Anaconda Reviews

We're using it in our department for data-related business needs, data retrieval, data manipulation, data preprocessing, visualization, forecasting, and prediction. So whether the business problem is a simple data analytics problem or complex modeling, Anaconda is used in our department. We use Anaconda for its Python libraries that come as a package, which is great. Not to mention that it eases the pain of updating all packages that would otherwise be carried out one by one for each. We use Panda's library within Anaconda to read, manipulate, preprocess and write the data. We use Numpy for mathematical functions. We use Matplotlib and Seaborn for data analysis and visualization. Finally and most importantly, we use Sci-kit Learn to create predictive models because it contains almost all the algorithms we need. Sadly, it does not contain XGBoost, CatBoost or LBGM however it is easy to install those with Anaconda because that's what Anaconda is for - helping managing all these packages, whether it is installation or simply updating.

Anaconda open-source distribution is a flexible platform enabling users to work with several popular data analytics languages such as R and Python.

It is being used by Engineering and Geoscience teams to prototype custom algorithms for use in solving use cases in the oil and gas industry, including subsurface, operations and other relevant functional area such as health, safety and environment.

Anaconda is being used for data analysis purposes. We use it to mainly manage python distributions and to preload scientific packages that make working with data very easy. It's used by pockets on campus, mostly those who have research needs. It relieves us from having to purchase expensive software like SAS or SPSS and uses both Python and R.

As part of an overall strategic assessment, we decided to test the possibilities of several data lakes by conducting a collection of data science experiments. Anaconda was instrumental to get the data science experiments up and running in record time, including testing different data analysis packages, generation of notebooks, and the sharing of the results to a larger team.

I use Anaconda, Jupyter notebooks to automate few routine excel sheets for getting desired modifications on them, also I use Python Pandas, other visual libraries for sales data analysis for dashboards, identify opportunities, for business enhancement etc.

The data science and operation research team in our company majorly uses Python as the programming language. So Anaconda was chosen to provide one research platform, allowing the data scientists to work on one unified environment, across different OS, using the same language while being able to share the work progress as well as results and promote the team efforts.

Anaconda is use by all the different analytics teams at my company. It solves for a unified, easy to install, toolkit with all the base scientific packages an analyst might need.

We use Anaconda for all of our Machine Learning projects in Data Analytics and Reporting department. Primarily we use Jupyter Notebook, Spyder and RStudio functionality to create various machine learning algorithms to solve real world business problems, such as how to keep users in our game longer and how to better monetize their experience.

Anaconda is used by most members of my department who use Python. Since Anaconda is a cross-platform program, it makes collaboration among Mac/PC/Linux users relatively painless. Anaconda's package management system helps us maintain the most up-to-date Python libraries, which is important for working on code development within our department. Anaconda helps us avoid problems with installing python libraries that sometimes arise when installing libraries using pip. This, in turn, allows us to spend more of our time developing code and building software rather than troubleshoot issues with installing libraries. Anaconda offers several IDEs for python (and R), which makes writing code and debugging easier.

I use Anaconda myself, for Python, Spider and R. It’s used by the whole organization. In my area, we use Anaconda for importing libraries to train predictive algorithms that help our clients to estimate value sources.

I used Anaconda in my company to program in Python. It is used all across the Information Technology department when using this programming language and Jupyter and Spider. It is specially used to work with libraries as it's made easy with this software!

If you are doing Python analytics, it's possible but nearly pointless to roll your own distribution. There are only two main analytics distributions, and Anaconda is the better one. So use Anaconda. As a distribution, if you are doing other Python stuff, then Anaconda holds a lesser utility.

I'm still a bit new here, but we have many different places where we do analytics. Some people like SAS, some like R, some like Python, some like SQL, some like Excel, etc. Anaconda is the "no duh" default distribution for doing analytics or anything scientific in Python. In particular, once you do your pull from the server, it's really necessary to have a powerful tool for data analysis. SQL "can" do a lot of things, but it is just horrible for analytics. Like using vice grips to brush your teeth.

Anaconda Is largely used by myself, not by my organization, for management of my Python and Jupyter packages. Generally it does a pretty good job of importing and updating libraries that I use for utilization within Python programs.