There has been a significant amount of AI engineering ecosystem that has popped up in the last few years which is helping to expedite the progress in this area. Tools, frameworks, and open source libraries are making boilerplate implementation ready for use by the engineering communities. The trends are so prominent that we are seeing large-scale organizations like Google, Microsoft, and Facebook open sourcing their AI tools and framework to help the engineering community build AI-based solutions. More often than not we see that most of these tools and frameworks are in Python. The use of Python for AI has become dominant in all aspects of AI engineering work like – ML, Data Engineering, Data Science, Model Development, and Deployment as well. 

This begs the question, what is AI, and why the AI Engineering community is looking at Python as a language of choice for AI-based solutions? In the subsequent sections, let us start with building a comprehensive understanding of the Artificial Intelligence domain first, some Python-based tools that are being used, and then understand the key reasons and advantages of using Python for AI.

The AI Landscape and Benefits of using Python for AI

A Brief Journey of Artificial Intelligence (AI)

Artificial Intelligence in general, means the process of making machines mimic human behavior. Founded roughly around 1956, the idea behind Artificial Intelligence was to make machines do things that are considered to be unique human capabilities, like intelligence or intuition. In the early days, the research was mostly around board games or logic experiments. 

In the early days, in some of the cases of rule-based or expert systems, Artificial Intelligence was considered to be a glorified if-else program. This means a lot of domain knowledge of the problem was coded into the system with the help of experts from that area. Checking all possible options and then optimizing the final output. On the basis of the most plausible answer was how AI was used in the initial days.

Machine Learning: The Beginning of an Era

As the research around AI progresses, we saw the dawn of a new class of the Artificial Intelligence subdomain which we affectionately called Machine Learning. The idea was to use statistical models to learn from the data of past observations to build a model which should help explain and predict future observations. The idea of using classifiers, clustering algorithms, and other statistical techniques to make sense of the data started to take shape in academics and industry. 

Python for AI – Tools for Machine Learning 

Machine Learning which uses statistical modelling and needs to train the models with a substantial amount of data generally works with Python and R Frameworks. R is an open-source language and framework for statistical workloads. However, it is majorly preferred by the academic community, and also the library support is still catching up. Python by far is the most dominant language in this space.

The open-source community in the machine learning and statistical modelling scape is very active. Tools like NumPy, Scikit-learn, Pandas, etc. are dominantly used by engineers and scientists alike. The growing community of engineers also adds to the support that a new engineer will get while venturing into this area. 

Deep Learning: Getting Closer to Humanization of ML

Deep Learning is a subset of ML. It loosely represents the way the neurons work in our brain. The neurons are structured in layers of repetitive structures. Thus, when presented with enough data they try to learn from the data with the use of mathematical optimization techniques like gradient descent and backpropagation. 

The current state of massive availability of data and frameworks makes the training of neural networks fairly easy. It has become the tool of choice in many application areas like image processing, text processing, and trading etc.

Python for AI – Tools for Deep Learning

The Deep Learning space in the last decade has seen a massive explosion of tools pouring in from major technology giants like Google, Facebook, and Microsoft. All frameworks almost invariably support Python as the de facto language of choice for training and many for inference as well. Some of the popular frameworks also support other languages too.

Frameworks like Tensorflow, Pytorch, and MXNet are very popular frameworks used for Deep Learning engineering and experimentations. Purposely, it is maintained beginner-friendly. It also works pretty well with other frameworks for Data Processing, Engineering, and Visualization. Deep Learning engineering in general is a very repetitive and experimentation-heavy engineering process. Therefore, it needs to have a language that is flexible and is very expressive in nature. 

Data Engineering: Working with the New Oil

In the age of AI, data is the new oil and data engineering is its refining process. Artificial Intelligence has a huge dependency on data and not just any data but pretty good quality data. However, it is believed that a quality model can be build with Deep Learning. It will always depend on the type of data that we feed into the training of one. So this gives rise to another set of parallel engineering domains called Data Engineering and Data Sciences.

