Top Data Science / Machine Learning Languages & Tools to Master in 2023

Data Engineers and Machine Learning Engineers are witnessing a steep rise in their demand and career prospects, thanks to the widespread adoption of Big Data, AI, and ML. Companies across all parallels of the industry are recruiting Data Engineers and ML Engineers who are proficient in multiple programming languages and can also work with a host of different Data Science tools and Machine Learning tools. 

As the demand for Data Engineers and ML Engineers continue to grow, their job profiles are also evolving, and so are the job requirements. Companies expect Data Engineers and ML Engineers to be expert programmers who are not only abreast of all the latest industry trends but can also create innovative products using various Data Science tools. 

If you are wondering what these tools and languages are that we’ve been raving about, we’ve made it easier for you – here’s a list of the top ten tools and programming languages that every Data Engineer and ML Engineer must know! 

Top 5 Programming Languages

1. Python

Python’s immense popularity in the software development and Data Science community is nothing surprising. There are multiple advantages of using Python for Data Science as this high-level open-source language is highly dynamic – it supports object-oriented, imperative, functional, as well as procedural development paradigms. 

The best part is that it has a neat and simple syntax which makes it the ideal language for beginners. Another great aspect of the language is that it features a wide range of libraries and tools for ML such as Scikit-Learn, TensorFlow, Keras, NumPy, and SciPy, to name a few.

2. C++

C++ is a general-purpose programming language that is extensively used by developers around the world to create sophisticated, high-performance applications. An extension of the C language, it combines the features of imperative, object-oriented, and generic programming languages. The two fundamental characteristics of C++ are speed and efficiency. 

C++ allows you to gain a high level of control over system resources and memory. What makes it a perfectly suitable language for Machine Learning is its well-designed ML repositories – TensorFlow, LightGBM, and Turi Create. Furthermore, C++ is flexible in the sense that it can be used to build applications that can adapt to multiple platforms.

Our learners also read: Free Online Python Course for Beginners

3. SQL 

SQL stands for Structured Query Language. It is the standard language for relational database management systems. SQL is used for storing, manipulating, retrieving, and managing data in relational databases.

SQL can be embedded within other languages by using SQL modules, libraries, and pre-compilers. Almost all relational database management systems (RDMS) such as MySQL, MS Access, Oracle, Sybase, Informix, Access, Ingres, Postgres use SQL as their standard database language.

4. JavaScript

JavaScript is one of the most popular web scripting languages. It is a prototype-based, multi-paradigm, single-threaded, dynamic language, that supports object-oriented, imperative, and declarative programming styles.

Although JavaScript is widely used as a scripting language for webpages, even non-browser environments including Node.js, Apache CouchDB, and Adobe Acrobat use the language. JavaScript comes equipped with many libraries useful for training and deploying ML models including TensorFlow.js, Brain.js, machinelearn.js, math.js, face-api.js, and R-js.

5. Java 

Another general-purpose programming language on our list, Java is a class-based, object-oriented language used to develop software, mobile applications, web applications, games, web servers/application servers, and much more. It functions on the WORA (write once, run anywhere) concept – once you compile a code in Java, you can run the code on all platforms that support Java (no need for recompilation).

Today, Java is used by developers and engineers to develop Big Data ecosystems. Also, Java has a host of ML libraries like Weka, ADAMS, JavaML, Mahout, Deeplearning4j., ELKI, RapidMiner, and JSTAT.

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Top 5 Tools

1. AWS

Amazon Web Services (AWS) is a secure cloud services platform developed by Amazon. It offers on-demand cloud services to individuals, enterprises, corporations, and even the government, on a pay-as-you-go model. AWS provides cloud computing platforms, database storage, content delivery, and various other functionalities to help businesses scale and expand.

Using AWS, you can run web and application servers in the cloud for hosting dynamic websites; store files on the cloud and access them from anywhere, anytime; deliver static/dynamic files to anyone across the world via a Content Delivery Network (CDN), and send e-mails to your customers in bulk.

