Data Scientist Salary and job profile is an icon for technical youth these days. People from different job profiles are switching to Data Science. There is a list of programming language which you can use for Data Science. In the list of preferable programming languages for Data Science Python and R are leaders. Now the question gets up, “How is Java for Data Science ? “. Can we build a Machine Learning Model using Java? If yes then How long does it take a Java Engineer to learn Data Science stuffs?
If you are a Java Engineer or having academic Java Background, this article may be a turning point for you. All you need to stick with this article by ending ( How is Java for Data Science ? ). The best part of this article is,” You will get complete learning path of Data Scientist using Java in 7 steps “.the best and Strange part is you must have already used these API ( mention in learning path ) in your regular development work. The only difference is the objective of why you use it. This article will help you to connect your past created dots I mean your old knowledge of development into Data Science. In this article, I will let you know about –
Table of Contents
- 1. Learning Resources –
- 2. How to Acquire data from ( PDF, CSV, Webpages, APIs, etc) using Java?
- 3. Cleaning of extracted data using Java-
- 4. Java Libraries for Data Visualization-
- Java for statistics-
- 5. Text Analytics using Java ( NLP )-
- 6. Machine Learning Java Libraries-
- 7. Deep Learning ( Neural Network) using Java-
- 8. Big Data with Java-
- 9.How a Java Engineer can Transform his career into Data Science (Motivation for Migration )-
- Conclusion –
1. Learning Resources –
I never ignore this section, If you really want to start your career into data science form Java background, Reading an article is not enough. Although for an overview these are good for proper understanding, you should read some books.
In my personal opinion, It is a good book with hands-on code. Frankly speaking, I have created this article after reading this awesome book. It is best for beginner and Intermediate guys in data science from java background. This book has 12 chapters. Each chapter is full of handy code as an example.
This book also covers data science basics into java. This book design will enable you to write production-ready data science application. It covers java basics very first followed by data science ( Machine Learning basics ). See this step by step approach to make you ready to grasp complete concepts. Otherwise directly jumping into complex topics XGBoost and Neural Network may confuse you.
2. How to Acquire data from ( PDF, CSV, Webpages, APIs, etc) using Java?
As a Data scientist, you may have to extract the data from different data sources. Data could be structured or semi-structured like CSV or any SQL table. It could be unstructured like PDF, Twitter feeds or Facebook feeds. So You need to be an expert at this part first. Here are some Java API which you use to extract the data from these different sources and data Formats.
Java Libraries for PDF (Portable Document Format ) extraction –
There are several API exist for working on Pdf extraction stuff. There is a lot of stuff that you can easily do with these API. For example Pdf to Text Conversion, Split and Merge, etc. Here I am listing few of them –
Java Libraries for CSV extraction –
As long as CSV ( Comma separated value) data extraction in Java is concerned, I think you should go with OpenCV API. You will get complete documentation and Implementation example in popular coding websites like StackOverflow etc. It is the most popular API / Tool in general data extraction .because most of the time small training data have CSV format.
See ! you can do basic operation in CSV without using any API. All you need to use Scanner class to read and Load into List. After it, you can tokenize it using default Java Tokenizer.
Java Libraries for JSON manipulation –
As a Developer or Data Scientist, It is really difficult to say no to JSON. The clear reason to use JSON because usually when you call any third party API (Rest API ), You get the JSON response Right?
JSON has three different data processing model –
1. Streaming API-
It is useful when data has a large size. In this model, data is processed token by token.
2. Tree Model –
It is useful when the data size is small because it loads the entire JSON into the Memory.
3.Data Binding –
This model converts entire data into Java Object. Here are some API for JSON data Handling –
Java Libraries for XML manipulation –
XML ( Extensible Markup Language ) is used by Application communication. It consists of elements and tags which give it a structure. To handle XML in Java, You may use JAXP. JAXP has three interfaces for processing
DOM (Document Object Model )parser –
It processes the whole document ( all elements at once). As it processes all elements at once it takes more memory resources. Obviously it also gives you the flexibility to access any element at any point in time.
2. SAX (Simple API for XML )-
It processes a single element at one time. Obviously when your APPLICATION has memory concern. It is the best way of handling XML in all of the three interfaces.
3. StAX ( Streaming API for XML )-
It is a hybrid model for the above two. It just trade-off between performance and Resources.
Java Library for Image Processing –
Image processing is one of the hottest topics in data science. How can you make sense out of an Image? It is really harder but OpenCV ( Open source computer Vision Library ) can make your life simpler. As a data scientist, you may need to resize or smooth an image. Apart from it, you may have to change the format, etc, I mean there are a variety of common tasks which you need to perform. OpenCV contains all such functions all you need to call them.
3. Cleaning of extracted data using Java-
The above section has a complete focus on capturing data. This section will lead you in cleaning your data. Whether for complex machine learning models or simple analysis Data should be validated around its completeness, uniformity, accuracy, and consistency. In order to achieve that every data scientist plan a pipeline of the process to clean it. There are so many names for this cleaning process data wrangling, data massaging, reshaping, or munging.
Process for data cleaning –
Regular Text Processing –
If your data contains text, You may need to tokenize it. Most of the time you need to trim it also. All replace functions and lower upper case resolution are there in the core Java library. If you are more specific around it there is still the third API for it. My purpose here is to introduce you to this step and just give you an overview to achieve it.
2. Data imputation –
Missing data can make your analysis or prediction inaccurate. In order to go safer side, You should handle that advance. These missing values can be replaced by null or empty.
3. Subsetting data your data –
This process is somehow related to sampling. If the data size is too large and you can not use it simultaneously, You should break into the part. The important thing is this sampling must be uniform.