Data Engineering comprises data collection, data processing, data cleaning, governance, analysis, reporting, and also visualization. It deals with building tools and frameworks in place to make the whole workflow seamless and usable for various modeling and reporting tasks which can lead to better decision making. 

Python for AI- Tools for Data Engineering

The Data Engineering process requires frameworks and infrastructure to ingest, process, and store large quantities of data. This includes not just an infrastructure that scales vertically but also horizontally across large server farms. The workflow included data cleaning, feature engineering, and storing a large amount of data. 

The tools include Apache Spark, Kafka, Delta Lake, and many more. This area typically leverages a lot on the existing big data architecture. It also needs to have a very flexible infrastructure in place to play around with data in short iterative cycles. The proliferation of managed data and analytics frameworks is also very commonplace like data bricks.

Data Visualization: Connecting Numbers to Narratives

Journalist and Designer, David McCandless, in his TED talk said, “By visualizing information, we turn it into a landscape that you can explore with your eyes, a sort of information map. And when you’re lost in information, an information map is kind of useful.”

A picture is worth a thousand words and it is not just the numbers but the narrative behind those numbers. That need to be in place for the decision-makers to zero in on the right options. Data Visualization is quite a complex engineering piece that stands at the confluence of art and engineering.

Visualization adds narrative to the numbers and it is very meaningful when conveying the right inputs to the decision-makers in the company. It is easy to process in a condensed form. The verbose nature of the data, in general, is not very expressive to create reports and help decision-makers with the right information they are looking for. Visualization helps to bridge that gap.

Python for AI- Tools for Data Visualization

The Data Visualization tools that are available in python are Matplotlib, Seaborn, Plotly, ggplot, and Altair, etc. The visualization tools need to be simple to use APIs, cross-platform support like browser, etc. It might be helpful if it is interactive in nature. 

Why is Python the Most Preferred Language for AI?

Guido Van Rossum created Python in 1980. Since then because of its simplicity, expressiveness, and flexibility. It has been the language of choice for many general purpose applications for amateur and seasoned programmers alike. A few of the features of Python which plays out to its advantage are:

1. Simple to Learn and Use

It is simple to start and use, the developer can build expertise in this language almost effortlessly. There is a huge buffet of online tutorials that make learning Python extremely easy for beginners. Simple syntax, expressive style, and natural language semantics make it an ideal choice for developers working on AI. Further, doing quick experiments and iterations with the language is very easy because of its interpreted execution format. It can tuned to run extensively fast with the compiled version also available.

2. Mature and Supportive Community 

Python has been around for almost 30 years now and over the years it’s developer community has grown many folds. From documentation to tutorials to books there is an extensive choice of options for taking the skill levels from the beginners level to the expert in a less span of time. Getting help at the time of need builds the confidence in the programmers to dive in. It also means a lot of time saved from reinventing the wheel. 

3. Support from Large Companies 

Python among the current generation of languages possible has the biggest large-scale corporation support among its peers. With Facebook, Amazon, Google, Uber, or in-short almost the whole world is delivering their open-source frameworks and packages in Python. It is invariably becoming the default standard for developers across the globe. 

4. Versatile Open-Source Library Support

Pretty much any domain that we can think of will be very likely to have python libraries and frameworks available for the developers. It saves time, promotes reuse, and also helps to build the community of developers. 

5. Efficient, Reliable, Flexible, and Versatile

Python applications is available everywhere, whether it desktops, servers, or mobile applications. It is by far the most versatile language among the current generation of languages out there. However, the versatility of the language attracts many applications and more developers get added. Big Data, Machine Learning, and Deep Learning are some of the latest areas where Python is finding its application too.

Python has a prominent place in the Data Analytics space. The research community is in love with python and that is evident from its applications. Thousands of Machine Learning libraries are doing round and many more are getting added on a daily basis. 

6. Rapid Automation Prototyping

Python is the poster child for the automation domain. With many tools, libraries, and frameworks in place getting into automation and also mastering the art is relatively easy. In the space of Artificial Intelligence and Data Processing, a lot of automation is required largely. Due to the fact that there is a lot of data to crunch and it is just not possible to handle all this sheer volume without automation. 

