Huge volumes of data are generated every day from IoT, social networks, and business systems which businesses use to extract insight for decision making and prediction. As organizations go through digital transformation, a trend that has picked up quite fast is integrating data into business processes. This data needs to be analyzed for it to be valuable to the business and this is where data science comes in.
In addition to Earning Python, R, SQL, and/or Scala certification, a data scientist should also consider acquiring ML and AI, statistics and maths, analytics, data visualization, and other data analysis related skills.
As data science evolves, some notable trends are worth considering.
Trend one: IoT Data Explosion
Almost 27 billion IoT connected devices generate as high as 2.5 exabytes of data every day. Data that influence business operations and data science will be central in extracting value from this data. The concept of data science for IoT, also known as IoT analytics, is driving business operations with the IoT analytics market expected to grow to $81.67 in value by 2025.
Trend Two: Automation in Data Science
Although automation has not been fully realized, it is gradually taking shape. From storage to data cleaning – a step that takes up much time to visualization, to exploration, and then modeling, automation plays a huge role in making the process efficient, cost-effective, and free from errors. Automation tools for data cleaning, visualization, and other aspects of data science are already being developed and deployed to give way to automated data science, automated feature engineering, and AutoML.
Trend Three: Data Science in The Cloud
The volume of data that is being generated currently is beyond the capacity of computers and on-premise servers. The cloud offers a robust infrastructure for big data storage and on-demand computing power with automatic scaling capabilities.
Cloud computing service providers like AWS, AZURE, and Google are exploring and offering powerful analytics tools alongside cloud storage facilities. The cloud gives data scientists ready access to resources like Spark and Hadoop frameworks while cloud computing products like Amazon SageMaker, Microsoft Azure ML, Google BigQuery, and IBM Cloud are capturing the attention of data scientists.
Trend Four: Data Science Security
Customers are becoming increasingly aware of the importance of safeguarding personal data. As businesses adopt data science technology, they are also seriously looking into technologies around data security. This is because the implications of a data breach can threaten the very existence of the business. On the other hand, guaranteeing the privacy of customer data earns the trust and ultimately the loyalty of customers.
What is Scala?
Scala is both object-oriented and functional programming language with highly scalable features making it a general-purpose language. It is an extension of Java and derives its name from its scalability advantage. Scala runs on Java Virtual Machine (JVM) and is interoperable with Java.
Scala is an ideal programming language when it comes to big data. It is supported by big data frameworks like Apache Spark and Apache Hadoop. These and several other data science frameworks have been written using either Scala or Java languages thanks to the fact that Scala has the capability of running parallel processes on large data sets.
Scala is a Vital Technical Skill for Data Scientists.
- Why Scala is used in data science
- Scala is interoperable with Java and can, therefore, run on JVM
- Employs both object-oriented and function-oriented programming techniques
- Its language is designed to be easily scalable an important feature when working with large data sets
- Scala works with relational databases like SQL
- It is designed to run parallel computations on large data sets
- It is the language used to write Apache Spark, a widely used big data analytics platform, and also supports its applications.
- Scala is compatible with several tools in Java’s ecosystem but also has a number of libraries that are suitable for analytics and for big data projects.
- Scala supports IntelliJ IDEA, Atom ENSIME, Emacs ENSIME, and its own IDE.
What is Python
Python is a popular open-source object-oriented programming language. It is easy to learn and use. It features an interpreter along with extensive libraries available in source or binary forms making it usable in multiple platforms. It also has a very resourceful community of data scientists and developers. Libraries such as Keras, Tensorflow, and Pytocrh are great resources for data science and deep learning.
Why Python is Used in Data Science
- It supports object-oriented programming, structured programming, and functional programming patterns
- availability of many data science libraries such as Pandas, Matplotlib, SciPy, and NumPy
- It is easy to use and has a simple syntax which facilitates faster code review
- Python comes with many capabilities including web development, data mining and visualization, and embedded applications
- Python is surrounded by a large resourceful community
- Python is highly scalable
Scala vs Python
Both Python and Scala are popular data science languages. Python is a high-level interpreted object-oriented programming language biased towards easy readability. Scala, on the other hand, is also an object-oriented programming language that is highly scalable.
Here is a comparison between Python and Scala languages popularly used in data science.
- Objects: Both Scala and Python are high-level, general-purpose, object-oriented programming languages. However, Python is a dynamically-typed language that supports several programming structures including object-oriented, structured, and functional programming. Since it is dynamically-typed, there is no need for object creation. Scala is statically-typed and supports both object and function-oriented language models. It is interoperable with Java. With Scala the type of variable and objects need specification.
- Platform: While Python interfaces with several OS system calls and libraries, Scala runs on Java Virtual Machine (JVM). Python is an interpreted language and Java a compiled language that requires source codes to be compiled before being run.
- Performance: In terms of performance, scala is faster than Python. Python, being a dynamically-typed language, creates extra work for the interpreter at runtime because it has to first decide data types. Scala runs on JVM. This makes it faster and a better option for large data processing tasks. Python is ideal for small to medium scale model-building and analytics projects and is most likely a choice language for start-ups and small businesses.
- Processes: Python supports heavyweight process forking and not multithreading. Scala is a good choice for multithreading since it has a list of asynchronous libraries and reactive cores.
- Learning: Python features a simple syntax that is easy to learn and use in code writing. Scala is a little complicated and is not considered the best for beginners.
- Community: Python enjoys huge support from its large community. Scala does boast of substantive support although it has a limited developer community compared to Python’s.
Scala together with Spark, even with limited ML capabilities, is preferred by developers with large data projects as it offers extensive code maintenance. Python, on the other hand, is the beginner’s favorite. It is easy to learn and use plus it boasts of several libraries and frameworks and does offer great community support.
Ready to start your journey handling large data projects, a certification course in Scala and Apache Spark is where to start.