Difference between a data scientist and a data engineer

• The terms Data Engineer and Data Scientist are distinct. A Data Scientist develops statistical and machine learning models.
• Data Science involves extracting useful business insights. Data Engineering is building the pipeline or workflow to allow data to flow seamlessly from one instance to another.
• Data Scientists and Data Engineers both require specific skill sets to solve the problems they face on a daily basis. They also follow different approaches.
• Both fields offer a wide range of career opportunities, and the scope of work is excellent. They are popular choices for IT-based organisations.

We will provide you with in-depth information, such as skill sets, career options, roles, salaries, and results, to help you gain a better understanding and distinguish between the two roles.

What is Data Science ?

Data Science is a multidisciplinary subject that combines methods and tools of computer science, statistics, and application domains to analyze structured and unstructured information to generate meaningful insights.

A data scientist digs deep into data to build and execute AI-based algorithms across various business verticals in order to solve complex problems. They also use data visualisations and dashboarding to identify patterns and trends in the industry.

What is data engineering ?

Data Engineering is a method of building and designing a stack of processes for collecting, storing and enriching data, as well as processing it in real time with authentication. This discipline uses tools and programming language to create APIs for large-scale data processing and query optimization.

Data Engineers take care of the hardware and software needs of an organisation, while focusing also on IT and Data Protection and compliance with security policies. They are also in charge of ensuring that data is tracked from the servers to the applications.

Data scientist vs data engineer – Roles & responsibilities

Data scientist and data engineers are both on the same path. As big data grows and evolves, new functions and specialisations will be created. Although both roles are pursuing insights through accurate data analyses, their roles and responsibilities get separated when it comes to achieving desired results.

The Role of Data Scientist

Data scientists are primarily responsible for analysing, processing and modeling a large amount of data to produce useful information. This will aid in solving problems or assisting with the decision-making processes within business or project requirements.

Data scientists are responsible for a variety of tasks, including:

• Data extraction, collection and gathering can be done efficiently
• Validate, clean, and process data
• Use machine learning, artificial Intelligence, statistical data modelling and predictive analysis to analyze data
• Create data models and algorithms
• Interpret and refine the results of studies
• Create actionable and useful insights using the collected data
• Use data visualisation tools such as slide decks and dashboards to present the findings.
• Automate routine tasks and develop predictive models with machine learning algorithms
• Find out what’s wrong, identify opportunities and trends in a lot of data.
• Create data-driven solutions to solve complex business problems

Click here to learn more about Data Science Classes in Pune.

The Role of Data Engineer

Data engineers lay the foundation for data science. Data engineers must lay the foundation for data scientists to work on their craft and meet the needs of the organization. They build the infrastructure and architecture needed to store, prepare, and obtain raw data that data scientists can use to perform their tasks.

Below are the roles and responsibilities of a Data Engineer.

• Collaboration with stakeholders and management to identify the needs of the business or project
• Design and develop databases and analytic infrastructure and servers
• Select the data sources and data sets that are relevant to your requirements
• Deploy ETL Processes

1. Extract significant data from different systems and sources, storing them in data warehouses, and creating data.
2. Transform after the conversion from source format into single and viable structural formations.
3. L Loading and logging data into files.

• Data scientists can prepare and clean raw data before they are analysed.
• All data processing should be optimised and maintained to ensure efficiency and scalability
• Create and deploy ML algorithms
• Help and resolve technical issues relating to data and infrastructure
• Compliance policies can be used to improve data security and reliability.
• Redesign the data architecture when there are new business needs

Data Scientist vs Data Engineer: Skills and Tools

To land a job as a data engineer or data scientist, you need to have strong big data skills. To build a career in this field, you need to learn important skills.

Tools and skills required to be a data scientist

Data scientists need a variety of specialized tools and skills to be able to identify trends and make more informed predictions. Some of them include –

• Programming Languages

R and Python are two of the most popular programming languages for data science. They are well suited to data analysis operations.

• Machine Learning Tools

Artificial intelligence tools like TensorFlow Apache Mahout and Accord.Net can help you improve the accuracy of your analytical models.

• Data visualisation

You can present complex data using visualisation tools like SQL, Bokeh, Plotly and Tableau.

• Statistics

Data scientists must be able to apply statistical concepts and techniques to analyze and work with data in order to get better results.

Tools and skills required to be a data engineer

Here are some essential tools and skills that you will need to be a data engineer in order to deal with the growing amount of data.

• Database Knowledge

Knowing SQL and NoSQL in depth will allow you to store and retrieve information in real time from databases.

• Data Transformation Tools

The data transformation process can be simple or complex depending on the source, format and output desired. Hevo Data is one of the most relevant tools. Others include Talend, Pentaho Data Integration and InfoSphere DataStage.

• Data warehouse

ETL and data warehouse help you leverage big data. AWS Glue and Stitch as well as Informatica’s PowerCenter are popular tools that can streamline data from multiple sources.

• Cloud Computing Tools

Data engineers’ primary task is to set up the cloud for storing and ensuring high availability. It is therefore important to have knowledge of cloud platforms such as Azure, ACP OpenStack and OpenShift.

Data Science Classes in Pune will help you to learn more about Data Science.

Career opportunities: Data Scientist vs Data Engineer

Career Opportunities for a Data Scientist

A successful data scientist is a combination of many skills, from being an expert programmer to mathematician. The field is always evolving, so there are many different job titles and roles. Below are a few different data science jobs.

• Data Scientist

Data analysis and processing is required to help the individual make better decisions.

• Data Analyst

Data analysts perform a variety of tasks including visualisation, munging and processing large amounts of data. They must also create and modify algorithms in order to extract information from large databases without contaminating the source.

• Database Administrator

A database administrator’s job is to make sure that all databases in an organisation are working properly and then revoke services based on their needs. They are also responsible of backups and recovery.

• Machine Learning Engineer

The professional must be able to handle big data. They must perform A/B tests, build data pipelines, and implement common ML algorithm such as classification.

Career Opportunities for a Data Engineer

Data engineering is a career that has many different aspects. They take on a variety of roles including: –

• Data engineers

Data engineers must build and test scalable Big Data eco-systems. The database system must be upgraded to improve its efficiency.

• Data architect

Data pipeline infrastructure is their responsibility. They collect data from different sources such as social media or steaming to create collection processes.

• Database Administrator

The administrators are responsible for designing, testing and maintaining the database systems that store collected data. They must optimise them to ensure a more efficient and secure operation, and a smooth data collection and storage.

• Analytical Engineer

To have better control of the processing systems, they use programming languages like Python and databases such as SQL and NoSQL. They optimize the database to ensure it runs smoothly.

Discover the many Data Science Course in Pune.