Undoubtedly, Data Science and Engineering are two of the most desired skills among students and working professionals in the job market today. With the rapid advancement of technology, these two cutting-edge fields have become a necessity for businesses in nearly every industry. The demand for professionals who can analyze and interpret data has never been more significant.
There are a few reasons for this massive popularity of Data Science and Engineering. First, the world is becoming increasingly digitized, meaning that multiple businesses are relying on data to make decisions and run their operations. As a consequence, several businesses prefer people with the right skills and knowledge to help them make sense of all this data.
Second, Data Science and Engineering offer some of the highest salaries in today’s tech industry. According to Indeed, the average base pay for a Data Scientist is $102,000 per annum, and the average base pay for a Data Engineer is $115,000 per annum.
Lastly, the market trend in these fields is also escalating at a tremendous pace. According to the report by MarketsandMarkets, the global market for Data Science is projected to grow at a Compound Annual Growth Rate (CAGR) of 27.7% during the forecast period 2021-2026. Business Wire stated that the global market for Big Data Engineering services is expected to grow at a CAGR of 16.3% during the same forecast period.
Amid the massive growth of these cutting-edge technologies, businesses across all sectors are seeking methods to integrate Data Science and Engineering into their day-to-day operations. As businesses aspire to stay ahead of the market, those with the right skills and expertise will be in massive demand.
Without much further ado, let’s delve into the most commonly used languages and tools employers look for in the fields of Data Science and Engineering.
Top Languages and Tools for Data Science and Engineering
From students to working professionals and corporate leaders, all are enthusiastic about learning Data Science and Engineering tools and techniques. So, what are the essential abilities needed to develop a successful career as a Data Scientist or Data Engineer?
First, let’s have a look at the top languages used for Data Science and Engineering.
In-Demand Programming Languages for Data Science and Engineering
Python is unquestionably the most sought-after programming language among students and professional developers worldwide due to its simplicity, execution speed, and code dependability. It is widely utilized in Web Development, Scientific Computing, Artificial Intelligence, and more.
Python is a versatile programming language suitable for several data-intensive tasks such as Data Analysis, building Machine Learning models, and creating data-driven applications. Python has a vast and active community of developers who have created a vast ecosystem of libraries and tools that can be used in Data Science and Engineering. A few essential Python libraries for Data Science and Engineering include NumPy, SciPy, Pandas, Matplotlib, and TensorFlow.
R is a powerful programming language that developers can use for a broad range of tasks in Data Science and Engineering, including Data Wrangling, Cleaning and Manipulation, Statistical Analysis, and Data Visualization. R is free and open source, making it accessible to everyone. A few essential R programming libraries for Data Science and Engineering include Esquisse, Ddlyr, and Lubridate.
Java is another high-level programming language due to its features, high performance, and efficiency. In Data Science and Engineering, Java can be used to work with Big Data and analyze it to discover trends and patterns. We can also use Java to develop predictive models in Machine Learning to make predictions about future data.
SQL, the abbreviation of Structured Query Language, is used to manage databases and manipulate data (insert, search, update, and delete records). It is ideal for people who wish to work in the Business Intelligence field or with Relational Database Systems.
MATLAB, a high-performance programming language, is utilized for technical computing and mathematical operations. It is widely used in numerous fields, including Signal Processing, Image Analysis, Machine Learning, Deep Learning, Data Import and Export, Feature Extraction, Dimensionality Reduction, and Data Visualization. MATLAB’s comprehensive Mathematics, Statistics, and Machine Learning libraries make it an excellent choice for Data Science and Engineering.
Scala, a high-level, functional programming language, is compiled into Java and produced on JVM (Java Virtual Machine). Scala is ideal for working with Big Data in Data Science and Engineering if your tasks revolve around Apache Spark because Spark was written in Scala.
Julia is a high-level, high-performance dynamic programming language for numerical computing, with syntax that is familiar to users of other technical computing environments. Its syntax is expressive and easy to learn, and its powerful type system and multiple dispatches make it easy to write efficient code. Julia also has excellent support for parallel computing, making it an ideal choice for large-scale Data Analysis and Machine Learning.
Now, let’s have a look at the top tools used for Data Science and Engineering.
In-Demand Tools for Data Science and Engineering
1. SAS Software
SAS, the abbreviation of Statistical Analysis System, is an industry-leading business intelligence and analytics software suite that facilitates organizations to make better decisions faster. It offers a comprehensive set of tools for data warehousing, business intelligence, predictive analytics, and more.
With SAS, Data Scientists, Data Analysts, Data Engineers, and Business Analysts can gain insights from their data to improve their business performance. Some of the essential SAS products include SAS Visual Analytics and Business Intelligence, SAS Visual Statistics, SAS Data Integration Studio, and SAS Enterprise Miner.
2. Jupyter Notebook
Jupyter Notebook is an open-source, web-based computing environment for implementing code and data. Due to its flexible interface, professionals can configure and arrange workflows in Scientific Computing, Computational Journalism, Data Science, and Machine Learning.
Jupyter Notebook can work with 40+ programming languages, which include Python, R, Scala, and Julia, among several others. In addition, it facilitates Data Scientists and Data Engineers to leverage Big Data tools like Apache Spark.
3. Apache Hadoop
Apache Hadoop, an open-source software framework, is designed for distributed storage and processing of large data sets. In the era of Data Science and Engineering, Apache Hadoop is a tool used for various purposes, such as Data Mining, Data Warehousing, and Data Processing. Apache Hadoop is a popular tool in the world of Data Science and Engineering because it is highly scalable and can be employed on a variety of hardware platforms.
