Data Science is the hottest field today in the job market. It requires a multitude of activities such as loading the data, data cleansing, processing the data, analyzing the data, identifying the hidden patterns, use them for prediction, and visualizing and reporting. Naturally, a lot of skills and techniques are required to bring each of these activities to fruition. Is Python for Data Science a better choice? Can Data Science do without Java? Or Java is a necessity? What is Java used for, if used in Data Science? Let’s see each of these questions in this blog.
What are the various skills required for a job in Data Science?
Data Science is flourishing. The year 2020 seems to be very big for this technology with experts predicting huge growth in demand for data science jobs this year.
There are numerous careers involved in the Data Science niche such as Data analysts, Machine Learning Engineer, Data Engineer, Data Scientists, and many more. Here we have compiled a list of essential skills and the corresponding programming techniques needed to implement the Data Science activities. However, based on the career choice in the Data Science field, the intensity of the following skills scale up/down.
Database Query Language – SQL
Data processing/storing – Big Data tools such as Hadoop, Hive, Spark, MapReduce come into play.
Programming Skills – This could be statistical or non-statistical programming language like Python/R/Java.
Statistics – A good understanding of statistical concepts is essential for Data Scientists.
Machine Learning – These algorithms can be implemented using R or Python.
Data Wrangling – These skills are central to a job in the Data Science field. This is a part of Exploratory Data Analysis (EDA). Many top Data Science professionals spend less time on this part and end up with shortcomings in providing the right solutions to business problems. Python, R, Julia, SQL skills are required for Data wrangling.
Data Visualization and reporting – Tableau is one of the most popular data visualization and dashboarding tool.
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Where does Java fit in?
Python and R have established themselves as strong programming languages in Data Science. However, Java also can be a major contributor in the field of Data Science.
- Java has numerous libraries for Machine Learning and Data Science such as Weka, Java – ML, MLlib, DeepLearning4j, etc.
- Many Big Data Frameworks are written in Java such as Spark, Hive, and Hadoop itself. What’s more, it is easier to find a Java professional who is comfortable working on these tools.
- JVM (Java Virtual Machine) allows the programmer to write reusable, portable code across platforms.
- It yields faster results.
- Java is ideal for scaling applications. Java can prove to be a perfect choice for building applications from the ground up, for complex and large ML/AI applications.
Is learning Java mandatory?
It is definitely good to know Java used for Data Science field. However, if someone says that Java is mandatory, then it is a myth.
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