Wrapping Up 

Python, with its simplicity, robustness, and expressive nature along with interpreted execution with huge open-source, corporate and community support is just the right mix of everything. Therefore, Artificial Intelligence Engineering is a highly iterative and experimentation-heavy domain. Thus Python is the perfect language to support its applications. No wonder the raging popularity of using Python for AI has only seen an upward trend and will continue to rise in the coming years as well.

We have a huge pool of expert Python Engineers with expertise in all verticals of the Artificial Intelligence domain to help your next big idea. Connect with us.

The post Python for AI: Tools and Key Advantages appeared first on EngineerBabu Blog.

The programming language can be used for serving a lot of purposes in web development. However, the most popular Python Programming Language can be widely used for and is popularly used in AI (Artificial Intelligence). It is also used in scientific computing, statistics, education, and machine learning, making it most popular among and in-demand for Python Web Developers.

It is an open-source programming language, introduced in 1992, and now becoming the most popular and widely used platform. With the increasing demand for AI and ML applications, Python Web Development is considered first for coding such applications.

Python is a simple language to learn for beginners as well, which also increases the craze of language among developers to become Python Web Developer. However, to decide whether you should go for Python Web Development, you must analyze the pros and cons of Python Web Development.

Evidently, everything has some pros and cons with it, but going with Python Development and choosing it as your application development. Now, it becomes necessary for you to analyze the pros and cons of Python Web Development and how it will affect your project.

Python Programming Language is available with a large number of frameworks. It includes Django, Pyramid, Flask, Bottle.py, Web2py, CherryPy, and many more. The developers can effectively use these frameworks to develop a robust and feature-packed website.

The variety of Python frameworks are used by the web developers to build many renowned websites, some of them are;

• Spotify
• Reddit
• Mozilla
• Yelp
• Washington Post
• Netflix
• Instagram, and many more.

Source: Britwise Website

Along with these renowned names, companies like Uber, Dropbox, Pinterest also used Python Programming Language in their code base to write structural programs. It is one of the fastest-growing and third most profitable programming languages in the world. Let’s take a look at the pros and cons of Python Web Development.

Pros and Cons of Python Web Development

Let’s discuss the pros and cons of Python Web Development, but before that, I hope you all are aware of features of Python Programming Language. If not, then first make yourself familiar with the important features of Python. Now, it will become easier for you to understand the pros and cons of Python Web Development.

Pros of Python Web Development

1. Extensive Library Support

Python is available with the extensive support of libraries and contains a variety of codes. These various libraries fulfill the purpose of minimum coding and can be used for delivering regular expressions, unit testing, web browsers, threading, document generation, databases, CGI, image manipulation, email, etc.

Python Web Development becomes more easier as it requires minimal coding for heavy tasks execution. The Python Web Developers can build their prototypes and test their ideas using Python, and it ultimately results in saving a lot of time, human resource, and money as well.

It is such an easy to learn programming language, that one can learn Python quickly. If you have a basic knowledge of programming, you can easily grab the Python Programming Language by watching a few tutorials and can even build a project too.

2. Extensible Language

Python is an extensible language, which means it can be extended to other languages. Python allows its developers to write some of the codes in different languages like C or C++. This feature of Python, makes it the most suitable and handy development platform, especially for project building.

Due to this, Python Programming Language has become the most preferred language from enterprise software or apps development. It is a very appropriate language when it comes to assembling the old and new infrastructure fragments. It is very difficult to achieve assembling in complicated mobile apps, but with Python Programming Language and the efficient Python Web Developers make it possible.

3. Embeddable Platform

In contrast to extensible, Python Web Development is embeddable too. It allows you to place your Python code in the different languages source code, like C or C++. This allows the developers to add the scripting capability to the source code in other languages.

