Anaconda Reviews

In Anaconda, we get everything: RStudio, Spyder, and Jupyter. R Studio is for R, and Spyder and Jupyter are for Python. Using these, we will be doing data wrangling and data modeling for a developing project.

It's interesting. It's user friendly. That's what makes it outstanding among the other competitors.

It has a collection of R, Python, and others. Their platform strategy has a collection of many other visualization tools, apart from Spyder and RStudio, which is really helpful for data science. For any data science professional, Anaconda is really handy. It has almost all the tools for data science.

It crashes once in a while. In case of a reboot or something unexpected, the unseen code part will get diminished, and it relatively takes longer than other applications when a reboot is happening. They can improve in these areas. 

They can also bring some database software. They have software for analytics and virtualization. However, they don't have any software for the database.

I have been using this solution for the last one year since I joined this company. It was suggested by some of my seniors because it would be better for the database and a one-stop solution that pays for all things.

It is stable. 

I didn't get an opportunity to test this feature. I haven't yet come across an area where I can test the scalability of this platform.

A lot of people who work for data science projects will use Anaconda on a daily basis or at least twice or thrice a week. I use Anaconda almost daily, like for at least half an hour daily. On some days, it can also be for five, six hours.

There weren't many issues for which I needed support from external people. So far, it's good. 

It's easy to set up. You download the EXT file and follow the instructions. It's as simple as that. It's not a big thing. It took around five minutes.

I would recommend it to anyone willing to work in data science. This will be a starting place that covers data-wrangling aspects, user relation aspects, and everything. It is a one-stop solution for everything. 

Anaconda is the main go-to place for analytics. This solution is very handy for almost all data science people. A lot of people I know nowadays use Anaconda. I don't think any other product can even come near Anaconda for data science.

I would rate Anaconda a nine out of ten. The long reboot time and once in a while crash are the two things that lack in Anaconda. Apart from that, I don't see any issues with Anaconda.

l began using it because it was open source and it was free and I knew other people who were using it. I just installed it and I got on with my testing. It was very useful for me because I could save my coding and present it to my assessor.  

There are several things that I think are valuable in the product. My first impressions were the product was fairly responsive, sleek and had a beautiful interface that was pleasant to use. It helped me to be able to easily share code between me and my colleagues.  

I had R installed at that time as well. It worked with R as well as Python. R is good for statistics and visualization. I've used R with Tableau as well and for my situation at the time, Anaconda was a bit superior in respect to this integration.  

The product can be improved in a few ways. It would be possible to simplify the installation although it was not a problem in my case because of my experience. One thing that hurts the product is that the company is not doing more to advertise it as a solution and make it more well known that I have seen. I do not really feel it is as known as it should be in our market.  

The features I would like to see in the next release are more packages. That is, it would be nice to have more libraries added by default.  

I have used it in one of my assignments from the university for several months.  

I have never experienced bugs or crashes or loss of work, so it is stable.  

I have not seen any issues with scalability.  

I have never yet had to contact technical support for Anaconda or Continuum Analytics.  

I have used quite a few products in this category and sometimes I choose one or another depending on what I think seems best for me at the time. I used Notebooks by Jupyter. I've used the R Markdown, which is on the cloud, by RStudio. I've used Tableau software. I used Power BI, which is Microsoft. I used QlikView by Qlik. Those are the main ones that I use more often.  

The main differences are the designs are different and sometimes the features or focus. Each of these products is developing quite well from one release to another. Power BI especially is picking up. One or two years ago it was not very developed but now it seems to be more mature and competitive. I can see why people who are working within a Microsoft environment tend to use Power BI because it is practically free and it is part of Office 365. 

Tableau is sleeker than QlikView and it looks better. Both have different options, but in general, I can not really pinpoint why in some situations I prefer Tableau over QlikView. On the other hand, it was easy to point to why I was using Anaconda.  

The initial setup really only takes minutes, but it is not an easy application to install. I have a technical background so that is not a problem for me. I have also worked in IT support. But I do see why some people might encounter some issues during the installation. Some issues might occur because it is a large installation file. I can not really remember if I needed some dependencies like .NET installed or something else. I probably can't remember that because I probably already had the necessary dependencies installed already. I do install quite a few products on my machine and there is a good chance that some other product already required what was needed so it was already there.  

The product is open-source and free to users.  

My only advice to people considering this type of solution is just to use Anaconda. It is a good product. Other products are good as well, but this is one you should try in this category.  

On a scale of one to ten where one is the worst and ten is the best, I would rate Anaconda in comparison to other products as between nine and ten. It is a very good solution. I will rate it a nine as there is always room for improvement.  

