits.
However, if you really feel you need to explain something you could use the medical receptionist analogue.
A medical practice has it's own patient database and appointment scheduling system used by it's admin and medical staff. This might be pretty complex internally.
However when you want to book an appointment as a patient you talk to the receptionist with a simple set of commands - 'I want an appointment', 'I want to see doctor X', 'I feel sick' and they interface to their systems based on your medical history, the symptoms presented and resource availability to give you an appointment - '4:30pm tomorrow' - in simple language.
So, roughly speaking using the receptionist is analogous to an exterior program using an API. It allows you to interact with a complex system to get the information you need without having to deal with the internal complexities.
They'll be able to understand the benefit of having a mobile phone app that can interact with the booking system, and an API is a necessary component of that. The second benefit of the API being public is that you won't necessarily have to write that app, someone else will be able to (whether or not they actually do is another question, of course).
You should explain which use cases will be improved by your project proposal. An what benefits they can expect, like customer satisfaction.