Python is one of the most convenient platforms to write and maintain codes without any problem. It is because the Python Programming Language doesn’t create any confusion, or require research and deadlocks. In Python, each segment of code functions smoothly and separately, which allows developers to handle situations in a guided manner and make the product development process quicker and more comfortable.

4. Multiple Frameworks

Python supports multiple frameworks and is one of the significant reasons that contributes to its popularity as the most preferred website development platform. As the frameworks ease the work of the Python Web Developers, which makes it the most liked platform among the developers.

From the variety of frameworks, developers can choose the most suitable framework for their project development to perform the project development smoothly and quickly. However, the selection of the frameworks depends on the requirement of the project.

5. Enhanced Productivity

The extensive support of frameworks, libraries, and easy to use language improves the efficiency of Python Web Developers. Such support allows programmers to deliver faster productivity than languages like Java and C++. With support, the developers had to write fewer codes as compared to other programming languages and achieve more results.

It is a wonderful platform that allows developers to build applications that can connect the language with the real world, such as AI (Artificial Intelligence).

6. High-Level Programming Language

Python is the most versatile and high-level programming language. It supports both Object-Oriented and Procedural programming paradigms. With this high-level language, the construction and execution of IoT applications can be easily possible. Python Web Development also allows easy development of games, radios, cameras, phones, and many more effective applications by using the Raspberry Pi framework.

Being a high-level object-oriented programming language, the Python Web Development supports code reusability, classes, and objects. A class allows the data encapsulation and functions as one.

7. Effectively Build Scientific and Numeric Applications

The Python Web Development platform is available with a variety of framework packages and libraries to develop the scientific and numeric applications. It also supports building tool kits like MayaVi and VTK 3d, a separate imaging library, and many other tools.

The most commonly used libraries and tool kits for building scientific and numeric applications are;

• SciPy (Scientific Numeric Library),
• IPython (Shell Command),
• Pandas (Data Analytics Library),
• Natural Language Toolkit (Library for Mathematical and Text Analysis),
• Numeric Python (Fundamental Numeric Package), etc.

8. Immensely used in Machine Learning and Artificial Intelligence

As the demand for Machine Learning and Artificial Intelligence is increasing rapidly in the market. As a result, more developers are aiming towards Python Web Development and incorporating them into various projects.

Python is the most appropriate programming language available. It has efficient Machine Learning packages, tools for result visualization, brilliant data analysis, and various other features that benefit the area of application development.

9. Application Scripting and Software Testing

Python is a very handy programming language due to its strong integration with C, C++, and Java. It is a very useful platform for customizing large applications and making extensions for them.

It is very efficiently used in Automation Testing. Python is the first choice of many QA Automation specialists to generate the learning curve. The language works well for those who are equipped with technical skills, as Python has a strong developer community, clear syntax, and excellent readability. Python is also equipped with easy to use frameworks for performing Unit Testing.

10. Portable & Easy to Debug

It is a very portable language, i.e., you can write code on a platform and even change the platform to run the code. However, when you write code in a language like C++, then you have to make some changes to make the code run on another platform. Therefore, Python Web Development is considered natural and very portable for its developers.

Python Web Development allows its developers to code only once and runs it anywhere- Write Once Run Anywhere (WORA). But make sure you are not including any system-dependent feature in your codes.

Python is an interpreted language. Therefore, its codes are executed one by one. This makes the code debugging easier for the developers. Hence, Python first compiles each statement one by one and then debug it.

11. Easy to Utilize

Python is an easy to understand and readable programming language- very much similar to the English Language. Thus, it reduces the coding complexity and provides the clear and easily legible syntax for Python Web Development. The easy syntax allows easy understanding of the relationship between different objects and eases the complete process of the Python Web Development life cycle.

Python also provides easy visualization presentation of data through charts and graphs to analyze the working and efficiency of Web Development processes. This helps developers to easily plot available data on the graphs and deliver the best results with it. It also allows companies and organizations to prepare clear reports and learn about the mistakes done from those reports easily.