4. Apache Spark
Apache Spark is a unified analytics platform that can execute Data Science, Data Engineering, and Machine Learning applications on single-node machines or clusters. In Data Science and Engineering, Apache Spark is used for a wide variety of tasks, from data ETL and streaming to Machine Learning and complex analytics.
While it can be used for anything Apache Hadoop is used for, Spark’s primary advantage is its speed. Spark can process data much faster than Hadoop due to its in-memory computing capabilities, making it ideal for interactive queries and real-time analytics. Additionally, Spark’s user-friendly APIs make it easy to use for Data Scientists and Data Engineers who are not familiar with Java or Scala.
5. Apache Kafka
As Apache Kafka has become increasingly popular in recent years, numerous organizations are looking to adopt it for their data processing needs. Apache Kafka is a scalable, high-performance, and fault-tolerant publish-subscribe messaging system. It is often used in Data Engineering and Data Science pipelines to provide a messaging bus for streaming data.
TensorFlow is an end-to-end Machine Learning platform where you can build and deploy ML models and solve real-world problems. In Data Science and Engineering, TensorFlow is employed in a variety of tasks, including Machine Learning, Neural Networks, Deep Learning, Data Mining, Data Analysis, and Predictive Modeling.
Keras is a powerful open-source Neural Network library that provides a simple and convenient way to create Deep Learning models. It is written in Python programming and can run on top of either TensorFlow, Theano, or Microsoft Cognitive Toolkit (CNTK). In addition, Keras can be employed in several tasks like Data Pre-processing and Model Evaluation.
8. Microsoft Excel
Microsoft Excel (MS Excel) is particularly well suited for working with tabular data. Its built-in functions and formulas make it more straightforward to perform typical Data Analysis tasks, such as calculating mean, median, and standard deviation.
Excel also has a number of powerful features that Analysts can use to create informative and compelling Data Visualizations, along with Data Cleaning and Data Transformation to perform more complex statistical analysis and modeling.
Tableau, one of the most sought-after Data Visualization tools, is used for a broad range of Data Science and Engineering tasks. For example, Analysts can utilize Tableau to explore data sets, build predictive models, and create visualizations in order to help communicate results to clients or other stakeholders. In addition, they can use Tableau to create dashboards that allow people to interact with data in real time.
Tableau is a powerful tool that can assist Data Scientists and Engineers in making better decisions, communicating their findings more effectively, and ultimately improving their productivity.
10. Microsoft Power BI
Microsoft Power BI, a cloud-based Business Analytics and Intelligence service, provides a single view of your most critical business data. It lets you analyze data from multiple sources, including Excel, SQL Server, and cloud-based data sources, and create rich, interactive reports and dashboards that can be accessed from anywhere, on any device.
Data Scientists and Data Engineers use Power BI to gain insights into their data in order to make better decisions and improve their products and services. Business Analysts also use it to understand trends and patterns in their data, and IT professionals use it to track the performance of their systems.
The Next Step: How Can You Learn Data Science and Engineering?
If you’re planning for a career in Data Science and Engineering, this is the moment to take action. There has never been a better opportunity to hone your Data Science and Engineering skills, with companies investing extensively in these technologies and a talent shortage.
Our world constantly evolves, and new trends and technologies are continually appearing. We must continuously learn and adapt if we want to stay one step ahead of the game.
So, how can you learn Data Science and Engineering?
There are several ways to learn new technologies, but a few of the most preferred ones are traditional learning, online courses, attending workshops and seminars, and self-study.
1. Traditional Learning: Colleges and Universities
Traditional education typically involves classroom instruction, which has been practiced for millennia. Suppose you want to learn Data Science and Engineering in a regular classroom setting. The most outstanding universities in the world, such as Stanford University, Columbia University, and Imperial College London, are among the top choices. You will eventually be given a certificate of completion or a Master’s degree by the relevant institution or university.
2. Online Courses: Ed-tech Platforms
The popularity of online courses has accelerated dramatically in recent years. Online courses are now available on a variety of platforms, and they cover a broad range of subjects.
Some of the renowned platforms include Coursera, Great Learning, edX, and Udemy. These reputed ed-tech platforms offer world-class Data Science and Engineering courses that will assist you in paving your successful career path in these cutting-edge technologies.
3. Workshops and Seminars
Several enthusiasts attend workshops and seminars to hone their professional skills or discover new professions or industries. Gaining fresh perspectives, connecting with other industry professionals, and getting the opportunity to learn from subject-matter experts are advantages of attending workshops and seminars. Workshops and seminars can also be a fantastic method to strengthen your resume and advance your career.
4. Self-Study: YouTube & Books
The most straightforward path to get you started with self-study is to choose a topic and find the best resources to learn it. Some of the best resources include YouTube and Books.
Springboard, Arxiv Insights, freeCodeCamp.org, and Edureka are a few excellent YouTube channels to learn Data Science and Engineering.
Here are two books to get you started with these cutting-edge technologies:
- Practical Statistics for Data Scientists – O-Reilly
- Fundamentals of Data Engineering – O-Reilly
As organizations become more aware of how these technologies may improve their workflows, the need for Data Science and Engineering expertise is sky-rocketing. Effective Data Science and Engineering experts will be in massive demand and may be able to find employment across numerous industries. Those that grasp these skills will also be able to create new opportunities for their businesses and themselves.
Kanchanapally Swapnil Raju is a Technical Content Strategist at Great Learning who plans and constantly writes on cutting-edge technologies like Data Science, Artificial Intelligence, Software Engineering, and Cloud Computing. He has in-hand skills in MEAN Stack development and programming languages such as C, C++, and Java. He is a perpetual learner and has a hunger to explore new technologies, enhance writing skills, and guide others.