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Recently, I have spent a lot of time interviewing many small and medium-sized enterprises, data scientists, business analysts, leaders and customers, programmers, data enthusiasts, and experts from different fields around the world to compile a list of these technologies. This field remains for a long time.
R language programming
Data scientists like R for many reasons. Although it has a simple syntax, it is very powerful in dealing with various complex data-driven tasks, selection of statistical tools, and massive function libraries, and it is easy to install. It complements ggplot2 (based on image grammar) to create a refreshing dashboard. Shiny makes it easy to build interactive dashboards.
Python
Python is a mature object-oriented programming language. In addition to regular data science, it is also specifically used for deep learning, web development, and software development. Development frameworks like Django and Flask can build better web applications more easily and with less code.
I investigated the choices of python and R users in depth, and tested whether they would be willing to switch to other programming languages. The following are their views and brief responses.
Data scientists studying R language programming expect more support in the fields of deep learning and computer vision. Among the people I have communicated with, R users can use tidyverse, dplyr, and data.table to perform first-class data operations very easily. Moreover, most users have statistical background, ETL, IDE and data processing capabilities, and can perform complex data processing faster.
Python users have already heard of ggplot2 and are very impressed with it, and expect matplotlib & seaborn to achieve the same effect. They believe that the complexity and speed of data manipulation can be improved. Python users have great advantages in using theano, TensorFlow, and Keras. Some of the industry's best APIs are written in Python.
SQL (Structured Query Language)
Data is everywhere. How to easily extract the data we need and/or how to interact with the data is a major challenge we face today.
SQL is a language for communicating with databases. Many interviewees said that SQL is a programming language for data manipulation and retrieval that must be understood to interface with various databases. Big data is also an interesting topic, you can refer to sparklyr and pyspark. Users of Python and R can connect to various databases and communicate with data tables from their IDEs.
Java
Java programming has a huge fan base. In the field of software development, this emerging programming language is still hot. In this era, JavaScript frameworks like react.js and Vue.js are becoming more and more popular in the field of advanced web development.
Adobe AfterEffects (a graphics and video processing software from Adobe)
When discussing the activities surrounding data2insights, a senior director of data science called on everyone to create some cool infographics and regain data-driven animation. He believes that when transforming data into information, a lot of extremely interesting information is ignored.
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The leadership team has always relied on the data dashboards they have created, and embedded some highly creative “concrete” during construction. The “bricks” are pieces of valuable information.
In addition, data science using Tableau is now popular with visualization experts, and it has great potential in the field of business intelligence; Power BI seamlessly integrates with Microsoft's existing application ecosystem. Both are constantly innovating and the prospects are bright.
These are all technologies with great potential for development and endurance, and it is not a loss to invest in shares.
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