12. Excellent for Prototype Building

Python is a brilliant and most preferred programming language to easily and quickly build a prototype. As the corporate world is highly competitive. Assuming that you have a unique idea thus having ample amount of time to build the product. But, it is a myth, because it might be possible someone else also had quite a similar idea in mind. Not only the idea, he might also have introduced the product before you, while you were planning to do so.

Therefore, to avoid such situations, being fast and quick becomes essential to sustain the competitive market. In this respect, Python can be the handiest tool to achieve your targets instantly. Python is a fast web development programming language. Therefore, Python can be the most appropriate programming language to build the efficient and quality prototype at a rapid pace. As well as, allows easy upgradation of the product and fastens the whole development process.

Source: RawPixel Website

Pros of Python Web Development over Other Language

While learning about the pros and cons of Python Web Development, it becomes crucial to learn about the pros of Python Web Development over other languages. Let’s take a look over such points and adopt it in the future for your new web development project.

1. Affordable Programming Languages

Python is a very affordable and suitable programming language for startups and organizations; who are looking for a cheap and pocket-friendly web development solution. It has become the first choice of developers to achieve the desired results at a much faster rate.

Python Web Development is the most adapted and ideal option to go with if you are looking to start the business at minimal capital investment. It is freely available as an open-source programming language. It allows you to download its source code, make changes into it and share it with others as well. Python Web Development becomes more comfortable because of the extensive availability of library collection for its web developers.

2. Requires Less Coding

Python requires very few coding in almost every task compared to doing the same task in other programming languages. To make it possible, Python supports a pool of standard libraries to ease the whole development process. It makes sure to guide the developers and save their time searching for any third-party library to carry out their tasks.

 Being very handy and easy to learn, Python is considered a good programming language for beginners.

3. Available for Everyone

Python codes are easy to run on any machine, whether it is Mac, Linux, or Windows. Python supports multiple tasks; however, the developers had to learn different languages to perform different tasks.

Python Web Developers can develop projects professionally, perform machine learning and data analysis effectively, automate the process, do web scraping, and can also build brilliant games with compelling visualizations. Overall, Python Programming Language is an all-rounder in doing numerous web development tasks.

Source: RawPixel Website

Cons of Python Web Development

Well, so far, we have learned about the pros of Python Web Development and how it can be the best choice for your project. However, if you decide to opt for Python, then you should be aware of its consequences as well. So, let us learn about the Cons of Python Web Development over other languages.

1. Doesn’t Work Well on Mobile

It is not a very effective and efficient programming language when it comes to developing mobile apps.

Being a weak programming language for mobile app development, the web developers are left with no other option, instead of choosing other programming languages. However, many web developers like to stick to the traditional mobile app development tool for this.

2. Slow

Python Programming Language uses Interpreter, instead of Compiler. Due to which, it takes a lot of time and slows the development process. In contrast to this, many other programming languages use Compiler for project development and fasten the whole process. This is one of the drawbacks of python which demotivates many web developers.

3. Usage of Memory

Python uses a lot of memory space in developing heavy applications. It fails to work under restricted memory allocation. The flexibility of data type declaration and usage in Python Programming also utilizes a lot of memory space.

In this reference, when developers and organizations look for programming languages in terms of less memory consumption and more task execution, Python turns to face a massive fallout for Python Web Development.

4. Doesn’t Support Games & Mobile App Development

Python is not chosen for mobile and gaming app development because it uses a lot of memory space. Apart from that, it supports slow application development.

5. Simplicity Creates Problem for Developers

As we know, Python is very simple and has an easy syntax for development. It makes developers very fond of it and causes problems to work with other languages. Suppose a programmer is working on Python for a long time, and suddenly he has to switch his technology or programming language. Then it will become difficult for developers to catch the new and completely different technology instantly.

6. Error Detection Issue

Python Programming Language uses Interpreter, instead of Compiler, due to which it causes problems in bug finding or error detection. However, the Compiler executes the task much faster, and help is bug detection and debugging too. It also creates problems in testing the task. Thus, it causes a lot of delays and requires much more time in error detection and debugging the issues.

