In spite of the fact that data scientists and data engineers have similar skill sets, they fulfil multiple job roles in the fields of Big Data and AI development systems. The data scientist fosters analytical models, while data engineers deploy those models under production. All things considered, data scientists primarily focus on analytics, whereas data engineers rely more vigorously on programming.

Top notch insights and management are significant components for utilizing data to its fullest potential. At EngineerBabu, the data scientist and data engineer work in harmony to streamline data presentation and strategy. We’ll walk you through the responsibilities and job roles of data scientist and data engineer, so you can figure out how to utilize data for your potential benefit. Let’s learn it in detail.

Data Scientist Vs Data Engineer: What They Do?

What is a Data Scientist?

A Data Scientist analyzes and interprets data to solve business related issues. At first, data scientists investigate data and perform market research to formulate business inquiries or questions based on a particular pattern or problem area. The data scientists should then design business questions as data analytics issues.

To recognize basic patterns in a data set, data scientists utilize advanced analytical technologies supported by statistics and machine learning. Data Scientists construct models to set up relationships between data objects. However, the Predictive models forecast future occasions dependent on previous existing records. While prescriptive models suggest significant changes in business strategy dependent on current and historical information.

Data Scientists should likewise interpret the consequences of their analysis to design data-driven business arrangements. At the point when data scientists present their discoveries to stakeholders, they should construct a cohesive narration that imparts the meaning of their results and how those results can advise business strategies.

What is a Data Engineer?

A data engineer can be represented as a data proficient who develops the data infrastructure for analysis. They are centered around the production status of data and things like resilience, formats, security, and scaling.

Data Engineers as a rule hail from a software engineering background and are capable in programming languages like Java, Scala, and Python. On the other hand, they may have a degree in math or statistics that assists them with applying diverse analytical approaches to deal with business issues. 

They are likewise knowledgeable about developing and managing distributed systems for the analysis of enormous volumes of data. Nonetheless, their essential target is to help data scientists transform a pool of data into important and actionable insights.

Data Scientist Vs Data Engineer: Role Requirements

What Are the Requirements for a Data Scientist?

Data Scientists should be acquainted with the accompanying programming languages: 

• Python
• R
• Java
• MATLAB
• Scala
• C
• SQL

In light of current requirements, this is what you’ll have to get a regular mid-level work: 

• Master’s Degree or Ph.D. in Computer Science, Math, Engineering or a relevant quantitative field.
• At least five years of experience in an Analytics or Data Science Job role.
• Excellent proficiency in SQL.
• Working experience with Java and Python.
• Good Analytical and mathematical skills.
• Experience in Data Mining methods.
• Knowledge on advanced statistical concepts and methods.
• Hands-on knowledge of  Predictive Modeling Algorithms and frameworks.
• Working experience with Machine Learning techniques (such as, artificial neural networks, decision tree learning, and clustering).
• Experience in creating automated work processes (Python or R).
• Experience in using web services like DigitalOcean, Redshift, Spark, and S3.
• Experimental designing experience and A/B testing.
• Experience in visualizing and presenting data utilizing Business Objects, Periscope, ggplot, and D3.
• Experience working in a cloud system with huge data sets.
• Proven working experience in Hadoop.
• Experience with both Relational Database and NoSQL Database (for instance, Couch, MongoDB, and Neo4J).
• Good understanding of architecture and system integration.
• Experience in data analysis from third-party suppliers like AdWords, Google Analytics, Facebook Insights, and Hexagon.

What Are the Requirements for a Data Engineer?

Data Engineers need to know the accompanying programming languages: 

• Python
• Java
• C++
• Scala

In light of current requirements, this is what you’ll require to get the data engineer designation:

• Bachelor Degree in Statistics, Computer Science, Information System, or another relevant quantitative field.
• Minimum five years of professional experience or a Masters Degree with minimum three years of experience.
• Advanced working knowledge on SQL (composing and troubleshooting).
• Experience working with query composing, relational database, and knowledge over other databases.
• Experience managing, developing, and optimizing big data models and pipelines.
• Working experience with PostgreSQL, MongoDB, and Redis.
• Experience performing inner and outer root cause analysis.
• Strong analytical skills while working with unstructured data sets.
• Cloud-based data solution working experience (e.g., AWS, EC2, EMR, RDS, and Redshift).
• Proven work experience in effectively processing, manipulating, and extracting values from huge and disconnected data sets.
• Working experience on Bash Scripting or JavaScript or both.
• Excellent Project and Organization Management Skills.
• Experience with configuration and automation management.
• Working knowledge of code and scripts (for instance, Java, JavaScript, bash, and Python).
• System Monitoring, alert, and dashboard experience.
• Hands-on experience with tools like Hadoop, Kafka, and Spark.