We primarily use the solution for the data science class. It's used for people to build their data science models.

The solution's most valuable aspects include the repositories, the way we log, the way we install that repository management, and how we can easily integrate the solution with Jupyter Notebook.

The virtual environment is very good.

Anaconda has a platform for data science professionals, but if they would have something for an on-prem solution for the data science platform, it would be more valuable to Anaconda. 

The user interface could be improved. There should be easy provisioning on AWS.

Configuring clusters could be easier.

The solution would benefit from offering more automation.

I've been using the solution for eight months.

It's too early to comment on the solution's stability; we've only been using it for eight months.

We're still in the early stages of using the solution, so we haven't tried to scale. We're planning to have 50 users on the solution.

We haven't reached out to technical support.

We did previously use a different solution. We switched to this solution because it offered higher security.

The initial setup was straightforward. Deployment took a few days.

I browsed through the documentation and handled the implementation myself.

We use the on-premises deployment model.

I'd rate the solution seven out of ten.

I was using this solution for buildings some PoCs, as well as during a hackathon.

The most valuable feature is the Jupyter notebook that allows us to write the Python code, compile it on the fly, and then look at the results.

I found the navigators and the other features to be very good for quick development.

There is a system that is similar to a marketplace where we can search for and install all the tools that we need, and they have been collated in one place.

Having a small guide or video on the tool would help learn how to use it and what the features are.

I have been using Anaconda for about six months.

We have not yet had to scale this solution.

Currently, we have between ten and fifteen users, and they are all technicians.

We have not been in contact with technical support.

This was our first experience with the Jupyter notebook and I do not know of any competing solutions.

The initial setup of Anaconda is straightforward.

I used the instructions that I received from our consultants and the deployment took between one and two hours.

My advice to anybody who is researching this tool is to download and install it, then start exploring the different tools that are available. I suggest starting with Jupyter because it is easy to figure out, then move to Python tasks.

This is a good tool that is quick to deploy with and pretty easy to use. I have not fully explored it yet, so I can only give it an average rating.

I would rate this solution a five out of ten.

We use different data science platforms for customer-specific projects. Whatever is being requested by, or is required by the customer, we learn it. Python is one of the technologies that we have a lot of experience with, and it is part of Anaconda.

Our primary use case is analytics. We use Anaconda to build models that predict the probability of an event, or it can be used for classification purposes. There are various uses for this tool.

One of the things that we do is subrogation and I can explain by using the example of a car accident. When an accident happens, you take your car to your insurance company and give them details about what happened. Also, the advisor at a service center will write down relevant information and supply it to the insurance company as well. At this point, the insurance company reimburses expenses for all of the damages that you have incurred. At the same time, they would like to find out if there is any fault that can be attributed to another person. If so, then they want to know whether it is possible to make any kind of recovery from that person or their insurance company.

With thousands of these claims coming into the insurance companies, it is very difficult for somebody to read all of the information and decide whether there is a potential for recovery or not. This is where our application comes into effect. We read all of the data into our software, which is built with Python using Anaconda, and try to gain an understanding of each and every case. This includes many details, even claim history, and we try to assess what the chances are of recovery or what the chances are of subrogation in each case.

This is just an example from one of our several clients. Each customer has different requirements and we customize a solution based on their needs.

The most valuable feature is the set of libraries that are used to support the functionality that we require. We use different libraries for finance and numbers, and we use the scikit-learn library for machine learning. A few of these libraries are very helpful and there is a very long list of them.

I think that the framework can be improved to make it easier for people to discover and use things on their own.

They need a better interface because currently, we have to do everything through coding. It would be nice to have a simple description of what each library is used for and how to use it.

I would like to see additional libraries included to support computer vision and natural language processing. The framework gives us the ability to create them, but having more in place would mean that we would need to do less coding.

Stability is not something that we really consider for this solution. When we are using Anaconda, we have to develop most of the things from scratch. It's a framework, and it is one of the tools that we use so that we do not have to think about dependencies. When I have Anaconda in my environment, I do not have to think about any prerequisites that may be required.

We have not been in contact with technical support to this point.

The initial setup is straightforward and not too difficult.

The length of time required for deployment changes after the first time. If somebody has to build everything then it takes longer. However, once all of the libraries are built, it takes one person perhaps three hours to deploy into production if it is done without interruption.

We have our own team for deploying this solution.

This is a great tool to work with, even if you are starting your career in analytics or another stream like data engineering or data science. This is a tool for everyone because you don't need to think about many things, such as what needs to be installed.