7. Underdeveloped Database Access Layers

The Python Programming Language database access layer is quite underdeveloped as compared to other widely used technologies like Java Database Connectivity (JDBC) and Open Database Connectivity (ODBC). As a result, huge enterprises don’t prefer to use Python for Web Development.

8. Design Restrictions

Python is a dynamic-typed programming language. It means Python executes specific tasks during app run-time. However, in a statically-typed language, such tasks can be completed. This generates restrictions on the design. If the design is loaded with elements, there is a possibility that the program will be stalled.

Concurrency and Parallelism are not considered in python projects. Due to which, the design might not look as sophisticated as you expect it to be.

9. Lack of Experts

Python Programming Language experiences lack of expert Python Web Developers. It is an easy programming language but lacks innovation and improved efforts from its developer’s community. In comparison to the Java programming language, Python also has only a handful of Python programmers in the industry having specialized skills and knowledge of programming languages.

There are very few talented and skilled Python Web Developers available in the market however, there is a huge demand of Python Developers in the market .

Conclusion

Majorly for startups and small enterprises, Python Web Development has turned out to be a great contributor to the new and modern application development. Apart from that, companies worldwide prefer Python Programming Language for Machine Learning and Artificial Intelligence web development due to its exceptional capabilities in both fields.

Therefore, Python can be a good option for many types of projects. The pros and cons of Python Web Development might have delivered an accurate idea of why and why not to use the Python Programming Language for your upcoming projects.

Source: RawPixel Website

If you have an idea and are looking for a Python Web Development company or a dedicated team of Python Web Developers, you can instantly connect EngineerBabu. We have ample resources and qualified and experienced Python developers in developing apps and server apps. For further details and assistance, you can contact us and discuss your project idea with us and take it on the floor.

The post Pros and Cons of Python Web Development appeared first on EngineerBabu Blog.

On this day, it’s impossible to imagine a life without data. Data science, the study of information from the enormous amount of data present is one of the most sought-after careers of the time. We’re living in a digital era where every organization is digitizing their data. A major part of Data Scientists’, Data Engineers’ and Data Analysts’ diurnal work includes dealing with zettabytes and yottabytes of structured and unstructured data.

Previously, data scientists were expected to perform the basic tasks of data engineers that included cleaning, creating data pipelines and optimizing data from various sources. However, separating the jobs with the skills and experience have helped businesses in a major way. There are a lot of overlapping skills of both the data scientists and data engineers possess but to achieve maximum efficiency, a business must hire different people for performing the jobs. If you have undertaken a detailed data science course, you will understand the difference.

From analytical, mathematical to programming knowledge, both job profiles may appear similar to employers and they often expect a data scientist to perform what the data engineer can effectively do and vice-versa. This may result in a reduced amount of efficiency and effectiveness of data science projects hence affecting the business in a major way.

In this blog, we list down the major differences between a Data Scientist & a Data Engineer. But first, let’s make you understand the basic need hierarchy of a data process.

The process starts with a company creating a product/service. For the product to be successful, the company needs to perform a market analysis, understanding the needs and demands of customers, the competitors’ analysis and much more to meet market expectations.

The data is collected from various sources by a data infrastructure engineer and later a reliable data flow along with a usable data pipeline is created by a data engineer. The pipelines are then passed forward to the data scientists who use various data science algorithms, analytical techniques, few testing methods like A/B testing to derive findings that can be used for better market performance.

Data Engineer and Data Scientist are the most in-demand jobs where currently the demand exceeds the supply. Although both professionals essentially have the same goal that is to help businesses optimize how they use data, they differ in how they use the specific skills they possess. To give you a brief understanding, data engineer’s job leans more towards programming to build scalable data products while a data scientist’s job is to focus more on the statistical analysis to gain insights and bring value to a business.

Let’s have a look at the specific differences of both the job profiles:-

What does a Data Engineer do?

Data Engineers deal with the basic infrastructure of data for analysis – including designing, building and optimizing data from a large number of internal and external sources. Usually, the sources include raw sets of data that contain human, machine or instrument errors. The Data Engineers create API’s and frameworks for consuming the data from given sources.

