Data Scientist VS Data Analyst – A very common question that makes you think!
- Innovating and figuring ways to fetch, build and create data sets.
- Technical background, knowledge of certain coding languages like Python, Scala, R, SQL, Spark, Hadoop and many more.
- Opinion based on Data and objectivity required while referring to the data points.
- Should be highly skilled in particular languages and data algorithms, certain skills that are frequently required, e.g. Machine Learning, statistical modeling.
- More on the technical side.
- General job features: Strategical decisions to fetch the data from the sources, writing algorithms, scraping data and providing a final data lake, running scientific analysis using R, etc.
- Higher Salary (based on Market trends).
- Interpreting the data, using tools, bridging the gap between Tech and Business.
- Tech and business background with good knowledge of excel and visualization tools. Absolutely welcome some technical knowledge so that any data glitch or technical lag can be highlighted and acted upon.
- Reflecting on the data provided and building a useful story which will help in taking decisions; tools like SAS, SRS, google analytics, R, etc.
- Should be a juggler and good knowledge of both the frontend and backend to web the right story.
- More on the business side.
- General job features: data collection, data cleaning, analysis, data visualization.
- Medium to high salary based on background and experience.
There is always a cusp of a job which might require both the skills, the subset of the above mentioned skills is a hard to find and great to have. Looking forward to your comments and suggestion.