2. TensorFlow

TensorFlow is an excellent Machine Learning tool for Deep Learning systems. It is an open-source, JavaScript-based Machine Learning software library used for training and deploying models on Node.js as well as in browsers. It is also an excellent tool for numerical computation using data flow graphs. 

While the core library allows for the seamless development and training of ML models in browsers, TensorFlow Lite, a lightweight library for deploying models on mobile and embedded devices. There’s also TensorFlow Extended – an end-to-end platform that helps to prepare data, train, validate, and deploy ML models in large production environments.

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3. PySpark

PySpark is nothing but Python for Spark. It is an amalgamation of Apache Spark and Python programming language. The primary purpose of PySpark is to help coders write and develop Spark applications in Python.

While Apache Spark is an open-source, cluster-computing framework, Python is a general-purpose, high-level programming language with an array of useful libraries. Both have simplicity as their core feature and can be used for Machine Learning and real-time streaming analytics. Hence, the collaboration is justified. PySpark is a Python API for Spark that allows you to leverage the simplicity of Python and speed and power of Apache Spark for various Big Data applications.

4. Hive

Hive is a data warehouse software that is used for processing structured data in the Hadoop platform. It is built on top of Hadoop and facilitates reading, writing, and managing large datasets stored in distributed storage using SQL. 

Essentially, Hive is a platform used to develop SQL typescripts for MapReduce operations. It has three core functions – data summarization, query, and analysis. Hive supports queries written in HiveQL or HQL, a declarative SQL-like language.

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5. Scikit-Learn

Scikit-Learn is an open-source, ML library for Python. Its design is inspired by the other top Python-based libraries – NumPy, SciPy, and Matplotlib. It comes with various algorithms, including support vector machine (SVM), random forests, k-neighbours, etc. It also contains a host of other tools for Machine Learning and statistical modeling such as classification, regression, clustering and dimensionality reduction, model selection, and pre-processing

Of all the open-source libraries, Scikit-Learn has the best documentation. It is not only used for building ML models but is also widely used in Kaggle competitions.

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So, that’s our list of the ten most useful and popular Data Science Tools and programming languages for Data/ML Engineers. Each tool is unique in a distinct way and has its unique applications. The trick to leveraging these tools to the fullest is to know which tool/language to use for which situation. If you’re a beginner, you can utilize these tools to do your machine learning projects.

Experiment with programming languages and ML tools. Learn through trial and error. The only important thing here is your willingness to learn – if you are curious to learn, upskilling no longer remains an arduous task! If you want to get your hands dirty on machine learning tools, get help from industry mentors, check out IIIT-Bangalore & upGrad’s Master of Science in Machine Learning & AI 

Why is Python considered to be the best fit for Data Science?

Although all of these languages are apt for data science, Python is considered to be the best data science language. The following are some of the reasons why Python is best among the best: Python is much more scalable than other languages like Scala and R. Its scalability lies in the flexibility that it provides to the programmers. It has a vast variety of data science libraries such as NumPy, Pandas, and Scikit-learn which gives it an upper hand over other languages. The large community of Python programmers constantly contributes to the language and helps the newbies to grow with Python. The inbuilt functions make it easier to learn as compared to other languages. In addition, data visualization modules like Matplotlib provide you with a better understanding of things.

What are the steps required to build an ML model?

The following steps must be followed in order to develop an ML model: The first step is to gather the dataset for your model. 80% of this data will be used in the training and the rest of the 20% will be used in testing and model validation. Then, you need to select a suitable algorithm for your model. The algorithm selection totally depends on the problem type and the data set. Next comes the training of the model. It includes running the model against various inputs and re-adjusting it according to the results. This process is repeated until the most accurate results are achieved. After training the model, it is tested against new data sets and is improved accordingly to produce accurate results.

What is the role of a data scientist?

Data is something that everyone needs. Everyone is either generating the data or consuming the data every second. From watching a video on YouTube and surfing on Google to posting a picture on Instagram and extracting high-security data by secret intelligence, data is being involved. With so much data around us, we need someone who can handle it and extract something meaningful from it and that is what a data scientist does. Data Science is the art of processing large chunks of big data and extracting processed information from it.

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