Sometimes, the data will be unformatted or system-specific for which the data engineer will need to recommend ways to improve the quality, efficiency, and reliability of data. The engineers are responsible for the performance of the entire data pipeline for which they build scalable and high-performance infrastructure. Data engineering is creating a data pipeline that is basically a production-ready set of data that encompasses the journey and processes of data in any organization.

When data engineers create data pipelines, they need to keep in mind that they are free-flowing, contain real-time analytics that is devised by a combination of a variety of big data technologies. The goal is to create the kind of architecture that enables data generation and supports the requirements of data scientists to answer business needs.

What does a Data Scientist do?

A data scientist usually deals with data that has been previously manipulated and processed. The data is then used by the data scientists for predictive and prescriptive modeling to answer business needs. They work more on the data analysis part of the business. Conducting research, examining data to find & explore hidden patterns and later present the analytical data to various stakeholders is a part of their daily work.

Data analytics and optimization are carried out through machine learning and deep learning.  But it doesn’t make the work any easier; a large volume of data from internal as well as external sources is to be analyzed to be presented in the form of a story that contains accurate and well-researched data. First, they interact with business leaders, understand their requirements and convey complex findings with the data to them.

Recommended Readings: Build SaaS Product with a team of Remote Workers

Data scientists need to interact with the business side with their data, they use their programming skills to accomplish what they couldn’t otherwise. They create reports, fulfill queries, identify trends and then generate insights that have the ability to verbally and visually communicate the observations and results to the business so that they can understand it and act on them in the future. The scientists do not build or maintain data infrastructure anymore after the specific bifurcation of the job profiles of data engineers and data scientists.

Expertise of Data Engineers

A data engineer is a qualified engineer in the computer science field and is skilled in Mathematics, Programming & Big Data. Comprehensive knowledge of how big data operation works, the strengths and weaknesses of all the tools used is mandatory. Here are the basic requirements of a data engineer’s job profile:-

• Practical knowledge of Linux
• Experience with Python or Scala/Java
• SQL
• Deep understanding of frameworks (Spark, Flink, etc.)
• Working Knowledge of MongoDB, PostgreSQL, and Redis
• Experience with cloud-based data solutions including AWS, EC2, EMR, etc.
• Internal and external root cause analysis
• Development, Management, and optimization of big data architectures and pipelines

Other than that, the programs majorly used by a Data Engineer include Hadoop, NoSQL, and Python. The engineers need to take unrefined data sources and convert them into clean and reliable data sets so that data scientists can run queries against the same.

Expertise of Data Scientist

In general, the data scientist has a Mathematics, Statistics or Physics background. To get into the detailed expertise required for a data scientist to be able to perform the required job,

• he/ she must possess statistical and analytical skills
• should be well-versed with Machine Learning and Deep Learning principles (artificial neural networks, clustering, etc.)
• data optimization and decision making skills
• High-proficiency in SQL
• Experience with Java and Python for Data Science
• Knowledge of predictive modeling algorithms and frameworks
• Expertise in Hadoop
• Experience in analyzing data from various platforms including AdWords, Google Analytics, Facebook Insights, etc.
• NoSQL and relational Databases’ knowledge
• Communication skills to convey technical findings to non-technical business members

The data scientist uses these skills in order to make business decisions based on the data, the findings need to be accurate.

In the case of data engineers, they may or may not be Machine Learning or Deep Learning experts.

Payscale of Data Engineers

According to Glassdoor, on an average, data engineers’ salaries range from $43K to a maximum average of $364K depending upon the level of experience and expertise.

Payscale of Data Scientists

The average pay scale of data scientists varies from $34K to $341K. It depends upon the kind of business, data science projects, experience as well as expertise in the field of data science.

Overlapping Skills

Clearly, both data scientists and data engineers need to work together as a team in order to produce good results but they shouldn’t be expected to perform all the tasks related to data science (from creating pipelines, performing analysis to communicating to business owners).