Difference Between Data Scientist and Data Engineer

Taking everything into account, there are many similarities between a data scientist and data engineer. The thing that makes them different is what they are focused on. How about we investigate the principle difference between both i.e., data scientist vs data engineer:

A. Data Engineer: A data engineer’s objectives are more centered around tasks and development. They are liable for building automated systems and model data structures to work with data processing. Subsequently, their goal is to develop and create data pipelines and tables to help data customers and analytical dashboards. 

Data Scientist: On the other hand, data scientists are more focused on the queries. They need to ask and answer queries in order to minimize the overall expenses, increase profit, and improve customer experiences. Accordingly, data scientists gather support, analyze, and propose a conclusion to the inquiry or question. Some of the frequent inquiries that are faced, includes:

• What sort of advertisements would get the customer to buy something? 
• Is there a speedier way for package delivery?
• What impacts patient readmission?

B. Data Engineer: Evidently, both data engineer and data scientist usually rely on SQL and Python. Despite that, the tech jobs vary a lot for both data engineers and data scientists. Data Scientists use libraries like Pandas and SciKit Learn. Whereas, data engineers use Python to manage pipelines. Libraries like Airflow and Luigi are valuable in such a manner. 

Data Scientist: The questions of data scientists are more centered around ad-hoc. Data engineer questions are directed towards data transformation and cleaning up. The Data Scientists use tech-tools like Jupyter Notebook, Tableau, and so on.

C. Data Engineer: With respect to background, both data engineers and data scientists are needed to have a specific level of understanding for data and programming. Whereas, there are a few differences that surpass programming.

Data Scientist: Since data scientists are more similar to analysts, having a research-based foundation is an advantage. This could be in anything going from financial aspects to psychology to epidemiology, or anything as. As far as skills are concerned, data scientists ought to have a blend of SQL and Python experience along with a good business sense.

Wrapping Up

Despite the profession you choose, it will be fundamental to equip yourself with advanced degrees and certifications. All things considered, more organizations are acknowledging the worth of alternative education.

While there is some crossover when it is about required skills and job role responsibilities. These are not a type of interchangeable jobs. So you’ll need to make a firm decision and have some expertise in either. In any case, both positions have an amazingly positive and rewarding job outlook.

However, if you like to explore both data science and data engineering, then, at that point, you could look for a career in Machine learning. The Machine Learning Engineers are capable in both data science and data engineering and have sufficient knowledge and experience to work in both fields.

If you are looking to hire such experts then EngineerBabu is the right place for you. We are an experienced team of data scientists and data engineers to support our clients in taking their business to the next level. For any query or assistance, you can reach out to us and hire expert data scientists and data engineers or a machine learning engineer. 

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The online food delivery industry is making lots and lots of effort to understand their customers in order to discover their preferences and tastes. When people order food from a dining joint or any other restaurant, then they expect that food to be delicious and at most be at reasonable prices. They expect their food to be delicious and meet their expectations. Thus, it is no surprise that there are several food deliveries and restaurants like Zomato, Swiggy, Uber eats, Food Panda, McDonald’s, Dominos, etc. apps that have flooded the app market.

Role of Data Science in Food Delivering industry

Smart investors invested in the food industry because of the advanced age of new and innovative apps that are available for food delivery and restaurant booking. Apart from restaurants, food delivery chains, other grocery stores, young cafeterias, fast food outlets, greengrocery suppliers, diary/farm suppliers, seed/pesticide suppliers have also been benefited by these apps.