I would rate this solution an eight out of ten.

I use the solution for learning purposes only. I don't use it for any production standard quota, and have not deployed it.

When I try to develop an implementer model, I can just do some trial and error and see the output immediately. Then later I can move them onto the Python file. Until that happens, I can see that it's there. That's the most useful feature for me in Anaconda.

We use Jupyter notebooks and it was all available in one package when we installed Python.

The interface could be improved. Other solutions, like Visual Studio, have much better UI.

In Visual Studio, we can create projects. I'm not sure it's that kind of feature is possible on Anaconda. In Visual Studio, we can bring a solution and then we can keep multiple projects organized and into it. If there are any libraries, we can have them as well and we can just call them. We can easily link the libraries into the project. This should be possible on Anaconda as well.

I've been using the solution for two years.

The solution is stable.

The scalability of the solution is good. I'm not sure if we'll be increasing usage in the future.

The initial setup was straightforward. We haven't made a deployment; we just use it for learning purposes.

I'd rate the solution seven out of ten. I haven't been using the solution for too long, and I'm still learning things, so I need to explore more before rating it higher.

I use this solution for some of my assignments. Basically, it is used to take data from our database, analyze it, and make predictions.

The most advantageous feature is the logic building.

The Python libraries are all readily available and there is no need to install anything separately.

There are many good things that are pre-built, and including even more of these would be a great benefit to the developer community. It would allow us to try specific models and use cases, then customize them as per a particular activity.

I would like to see the inclusion of some statistical modeling functionality.

Having some examples built-in that we can customize based on the use case, rather than having to build the entire model, would really be an advantage.

Additional support for the visualizations would be an improvement.

The ability to schedule scripts for the building and monitoring of jobs would be an advantage for this platform.

I have been using Anaconda for about a year.

The stability of the platform is quite good.

I have not tried to scale Anaconda, but we will be working on that shortly. At this time we have between ten and twelve users, and we are looking at how to extend that and still be compliant.

All of our users are technicians.

I have not contacted the technical support directly because to this point, we have relied on help from friends, colleagues, and the community. Most of the problems, we have been able to sort out ourselves.

Prior to Anaconda, we were using SAS for some of our predictive analytics.

The main reason we switched is because of the licensing cost that is incurred for these kinds of specialized software solutions. In addition, functionality is limited to predictive modeling and some specific types of analysis. In Anaconda, there are a lot of other aspects that you can try. 

I found the initial setup to be moderate. It was not too complex nor too easy. We had a couple of people who were working on it and we were able to sort it out with assistance from the community help channels.

It takes between three and four hours to complete the setup entirely.

We implemented this solution ourselves.

The licensing costs for Anaconda are reasonable.

Our team is working on expanding the use of Anaconda. They're doing some research with respect to some of the libraries and modules, trying to do different things with existing datasets. I have been doing some slicing and analysis based on what has already been developed, and we are trying new things now.

My advice for anybody who is implementing this solution is to start with a straightforward deployment. However, if they want to start with deep learning immediately, the functionality is there, but I would recommend the full deployment.

This is a good solution, but there is a little room for improvement.

I would rate this solution an eight out of ten.

My position is master of data and we are a customer of Anaconda. Our primary use case was to find technological solutions to manage our warehouse in conjunction with our customer base. Anaconda enabled me to plot the data on a graph and find the optimal area for where our warehouse should be located.

I mainly used the product for the libraries. 

I hit some contribution issues and attribution problems, so the product could be improved in that area. There's always room for improvement. It's one thing if you have an IT guy with the solution, but there were some cases when it wasn't so simple. There were a lot of typos in the documentation and because we were using the product on-premise, the solution had to be implemented by the IT team here and they had some difficulty fixing problems, particularly from the wall sheet. I think better documentation or a step-by-step guide for installation would help, especially for on-premise users. That would be great.

I haven't used it enough to think about additional features and I didn't hit any roadblocks that made me think about that. It worked well for me. 

I've used it for a couple of months. 

I think it's pretty stable compared to the RStudio solution. There are some good tools which work better and quickly. 

I generally don't use technical support, so I don't have any experience with that.

The setup was quite complex. It was easy to get the whole way through, but we had some issues getting the correct function we needed and getting it properly. Deployment took around ten days to two weeks because our IT guys weren't able to work on it full-time. We didn't use any external help, it was our IT team who did the job. 

I would recommend having a good background so that you know what you're getting into and whether Anaconda is the right solution for you. If you have a strong IT team to support the solution it's a very good tool to work on.

I would rate the solution a seven out of 10.