I would highly recommend Python because it is a really nice language and ecosystem. I learned Python as an engineer who became a Data Scientist for a start-up for about 3 years. I learned JavaScript after Python and it took more effort to learn JavaScript.
There are analytics workloads that can be done in Python to produce data visualizations that are very powerful. It's large growth in recent years puts it in the top 3 languages and probably the #1 scientific computing language. It's the default language in most machine learning (ML) applications now.
JavaScript is now my favorite because it is so core to the web & creating visual anything in a browser, but I will use Python again soon on data analytics workloads that will come from my Meteor app data in MongoDB.
As a Data Scientist, I tried out many many environments for the most productive analytics platform, and I did end up with a favorite called Databricks.
This environment is sufficient to learn most of what you need with Python, although you'd for sure use your local machine too. The Jupyter notebooks are a very popular open-source local alternative to Databricks.
Databricks was built by the creator of Apache Spark, and there are also Python APIs to access all the of the distributed computing resources made available in Spark. This environment makes it really easy to eventually learn Spark & Scala too. amplab.cs.berkeley.edu/software/ (check out the Amp Lab stack)
Here is how simple it is to connect MongoDB to Databricks and run some ML analysis on the data. This example is in Scala, Scala is a lot like JavaScript in my opinion, so it's also super easy to pick up. docs.databricks.com/_static/notebo...
Here is a simple Python notebook in Databricks using the Plotly library which I also really like a lot.
I would highly recommend Python because it is a really nice language and ecosystem. I learned Python as an engineer who became a Data Scientist for a start-up for about 3 years. I learned JavaScript after Python and it took more effort to learn JavaScript.
There are analytics workloads that can be done in Python to produce data visualizations that are very powerful. It's large growth in recent years puts it in the top 3 languages and probably the #1 scientific computing language. It's the default language in most machine learning (ML) applications now.
JavaScript is now my favorite because it is so core to the web & creating visual anything in a browser, but I will use Python again soon on data analytics workloads that will come from my Meteor app data in MongoDB.
As a Data Scientist, I tried out many many environments for the most productive analytics platform, and I did end up with a favorite called Databricks.
There is a free community edition that you can try/use here: databricks.com/product/faq/communi...
This environment is sufficient to learn most of what you need with Python, although you'd for sure use your local machine too. The Jupyter notebooks are a very popular open-source local alternative to Databricks.
Databricks was built by the creator of Apache Spark, and there are also Python APIs to access all the of the distributed computing resources made available in Spark. This environment makes it really easy to eventually learn Spark & Scala too.
amplab.cs.berkeley.edu/software/ (check out the Amp Lab stack)
Here is how simple it is to connect MongoDB to Databricks and run some ML analysis on the data. This example is in Scala, Scala is a lot like JavaScript in my opinion, so it's also super easy to pick up.
docs.databricks.com/_static/notebo...
Here is a simple Python notebook in Databricks using the Plotly library which I also really like a lot.
Link to Python Notebook
Anyway I hope you enjoy learning any new languages you pick.