Data analyst at a tech vendor with 5,001-10,000 employees
Real User
Top 20
Dec 8, 2025
I have been using Anaconda Business for data science practice over the last two years. Anaconda Business serves as my primary tool for data analysis of data science projects. I specifically use Anaconda Business for Jupyter notebooks, where I employ the Python language for predictive modeling and data analysis.
Automation Test Engineer at Tata Consultancy services Limited
Real User
Top 10
Nov 1, 2025
My main use case for Anaconda Business is centralized package management and security and compliance for a private repository and team collaboration with enterprise integration in my work. For example, my company has 50 data scientists working on different machine learning projects. Normally, everyone installs packages such as NumPy, Pandas, or TensorFlow directly from the public internet, which can cause problems including different versions of the same package causing conflicts, security vulnerabilities, or unapproved licenses, and a lack of control over what is being downloaded from the internet. With Anaconda Business, the IT admin team can host a central private repository of approved packages, block or restrict unapproved or risky packages, and ensure everyone in the company installs the same secure versions of libraries.
Anaconda Business serves as the main solution for security and compliance, scanning packages and vulnerabilities for our organization while ensuring air-gapped and on-premise environment safe access. Anaconda Business helps with security and compliance by blocking public packages that have malwares or license risks, keeping our company data, science, and environment secure and compliant. It mostly helps with security and compliance by providing a safe and curated package repository and checking for vulnerabilities by scanning it, so our teams can only use trusted, approved Python or R packages.
Senior Consultant at a tech vendor with 1,001-5,000 employees
Real User
Top 20
Oct 28, 2025
My main use case for Anaconda Business is basic data manipulation work through Python. A specific example of the data manipulation tasks I'm doing with Anaconda Business is cleaning data, as I use Jupyter Notebooks to write Python code to clean it up.
Product Engineer at a tech vendor with 10,001+ employees
Real User
Top 5
Oct 15, 2025
I typically use Anaconda Business for personal uses; actually, I use Anaconda machine rather than Anaconda Business. I use Anaconda Business mainly for development purposes. I use Anaconda Business for data analytics, as I train AI models using the many libraries that Anaconda provides, particularly with Python language for training purposes.
My main use case for Anaconda Business is serving my clients with the help of different paths of the platform. A specific example of how I use the platform to serve my clients is fetching data to serve and query the interview-perspective point of view. I have something else to add about my main use case with Anaconda Business; sometimes we use it in my startup cases or current hotspot queries. It's not shareable or public, but I use it in my startup also.
We use Anaconda for data science model and development, specifically for coding in Python. We use it mainly for forecasting and predicting models within the environment of Anaconda Python.
I have been very enthusiastic about artificial intelligence and machine learning since my first year. I started learning Python in my first year and was using a MacBook with the M1 chip, which didn't have native Python support. I discovered Anaconda, which developed Python for Mac, so I started using it for Python. Later, I realized its use cases in machine learning and data science.
Analytics Analyst at a tech services company with 10,001+ employees
Real User
Aug 13, 2020
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.
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.
Head - Data Science (Senior Program Manager) at a tech services company with 51-200 employees
Real User
Dec 16, 2019
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.
Master Data at a energy/utilities company with 1,001-5,000 employees
Real User
Dec 9, 2019
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.
Assistant Professor at Veermata Jijabai Technological Institute (VJTI)
Vendor
Jan 17, 2019
The best platform for a data scientist for development purposes. It supports applications which are needed for data analytics like Jupiter and predictive analytics like R.
Anaconda Business provides a comprehensive platform for data science applications, integrating extensive libraries and seamless Python and R compatibility, enhancing developer productivity.Anaconda Business offers data science professionals a platform combining extensive library support with pre-built models and seamless integration of Python and R environments. With features like a user-friendly interface and integrated Jupyter Notebook, it facilitates real-time code execution and debugging....
I have been using Anaconda Business for data science practice over the last two years. Anaconda Business serves as my primary tool for data analysis of data science projects. I specifically use Anaconda Business for Jupyter notebooks, where I employ the Python language for predictive modeling and data analysis.
My main use case for Anaconda Business is centralized package management and security and compliance for a private repository and team collaboration with enterprise integration in my work. For example, my company has 50 data scientists working on different machine learning projects. Normally, everyone installs packages such as NumPy, Pandas, or TensorFlow directly from the public internet, which can cause problems including different versions of the same package causing conflicts, security vulnerabilities, or unapproved licenses, and a lack of control over what is being downloaded from the internet. With Anaconda Business, the IT admin team can host a central private repository of approved packages, block or restrict unapproved or risky packages, and ensure everyone in the company installs the same secure versions of libraries.
Anaconda Business serves as the main solution for security and compliance, scanning packages and vulnerabilities for our organization while ensuring air-gapped and on-premise environment safe access. Anaconda Business helps with security and compliance by blocking public packages that have malwares or license risks, keeping our company data, science, and environment secure and compliant. It mostly helps with security and compliance by providing a safe and curated package repository and checking for vulnerabilities by scanning it, so our teams can only use trusted, approved Python or R packages.
My main use case for Anaconda Business is basic data manipulation work through Python. A specific example of the data manipulation tasks I'm doing with Anaconda Business is cleaning data, as I use Jupyter Notebooks to write Python code to clean it up.
I typically use Anaconda Business for personal uses; actually, I use Anaconda machine rather than Anaconda Business. I use Anaconda Business mainly for development purposes. I use Anaconda Business for data analytics, as I train AI models using the many libraries that Anaconda provides, particularly with Python language for training purposes.
My main use case for Anaconda Business is serving my clients with the help of different paths of the platform. A specific example of how I use the platform to serve my clients is fetching data to serve and query the interview-perspective point of view. I have something else to add about my main use case with Anaconda Business; sometimes we use it in my startup cases or current hotspot queries. It's not shareable or public, but I use it in my startup also.
I use Anaconda for predictions, building models, and incorporating them within the product for specific use cases.
We use Anaconda for data science model and development, specifically for coding in Python. We use it mainly for forecasting and predicting models within the environment of Anaconda Python.
I have been very enthusiastic about artificial intelligence and machine learning since my first year. I started learning Python in my first year and was using a MacBook with the M1 chip, which didn't have native Python support. I discovered Anaconda, which developed Python for Mac, so I started using it for Python. Later, I realized its use cases in machine learning and data science.
I use the tool for Jupyter Notebook.
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.
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.
We primarily use the solution for the data science class. It's used for people to build their data science models.
I was using this solution for buildings some PoCs, as well as during a hackathon.
I use the solution for learning purposes only. I don't use it for any production standard quota, and have not deployed it.
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.
I use this solution for some of my assignments. Basically, it is used to take data from our database, analyze it, and make predictions.
We use Anaconda to develop machine learning models. Use primarily use Scikit-learn and TensorFlow.
We primarily use the solution for deep learning and machine learning.
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.
The best platform for a data scientist for development purposes. It supports applications which are needed for data analytics like Jupiter and predictive analytics like R.