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Big data has helped this industry to grow faster and to reach the desired goals of a larger market share. Data is in the form of customer orders, location for home delivery, GPS service, tweets, social media messages, verbal reactions, images, videos, reviews, comparative analysis, blogs, and updates have become widespread. The data facilitate users to access information on average waiting time, delivery experience, other records, customer service, the taste of food, menu choices, loyalty and reward point programs, and product stock and inventory data.

These apps are not only helping in boosting sales but also guarantee that they build brand image and create a special bond and relationship with customers. This leads to repeat customers’ rate that tends to become purely hearted to their favorite brand.

Benefits of Data Science in Food Ordering

1. On-time Delivery: Food delivery can be reformed in terms of time by using different big data analysis tools and techniques. There are vast numbers of restaurants which are specialized in food delivery or home delivery of your food parcels. The predictive analysis is done with the help of Big data. Big data can collect data from various sources like road traffic, weather, temperature, route, etc. and provide an exact estimation for the time taken to deliver the goods.
2. Improved Quality: A customer always expects his food to be delicious and of the same taste in any season of the year. But it depends upon their quality, storage, and proper measurement of ingredients. Here, big data can help to check these changes and able to predict the impact of each of the food quality and taste.
3. Opinion Analysis: Sentiment analysis is based on the customer’s emotions or reactions over social media networks or reviews given on the particular food ordering app. Using Artificial Intelligence (AI) techniques like natural language processing (NLP), big data analysis tools go through this text and classify in the groups positive, negative or neutral responses. This technique of big data analysis is commonly being used by food delivery apps to analyze their customer emotions on a scale. Any negative review can be analyzed at scale and appropriate actions can be taken to prevent the spread of harmful or false words which leads to negative publicity. These techniques are incredibly beneficial for large-scale food chains like Dominos’, McDonald’s, Starbucks KFC, PizzaHut, etc.
4. Customer Service: Customer satisfaction is the hardest part of any organization to achieve. The food industry is no better. These days there are multiple channels available to be in touch 24/7 such as mobile apps, websites, social media, etc. All of these give a brief and clear idea about the services’ satisfaction. Big data can help to improve customer service and satisfaction by analyzing all the inputs from different sources.

Final words: As the on-demand marketplace growing, food delivery businesses will need to quickly capitalize on all its data that they have on various demand patterns, food preparation time, delivery routes, and more – to optimize their services and gain a competitive advantage in this firm. Restaurants and food delivery businesses that are not using big data or its techniques are missing out on a profitable opportunity to increase or gain their customer satisfaction. If the food industry cannot collect or accumulate their big data now, then it would be too late to go back, revamp the data and analyze it.

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Being a leader in offering big data services, EngineerBabu helps businesses to manage, store, and integrate massive datasets. Also, we help businesses to gain predictive insights that facilitate proactive business decisions and pre-emptive planning. Additionally, EngineerBabu promises to deliver best-in-class frameworks for multi-dimensional data aggregation and utilizes visualization-based data discovery tools for insight generation.

Hire a dedicated Data Scientist from EngineerBabu

EngineerBabu worked with 700+ small and big startups. We understand the problem in-depth and give an optimal solution. The Data Scientists of EngineerBabu are trained in all the skills of Data Science. If you are looking for a Data Science expert, hire a Data Scientist from EngineerBabu.

 Why hire Remote Data Science experts from India

• New Ideas and Fresh Energy: India is a young country. More than 65% of the Indian population is under 40 years. Thus, they come with a unique idea with positive vibes.
• Flexible Time Zone: India follows 5:30 GMT format, which gives flexibility to every country to hire developers from India.
• No Infrastructure Cost: If you hire a Data Scientist from EngineerBabu, you don’t need to build an infrastructure from scratch. Just hire a remote team and go. We have everything with us.
• No HR Manager and Recruiter Required: Exactly, no punch-ins/out, no salary slips, no time to recruitment. Just chill at your place. We will handle the entire troublesome task for you.

If you are having a project and searching for a Data Scientist let us know or can drop an email at mayank@engineerbabu.com

I am very open to any kind of feedback, suggestion, or questions if you have any kindly write in it to the comment section below!

The post How to implement Data Science to a Food Ordering App and Why? 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.

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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.

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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.

To read more related blogs or know about our services or previous work, you can visit our website.

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