Anaconda: The Data Science Starter Kit.
Updated March 13, 2019

Anaconda: The Data Science Starter Kit.

Matthew Deakyne | TrustRadius Reviewer
Score 7 out of 10
Vetted Review
Verified User

Overall Satisfaction with Anaconda

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.

Pros

  • Clear install story. There are a lot of ways to install python. There's only one way to install anaconda. This makes teaching and standardizing much easier.
  • Batteries included. It's easy to install things in python, but anaconda ships with most of what you need out of the box. This helps with standardization and reproducibility.
  • Good integrations with Jupyter and other visual tools. Jupyter is really convenient when learning various python packages. Anaconda makes these tools easy to launch and to use.

Cons

  • Doesn't play well with other Python. I use python for more than data science, and whenever I have multiple versions of python on my machine —some using conda, some using Pipenv, some using poetry— it can get really confusing. If Anaconda is all you use, then it works really well.
  • Not all packages are available in Anaconda. Conda install doesn't always work for all PyPI packages. This adds to the frustration above - as you have to install some packages outside of conda, and then figure out how to use them internally.
  • Visualizations don't always work like you'd hope. This is getting better, but creating interactive graphics doesn't always work well in this context.
  • Anaconda has helped us analyze academic data to inform business decisions.
  • Anaconda has helped us visualize complex data sets into understandable graphics.
  • Anaconda has helped us explore machine learning abilities and limitations.
I prefer Anaconda due to the control I have at every level over the data and the visualizations. Power BI does a better job at guessing what graphics to use, but these usually aren't the most helpful. Anaconda and the slew of Python extensions that add incredible functionality, make it much more powerful without such a steep learning curve as Power BI.
Anaconda is excellent if all you do is data science. If you are already a python developer, then it may be more frustrating having multiple ways to manage your distribution and your packages. It has very clear use cases and makes starting off in data science much easier than figuring out all you'd need to install in Vanilla Python.

Anaconda Feature Ratings

7.0
Connect to Multiple Data Sources
10
Extend Existing Data Sources
8
Automatic Data Format Detection
3
7.5
Visualization
9
Interactive Data Analysis
6
8.3
Interactive Data Cleaning and Enrichment
9
Data Transformations
10
Data Encryption
6
9.5
Multiple Model Development Languages and Tools
10
Automated Machine Learning
9
Single platform for multiple model development
10
Self-Service Model Delivery
9
8.5
Flexible Model Publishing Options
9
Security, Governance, and Cost Controls
8

Using Anaconda

15 - Most of the users are researchers or analysts in some capacity. It is featured in Software Carpentry workshops, and so the usage has expanded to Academics and some Staff members.
2 - The two supporters in an ongoing basis are based in the Libraries, and their positions are to support data practices in research. They have functional programming skills, such as version control and unix command line expertise, as well as scientific programming skills such as R and Python. They instruct on how to install and use Anaconda in practical workshops, and also have consulting services.
  • Data Analytics. Evaluating data sets.
  • Data Exploration. Visualizing relationships within data sets.
  • Reporting. Creating reproducible reports.
  • Introduction to programming environment. It works well as an introduction, as the install story is really clear.
  • Data literacy. Helping non-programmers and non-statisticians better understand the capabilities of machine learning and other computing techniques.
  • Data pipelines. Moving data from one system to another is handled by many different systems - anaconda and python make this fairly easy.
  • Replacing programs such as Excel.
  • Generating interactive websites.
  • Automating reporting across the University.
It's really good at data processing, but needs to grow more in publishing in a way that a non-programmer can interact with. It also introduces confusion for programmers that are familiar with normal Python processes which are slightly different in Anaconda such as virtualenvs.

Using Anaconda

It's really good at installing and getting started. It's less usable and configurable after that. If you stay in the ecosystem, and don't know how to Python any other way - it works really well.
ProsCons
Like to use
Easy to use
Technical support not required
Well integrated
Consistent
Quick to learn
Convenient
Feel confident using
Familiar
Unnecessarily complex
  • Installation. This is the highlight of anaconda. It's really easy to install.
  • Launching interactive consoles. It integrates with Jupyter really well.
  • Getting started. All the libraries you'd need are included.
  • Managing Python virtual environments.
  • Version control. You can use git, but better integration would be nice.
  • Developing interactive websites. It's not really intended for this.

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