Data Analysis is a key process that needs to build systematically apply relevant statistical methods and techniques on logical & physical data models, that is essential for data evaluation (or get near truth of data).
The above method is a traditional analysis method followed in a relational & datawarehouse platform scenario, in the current times Data Analytics is a new concept (science) that has developed enormous opportunities for the users to analyse, conclude and visualise information about relevant data set. Analytics will help organisations to sustain better business decisions and build data science capabilities to exisitng models/theory within their data platform. In my opinion, technology isn’t a barrier to sustain this kind of new trend.
By creating a data science, the organisation can build upon data collection methods, ingestion processes and end-results for entire data models that isn’t specific to a particular domain. Having said that Big Data is another trend catching up (fast paced in few parts of world) other parts of world to build data streams and collate multiple data sources for better insights with variety of software tools and machine learning programming methods. So there is a big question about which software or programming method is right for thi kind of job, not going into biased discussion on technology/vendor for data analytics/analysis kind of project.
In this cyberage information is available at tip of your fingers (with few clicks on your device). For specific bunch of users such as data stewards, report authors and data scientists there is a wide range of availability within software field, just to name a few:
- Power Pivot
- Google Chart
- Data Wrangler
- …..see more here at: Data Visualization & Analysis Tools chart.
The reason about why I have listed R on the top (which is a fact as well) is it will enable emphasis on data insights, build statistics and data visualisations that can solve real-world problems. No doubt that this is a favourite for professionals like data scientists and statiscians.
Not just with R, in this kind of scenario Python also matches up to the expectations with few differences on how one should evaluate data analysis/analytics software for their specific data science needs.
To put a strong foothold on Microsoft data analytics world, SQL Server 2016 has opened up enormous opportunities for data science stream by adding R integration into the database. In few words:
SQL Server 2016 expands its scope beyond transaction processing, data warehousing and business intelligence to deliver advanced analytics as an additional workload in SQL Server with proven technology from Revolution Analytics. Not just that, building PolyBase into SQL Server, expanding the power to extract value from unstructured and structured data using your existing T-SQL skills. With this wave, you can then gain faster insights through rich visualizations on many devices including mobile applications on Windows, iOS and Android.
In the recent times I have found a fantastic reference to comparison between R & Python, see below (source):
As a closing note I would encourage you to refer to what is available with Microsoft as well:
Few links as well: