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I just completed data science topic i want to fo a mini project in data science can anyone suggest me a project . I searched a lot in google but i not found any projects can anyonr suggest a project
The beauty of data science is that the project can be anything you're curious about. To figure out what kind of project you want to tackle, what kind of questions do you want to know the answers to?
At the top of my head, you could explore:
the various COVID related data try to find any insights or simply
the price differences of peaches around different areas of Atlanta,
GA
is there a particular skill you want to improve such as time series analysis, classification, or regression?
Find a question you want to answer or a project that will improve your skill in a certain area.
Try this project :
https://www.udemy.com/course/data-science-hands-on-1-hour-project-on-deep-learning/
The Hands on project is currently free on Udemy. It uses Image localization techniques to build, train and test deep learning model for autonomous vehicles. Try changing the data set and try something more innovative liks skin cancer detection or text detection. Try it once.
Try creating a project on no of accidents happened in your country or state, due to different conditions like natural calamities or human mistakes or due to animals and predict the no of cases for the next upcoming year.
Try creating a time series analysis project on covid - 19.
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My team is making the transition to Scrum.
I am facing an issue I still not found on the various Scrum resources I've been studying: how to manage training?
I express myself by example:
my team has 4 developers, 2 of them know nothing about Test Driven Development
the project must be done using TDD
Should I create a backlog item "Study TDD" and use the first sprints so that the untrained developers learn TDD?
Or should I remove the developers from the project until they completed the training? Which is the best practice in this case?
Just send them to the training, and continue your sprints as normal. While they are in training they won't contribute to the velocity, the same as if they were sick or on vacation or just having a bad day. The velocity isn't a goal so much it's an indicator.
You can create a story for training if you want, but it isn't necessary. If creating the story helps, by all means do it. Don't do it just because you think you're supposed to. I've been on teams that liked to track non-product tasks, and teams that didn't. Do what your team decides to do.
In your question you wrote:
the project must be done using TDD
I hope that's because the team decided that, and it wasn't something that was decided for them. The whole point of scrum is to build a team that can make these decisions for themselves.
Well, I will answer YES.
you need to create back log
you need to define test cases and follow TDD
you need to do stand-up meetings and daily follow up
you need to define a team member as scrum master who have best understanding
further, you can engage an online training of transformation expert
Like, I know these guys regarding Agile/Scrum Transformation. http://sparklegenius.com/solutions/agile-transformation/
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Recently, I am working on my course project, the topic is the creation of a new hybrid software process model by integrating Scrum and Team Software Process (TSP). Integration of these two models will be based on the SEMAT Essence Kernel Framework.
I am wondering:
Which steps should be followed for this integration (like
determination of the roles and artifacts in these two models)?
What should be the criteria to decide on good sides?
Thanks in advance!
The best way I think I can answer this question is by quoting the agile manifesto.
"Individuals and interactions over processes and tools"
Agile is about people, teamwork and craftsmanship. It's about involving the customer closely to figure out what really is needed - and delivering that, in small increments of working software. Agile is inspect and adapt, based on experimental delivery and the feedback and evidence that comes from that.
Trust yourself. Work closely together and you can do this. The best learning often comes from doing. :)
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What can you do after learning big data related concepts, Hadoop, ML, NLP etc? Where can you implement these?
Not really a software related question - but it's very relevant to current technology and why some software exists. So here is an opinion.
We now live in a world where it is possible to monitor and digitally record information on an epic scale that continues to expand with concepts like The Internet of Things.
With this information it becomes possible to look at the evidence behind decisions that previously would have been made by gut instinct or opinions. What impact does road design have on traffic flow? Which medical drugs get best results in the real world and not just in drugs trials? Is there a correlation between office temperature and productivity? and so on for millions of questions in different domains.
Around the world, organisations are using data they never previously had, to get better at whatever they do (Good or bad).
The big data concepts are the tools for managing all this information. Big Data is not just large in volume, it is often unstructured and in different forms.
So to answer your question. You can implement these concepts by working with organisations that are using Big Data. Hopefully you can see the potential of Big Data along with the mind bending headache it can create when trying to make sense of it.
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I have multiple variations of an automated strategy for trading certain investment vehicles. For each of these variations I have cross-validated backtests on historic data. I need to pick the best-performing test. There is significant variation between the tests in terms of trades per day, net position size, etc. This makes it difficult to compare one to another.
The nature of the test relies on the predictions of a multidimensional nearest-neighbor search.
Having been recently acquainted with R, I am looking for packages/functions/tests to help me analyze various elements of my strategies' performance. Particularly, I am interested in two things:
1. Packages/functions/metrics that gauge the efficacy of my predictor.
2. Packages/functions/metrics that gauge the relative "profitability" of one variation to another.
If you know something that I should take a look at, please do not hesitate to post it!
I would definitely take a look at these two R Task views:
Taskview Econometrics
Taskview Finance
They provide a broad overview of the kind of packages that are used in these fields. Googling for:
using R for financial analysis
also got me a lot of hits that are relevant for your situation. Good luck!
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R can be very useful for prototyping and some exploratory work. However, I'd like to know what kind of on-line or real-time applications have you (the SO reader) successfully deployed using R and why did you choose R for that task.
Questions:
What kind of problem did it solve? (trending, analysis, etc.)
Why did you choose R over other programming languages? (pros/cons: speed, scaling, etc.)
I'm looking for first-hand experiences, so please do not answer unless you have that.
The breadth of applications is beyond the scope of StackOverflow.
Suppose I said that Java is a fine language for mucking around with APIs, but can anyone point to where it's used in the real world for real deployments? The list would be rather large, with, I kid you not, at least dozens of examples.
If you wish to see examples of real world use, stop by the Bay Area R Users Group, which has had presentations # or by people from Google, Facebook, LinkedIn, Twitter, and more. There are smaller offshoots in NYC, LA, Chicago, London, and elsewhere (there are even Aussies and Kiwis who use R).
So, the best solution to see documented usage is to go to Meetup.com, search for [city] R user group, and identify a Meetup, then select "Past" to see past presentations.