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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Business Intelligence is a very wide term used to cite technologies and applications. Both are thoroughly used in collecting, analyzing, and presenting business details and information. The demand and worth of BI has increased gradually after the worth of data gathering and analysis is valued. As a result, spreadsheet maintenance has become an age old activity against actionable and insightful data visualization and interactive business dashboards.

After the COVID-19 impact all over the world, the year 2020 particularly became a major opportunity for the BI trends and the industry. Hence, last year business intelligence trends will continue to play out through 2021. However, the BI market is evolving rapidly. Thus, is the high time to monitor the trends of business intelligence and work over it. Thus, here we reveal the top 5 business intelligence trends in 2021. But, before that learn in brief what BI stands for and its benefits.

What is Business Intelligence (BI)?

Business Intelligence is a business-oriented feature of Data Analytics. Wherein the data collected by the business is changed over into knowledgeable and actionable insight with the goal to turn out to be more profitable. Business Intelligence can have numerous aspects including analyzing various types of data that are generated by the business. Afterward, present the resultant acquired from the data to decision-makers in the business using reports, presentations, graphs, dashboards, summaries, and so on.

In simple words, Business Intelligence doesn’t straightforwardly tells the decision-makers what the roadmap should be followed and what decision to make. Yet it gives details of all the aspects based on the data analysis that can be used to make an intelligent and well-informed decision. 

For instance, suppose a global car business needs to analyze their car sales on a country-by-country basis and understand which models perform better. They can utilize Business Intelligence to gather information about all the models they have. Such as worldwide sales, popular models among customers, some feedback for improvement, and many more. By breaking down such details in numerals form, the business can learn about which models are more famous in each country. In addition to that, how they can make improvements in their products or sales to generate profit.

Overall, Business Intelligence serves multiple purposes and introduces numerous benefits to businesses. Let’s learn about the benefits of using BI systems in your business.

Benefits of using BI Software in Your Business

• Fast and accurate reporting
• Valuable business insights
• Competitive analysis
• Better data quality
• Increased customer satisfaction
• Identifying market trends
• Increased operational efficiency
• Improved, accurate decisions
• Increased revenue
• Lower margins

Top 5 Business Intelligence Trends in 2021

1. Artificial Intelligence

The U.S. companies are taking up their AI interests in a post-COVID world, with the outcome sure to reflect in the coming years. In a survey by PWC, 86% of executives said that AI will be a standard technology at their organization in the next few years.

Regardless of whether through personalization driven by Machine Learning, data augmented prep, auto-outlined data visualizations or clarifications made by natural language generation, AI technology has influenced BI and is here for the long haul. On account of machine learning algorithms and advanced neural networks the modern BI trends empowered with augmented analytics. It can identify irregularities, examine sudden events and dig deeper to find the most useful and relevant data possible. As opposed to its name, Artificial Intelligence accumulates information and makes it more accessible to all.

2. Self-Service Business Intelligence Would Be On Rise

The BI trends and tools have used the data stores in central distribution centers. However, a business has become inescapable and needs admittance to a tremendous amount of data in a short time-period by various users. This results into, decrease in reliability of data stores.

Therefore, the organizations are now moving to self-service business intelligence that gives more independence and flexibility. Self-service Business Intelligence trends provide various classifications of users and their obligations.

They have perceived that 5% of business analysts depend on BI tools of which ¼ users need greater flexibility while going through business data.

3. Natural Language Processing

Natural Language Processing (NLP) is an AI-technique. It shows computer programs how to correctly translate a language into a structured form that can be interpreted by humans. It has contributed a lot to the development of business intelligence.

Business Intelligence trends in NLP have definitely improved; how organizations gather and filter relevant data. Examples of NLP that BI applications will depend on considerably more, in the future include: 

Natural Language Generation: Converting any structure into a human language, for example, the technique used in Cortana. 

Machine Translation and Learning: Machine Translation works by changing one language to another like Google Translate. ML is totally a unique area that deals with how well a PC or software can train itself to manage new issues.

Speech Recognition: This is the technique used by software like Siri, Google Voice, Echo Dot, etc. for understanding a human’s perception when you communicate with them and react in a similar manner.

4. Increase in SaaS and Cloud Adoption

The rapidly increasing number of organizations are trying to migrate to cloud services and leverage all the advantages of cloud-based BI. Regardless of hosting on a private or public cloud or preferring a Software as a Service (SaaS) BI trends. The demand for cloud BI keeps on growing at an enormous speed. Thus, based on a 2020 Cloud Computing and Business Intelligence Market Report, 95% of enterprise software vendors think of it as mandatory to have and 54% of enterprises say it’s either basic or vital to their business drives. 

For organizations and their IT teams, some of the critical advantages of executing cloud BI include:

• Stress reduction due to hands-off updates and maintenance.
• The Cloud data are accessible on any device from any location.
• Increased scalability and flexibility.
• Easier administration and management.
• Higher cost efficiency and lower ownership cost.
• Security given by calamity assistance and data backup.

5. Automation

Automation is a growing and trending system in the market. According to Adobe’s Digital Trend Report 2020- 64% of big organizations said they used AI to automate data analysis in 2020, up from 55% in 2019. Automation can deal with repetitive tasks and tedious obligations in any case performed by humans, freeing up those assets for others.

As data analytics increases and data volumes scale up continually. Then automation will be essential to remove manual data processes, considering additional time spent on decision-making. The capacity to quickly examine data and act on the outcomes defines BI. Hence, the automation speeds up business processes at all steps of the data workflow. This makes it simpler for users to find the solutions for their business questions whatever they need.

Conclusion

As a company, being data-driven is not any supreme service, however it is the expectation of the modern business world. The last decade of 2011-2020 has been a major time-period for data. Thus, 2021 and onwards it is definitely going to be an exciting time of leaving all the past hypes. It boosts to move forward to extract maximum value from the online business intelligence software by following the latest trends of business intelligence. 

Thus, the companies in all the industries should aim to use all the insights effectively with business intelligence systems as the data becomes more diverse and complex. Due to this, the year 2021-22 will witness a huge increase in the demand for Data Analyst or Data Scientist. Especially the ones who can come up with a unique and reliable solution for the data related issues.

So, if you also like to hire a dedicated data analyst or data scientist then EngineerBabu is the right destination for you. We understand business needs and dedicatedly work to deliver best services and quality business solutions. For more information and detail you can Contact Us, we are here to help you in the best possible way we can.

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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.

Recommended Reading: Data Scientist vs Data Engineer

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.

Recommended Reading: Create an App like Seamless

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!

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