Necessary Programming Languages and Software Used in Data Science

1. Programming language enables you to devise programs that can execute specific operations. Moreover you can reuse these programs whenever you need to execute the same action.
2. Python and R language are not able to address problems specific to some domains. One example is relational database management systems. There SQL is king.
3. SQL at its most advantageous when working with traditional historical data. When preparing your BI analysis. For instance, you will surely employ it.
4. Python and R are suitable for mathematical and statistical computations and they are adaptable.
5. Matlab is invitable. It is ideal for working with mathematical functions or matrix manipulations.
6. Java and Scala are very useful when combining data from multiple sources.
7. Applications software or software solution is a small scope, a lot easier to learn.
8. Hadoop is listed as a software in the sense that it is a collection off programs but don’t imagine it as a nice looking application. It is actually a software framework which was designed to address the complexity of big data and its computational intensity. Most notably Hadoop distribute the computational tasks on multiple computers which is basically the way to handle big data nowadays.
9. Power BI, Sad, Qlik and especially Tablo are top notch examples of software designed for business intelligence visualizations.
10. In terms of predictive analytics EViews mostly used for working with econometric time series models and Stata for academical statistical in econometric research where techniques like regression cluster and factor analysis are constantly applied as a final note.
11. A data architect creates databases from scratch. They design the way data will be retrieved processed and consumed. The tasks of the Data Engineer step on the work of the data architect. His primary job responsibility is to further process the obtain data so that it is ready for analysis. So the result of his work is something analysis and people in the analytics positions will heavily rely on. A claim an organized data set. In fact, the data in a database is not created once and for all you have a certain flow into and from the database and there is person who handles this control of data. Her position is database administrator and she mainly works with traditional data. Needless to day Administration of big data is usually automated.
12. BI analyst will do analyses and reporting of past historical data. BI consultants are often an external BI analysts. Many firms outsource their data science departments as they don’t need or want to maintain one. BI consultants would be BI analysts had they been employed. However, their job can be more varied as they hop on and off different projects. BI developer is a person who handles more advanced programming tools such as Python and SQL in order to create analyses specifically designed for the company. It is the third most frequently encountered job position in the BI team of a firm.
13. Data scientist employs traditional statistical methods or uncoventional machine learning techniques for making predictions. Data analyst prepare more advanced types of analyses and do the basic part of the predictions of the data science team.
14. Machine learning engineer is looking for ways to apply state of the art computational models developed in the field of machine learning into solving complex data science and business tasks.
15. SWOT analysis is a famous type of qualitative analysis contributing to the strategical decision making of a company. It points out the strengths and weaknesses of running a particular business. A SWOT analysis can improve the firm’s strategy but it is not a data driven analysis.
16. Storytelling is a crucial skill for a data scientist. They should have the ability to express complex mathematical and programming concepts to end users such as company managers and high level executives in only a paragraph and a single visualization to achieve this. Using the Excel, Stata and Spss are still frequently used.
17. Machine learning and AI are not particularly old disciplines and because they are tightly woven to the evolution of technology many developmends are still ongoing. As such these developments are surrounded by disputes from scientists and academics who have not reached firm conclusions. For example it is well known that deep learning algorithms may let a model perform exceptionally well.