However, they possess a few overlapping skills but the level of expertise in skills is completely different.

• Analytics
  Both the data scientists and data engineers possess analytical skills. They know how to analyse data in order to give results and suggestions to a business but when you compare the level of expertise, the data scientist has a deeper and more advanced knowledge of analytics. If a data engineer is asked to perform analysis, he/she will only be able to perform it at an amateur or intermediate level. As mentioned previously, the data scientist knows how to take data from internal and external sources and is well-versed with various tools including Google AdWords, Google Analytics, etc.
• Programming
  Yes, it’s true that data engineers and data scientists are skilled in programming but data engineers know way more than data scientists. Creating data pipelines may sound like an easy task but it is only a skilled data engineer that can create it in an effective and understandable way. Once the data pipelines are created, the data scientist’s role comes into play.
• Big Data
  Having read the above content, you might have understood how different the two job profiles are in terms of skills and their expertise level. Another overlapping skill of a data scientist and data engineer is that of Big Data. Employers may often think that a data scientist will be able to create Big Data pipelines but they’re mistaken! It is the data engineer’s job of creation of the pipelines that are then used by the data scientists. The data scientists use their advanced math skills to perform data science analysis.

How to hire the right person?

Data is incredibly complex in nature and to hire the right person for the current requirement in your organization is of utmost importance. If your business is in its early stages, hiring a data engineer will be more beneficial as he/she will construct systems that can be analyzed by data scientists. On the other hand, if you are farther along in the business, you will need a data scientist who will use the data systems to further provide insights for improvements in the performance of your business.

The output that you get from a data scientist would be an insightful data product while the output from a data engineer would be a data flow, storage and retrieval system.

Job Outlook

Working with Big Data provides a huge number of opportunities to learn, grow and earn as a data science professional. Without data engineers, the data would be unusable and very difficult to analyze for further advancements. Currently, the number of jobs for data engineers have increased remarkably as compared to a few years ago. As per Glassdoor, the number of job openings of data engineers is approximately five times more than those of data scientists.

A data science team involves the work and efforts of both – data engineers and data scientists. As the demand for data management has significantly raised, big companies like PlayStation, The New York Times, Bloomberg, Amazon and many more are seeking for data science professionals and enthusiasts who will manage data efficiently to provide good results.

Organizations fail to understand the difference between the two job roles, however, they should be hiring employees with unique skills by distinguishing them. A data scientist will relatively be an amateur in data pipeline creation and may make the wrong choices. He/she can acquire the skills of a Data Engineer but a company could easily hire a data engineer and get a better ROI (return on investment) in terms of time and money.

In conclusion, we hope that the differences drawn in the blog gave you a clear understanding of the exact difference between a Data Scientist and a Data Engineer. A collective comprehension of the subject will make it easier for you or your business to manage data in a better and in a more effective way.

We at EngineerBabu have worked for over 5000 business owners and founders that share a common goal of incredible business performance while also sharing a common struggle: the inability to find adequate engineering talent to scale their businesses.

We work with the mission to push the world forward by bringing global opportunities to talent and bringing great talent to tech companies with remote teams of skilled engineers all around the world.

Recommended Readings: Top 10 Tech Companies who allow to Work Remotely

If you’re looking to hire a team of talented engineers that will go far and beyond to serve your requirements without having to face the hassle of recruitments, you’ve come to the right place. EngineerBabu gives you the opportunity to diversify your sourcing strategy; we provide your business with quick and impressive access to worldwide talent pools.

We constantly engage with high-caliber talent that is beyond average and once a client is on-board with us, we kick off the first candidate in just 5 days. Other than that, our workspaces are high-quality and fully equipped to suffice everything you need to be productive and deliver a high-quality product. Above all, we do all this taking 50% less time compared to other recruitment companies.

We hope that this blog addresses the queries about the difference between a data scientist and a data engineer. Let us know in the comments below if you would like to read more such blogs. Also, if you liked this blog, feel free to share it with your friends, family or acquaintances.

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