4. Another optional cleaning process-
Steps like sorting the data in a certain order are important but not mandatory. I will recommend you to validate the capture of the data. For example – you are scrapping a web and filtering all date values into it. Suppose you got the date but in different formats or Timezone.
So there are strong APIs for data cleaning which makes better Java for data science.
4. Java Libraries for Data Visualization-
Well till now, We have seen the API and process for data capturing and cleaning it. Now it is important to visualize it. In order to identify the pattern, Data visualization is a good way because humans understand better in pictures. Here you need to be sticky with Java API. In that place using any third party tool is a better way. Here is the list of Best Data visualization tools for data science.
Still, you want to do in Java code, You can easily achieve with –
These graphical APIs are really easy to use. You will easily get the error trace on the open-source community. These give a strong base for java for data science. You can easily create a Bar chart, Histogram, Donut chart, and much more with these libraries.
Java for statistics-
Truly speaking, Here is the area where real data science work starts. Now you must be thinking, ”So what were we doing earlier “. The answer is pretty simple it was pre-processing. This pre-processing is equally important as machine learning or data mining stuff. Statistics is the heart of data science.
Here Role of API is very important. You can also do the basic task in a core programming language but API can save a lot of time. Usually, programming of such a statistical algorithm takes a longer time. It also needs so much of optimization. As a Data Scientist, there are four statistical tasks which you need to perform on daily basis –
1.mean, mode, and median ( Central tendency )
2.Standard deviation and sampling.
You may use Apache Common and Guava API for the above task. Before practicing the syntax I will suggest going through such basic concepts of correlation, standard deviation. Now, let’s move to the next section of java for data science.
5. Text Analytics using Java ( NLP )-
Text analytics is one of the hardest fields. The good news is,” There is still a lot of opportunities in NLP “. In the continuation of series java for data science. There are some powerful NLP framework which you should try-
Awesome set of libraries for all NLP stuffs. Using you can achieve the functionality of Name Entity Recognizer, Lemmertizer, stemmer, dependency parsing, and much more. It contains a multi-language corpus, So It’s possible now that you may use the NLP model for different languages apart from English. It has a good accuracy model for Sentiment Analysis.
Every NLP library does the same task for you like ( POS tagging, dependency parsing, etc ). The difference arises at the accuracy level. The accuracy also varies for different domains. Although while designing such a model, Training data is uniformly distributed.
This library brings the power of deep learning into the NLP domain. We have already gone through it in the Deep learning section. Actually these NLP frameworks and models are built on a huge corpus which slowdowns the performance some time. With DL4J there will be no performance issues as well.
4. Other Java NLP libraries –
There are few for java NLP library which is also quite useful. Please have a look at it-
How to Progress on NLP with Java –
I know, you must be thinking if there so many NLP frameworks which one should I learn or I have to learn all. Right? See, You need not learn all just go through the documentation of anyone. Make sure you understand the concept and functionality of NLP stuffs like ( Tokenizing, NER parsing, POS tagging, etc ). Once you have a basic understanding of the NLP concept, All you need to see the Syntax which is no big deal.
6. Machine Learning Java Libraries-
I often found people are confused about java for Machine Learning and java for data science . See both are different Machine learning is a part of data science. To uncover the basics of machine learning, Read the article – ” What is Machine Learning “. Now to implement Machine Learning in Java use these java machine learning libraries –
There are so many machine learning algorithms under each machine learning category ( Supervised, Unsupervised, Reinforcement ). You will get the module for these machine learning models in these Java machine learning libraries. All you need to fit these modules into your code and tune the parameter.
7. Deep Learning ( Neural Network) using Java-
The most popular word in the AI environment is Deep Learning these days. Before reading this section ahead it is essential to read the Difference between and Machine Learning. In Java we have –
In these three, Deeplearning4J is the most popular ( personal opinion). Using these API, You can build complex Neural networks like ( Recurrent Neural Network, Conventional Neural Network, etc ).
8. Big Data with Java-
This is AI and Internet era where every other second we create some data. To handle these data we need huge resources support. To solve this problem technology came into the picture is Distributive computing. The overall system needs a distributive algorithm and node connected as a data resource. Managing everything at the application level was really harder. So as a solution people start building a framework for big data. Here is the popular name of these big data frameworks-
9.How a Java Engineer can Transform his career into Data Science (Motivation for Migration )-
Java is a very popular and mature programming language for Enterprise applications. There is a big bucket for java backed application which is established and performing exceptionally well in the market. Application Framework like Spring, Hibernate auto handles most of the overhead of Infrastructure in Software development. Yes, I agree Java is almost perfect. Like every coin has two phases As you already familiar with the fact, How the IT industry is growing in a very rapid manner. Every other day, We encounter a new framework or new skills for different use cases. So cant sticky with your current job role. In the list of Top job roles for this century, Data Scientist comes first. So the point for discussion is How a Java Engineer can Transform his career into Data Science and how is java for data science.
The pain area is, suppose you have been working on java for the last 10 years. Now you need to learn different languages like python like a fresher. I agree with the fact that If you are hands-on one programming language, It will be a cake to switch on another. Apart from this I always recommend learning something new but If you can achieve the same thing in Java, It would be awesome right ? Especially when you need to finish something very quickly. You can finish the task 50 percent faster in Java.
Sometime when you cant change the older technology stack which is in Java. Now you need to add some data science analytics on the top of it. You can do it if you know these libraries and little basics. So far we have seen there is nothing which we can not achieve in Java.
This article is a learning path for Java Data Scientist. In Java, you achieve everything which you can achieve in Python, R, and Julia. I agree that sometimes you need to write larger code . Especially If you have hands-on experience in Java it will be too easy for you. I hope you like this article. Please write your comment on – ” How is Java for Data Scientist ?” .You may share this article who are Java developers and looking to change their Job into Data Science.
Data Science Learner Team
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