If you ask a digital analyst on how they he would like to develop their career, he might respond that he would like to get into data science or data modelling.
A digital analyst is not usually focused on advanced data modelling.(building, evaluating and deploying models). At the same time, there is an overlap on some of the actions an analyst and a data scientist do.
Actions in column 1 and 2 below can be done by a good analyst and a good data scientist can usually do actions in the columns 2 and 3.
Both an analyst and a data scientist could visualize data, present analytics results, need to understand business context and need to make recommendations.
An good analyst can establish causality and test for statistical significance but predictive models or advanced statistical analysis are used more often by a data scientist.
What are some reasons for a digital analysts to learn other tools?
1. Advanced custom Data visualization:
An Analyst who has reached a good level on data visualization, might decide to learn R or Python to build more advanced custom visualizations of data.
2. Data modelling for impactful recommendations:
Another Analyst might focus on learning how to apply predictive models such as regression to the datasets they work with in order to deliver more impactful analytics recommendations.
With an array of tools at the disposal of digital analysts and an even more varied range of skills digital analysts can learn, which ones are worth investing in for 2020?
When it comes to data modelling tools, research shows top contenders to be a matchup between Python, R and SQL.
I have created on twitter a short poll to see the level of interest for each and to see also other responses. From 531 votes Python was the top choice (47.1%) followed by SQL (21.8%) and R (19.4%)
Other studies show Python to be the leading choice for selecting a modelling coding language
For example, according to the latest trend search on Indeed, Python is said to be the most popular language for artificial intelligence and machine learning. This is further strengthened by the fact Python is generally considered easier to learn, beats both R and SQL in terms of being a primary modelling coding language.
Python also shows at the top when it comes to one of the most regularly used data science tool.
What can you do with Python ?
- Data scraping
- Data analysis and mining
- Data visualization
- Natural language processing (NLP)
Links to Python resources and examples:
Python and Google Ads – KPIs reporting
Spy on competitors using Data Studio and Python
Python & Search Console – The goal of this exercise is to see if the content of your site is optimised for the search queries that have converted in the past.
Python and Google Sheets/Excel:
Predicting CTR in Python ,generating keywords for marketing campaigns , Projecting future trends
Python + Facebook: In 2017, Facebook made itsProphet open source. The forecasting tool is accessible through Python and R and is optimized for businesses to forecast trends, whether they’re hourly, daily, weekly, or seasonal.