Now-a-days the buzz word is Data Analytics. Customers are savvy and want to know how we can help analyze their data.
I feel Data Analytics should be part of every medium to large Dynamics 365 project. This is first of many blogs I will start writing for Data Analytics.
Good news is that Microsoft is already leading the way in Data Analytics through extreme investments in Dynamics 365, Windows, Azure, Machine Learning, SQL Server and IoT.
Data scientists are a special breed who work on data and have the art of making some sense out of it. Having said that it is not as simple as picking customer's SQL Server and start writing TSQL queries.
Data scientists should have the following
- Constant learning of current state of any project or business
Customer's data is the representation of its business in the form of numbers, text, dates and images.
- Fully understand the expectations and deliverable
Planning and the execution both depend on the end goal. A data scientist should be clear of what is expected so that the analysis and transformation can be performed accordingly.
- Knowledge of methodologies and tools
A data scientist should be aware of various methodologies and tools available at his/her disposal. For example, Microsoft has many tools for data analysis and they work with all kinds of data.
- Curiosity, Persistent, Patient and Focused
A data scientist sometimes need to churn Terabyte or Petabyte or Exabyte of data. Therefore they need to have constant curiosity and be persistent, patient & focused to explore, visualize, slice and dice data.
- Technical Savvy
Data scientists should know how to work with raw data. They should be able to transform it to an easy format & visualization so that correct analysis can be made and timely decisions can be reached. Data scientists should be good in math and statistics. Statistics is a branch of mathematics dealing with the collection, analysis, interpretation, presentation, and organization of data.
What does a Data Scientist Do?
- Data scientists spend most of the time preparing data
Data scientists need to clean, prepare and process raw data so that it can be analyzed.
- Run software programs against data
Data scientists use various software programs and languages to analyze data. For example SQL query language, R, Python, Hive, Hadoop, Microsoft R Server, Power BI, Excel, etc.
- Prepare Reports and Visualizations
Data scientists produce reports, charts and tables for easy understanding.