The Difference between Data Scientist and Data Engineer are explained in detail which focuses on key points including tools, languages, roles, Skills, jobs and salary. 

As Data is growing in a rapid manner, the fields like Data Science and Data Engineering is a growing field in a number of industries day to day life, as both the profession focuses on managing and analyzing the data, extracting meaningful insights from the raw data by following their roles and responsibilities.

A few years ago, a startup company tried to build an analytical model. The data scientist created the promising machine learning model using past users’ behavior, but the model failed as it was trained with incomplete and inconsistent data. And Therefore to build such types of models which need a well structured Data Infrastructure.

Therefore with Data Scientist, we required a Data engineer who manages data with well structured and consistent data.

In this article, will explain the roles and responsibilities, tools and languages on which Data Scientist and Data Engineer work on-

Data Science vs Data Engineering: Roles, Skills, and Responsibilities Explained

1. Data Engineer

Data Engineer is the practical approach where Data Engineer focuses on building data Systems for the purpose of collection, aggregation and analysis of data. The Data engineer collects the data, manages the data and converts the data into useful and meaningful information, so the data scientist can work on the data to generate machine learning models or the prediction models.

Let’s learn more about the roles and responsibilities of Data engineer-

Data Engineer Roles and responsibilities

1. The main goal of a Data Engineer is to collect and manage the data in the clean and structured  format focusing on data consistency, data validation, data Integration and data security measures.

1. Data Engineers collect the data from the raw data and different sources. They also design data pipelines for the better flow of information into data warehouses. 

1. After collecting data, data engineers store the optimized data into the database system maintaining the quality of data.

1. Data Engineers design efficient data pipelines for the smother data transformation for data analysis.

1. Data Engineers also deals with big data tools like Hadoop, hive, spark etc. and also work with NoSQL Databases like MongoDB etc.

1. Data Engineers work on Cloud platforms like Amazon Web Services(AWS), Azure, google cloud etc. to build reliable and cost effective data solutions.

Skills as Data Engineer

Data Engineers should have the knowledge of different  tools and technologies used for Data Analysis. Let’s discuss the required skills of data Engineer in detail-

1. Programming Languages- Data Engineer should learn the programming languages like 

            Python, Java, SQL etc.

1. Handling Database Systems- proficient in handling data base systems using the tools like PostgreSQL and MySQL

1. ETL Tools – For Data Analysis and Data Transformation, while building efficient pipelines we use ETL Tools like Apache airflow

1. Cloud Platforms- for further data storage and processing Data Engineer should known about the cloud Platforms like AWS, Google Cloud etc. 

2. Data Scientist

Data Scientist plays an important role in analyzing large datasets, implementing machine learning and statistical models. As the demand for Data scientist in increasing in various sectors like healthcare, finance, marketing, technology etc., the work of data scientist is also increases 

to generate meaningful insights for business purposes. 

Data Scientists use the structured and cleaned data to build machine learning and predictive models to make appropriate decisions for business gains.

Data Scientist Roles and responsibilities –

1. Work with data to clean, manage and process the data in the structured format.
2. Processing data, cleaning and validating data for data analysis 
3. Start the data analysis process 
4. Applying machine learning algorithms and techniques 
5. Extracting useful and meaningful data through various data techniques like data mining and data extracting
6.  Analyzing the data to find different patterns
7. Develop machine learning and prediction models 
8. Finding the solution for the business problems
9. Discussed with the team to find some strategies to deal with the business.

Skills as Data Scientist

The requirement of a Data Scientist is high in every sector as it satisfies business gains by having appropriate data decisions. Let’s have a look on key skills needed to become a Data Scientist

1. Programming Languages- Data Scientist should known about the programming languages like Python, R, Java etc.

1. Database Languages- To maintain the Database, they should have knowledge of SQL, MySQL. Hive etc.

1. Mathematics and Statistics- should known about the statistical skills including Exploratory data analysis (EDA), Numeric Variables, Qualitative and Quantitative Analysis, Variance and Standard Deviation, Euclidean Distance, Confidence Coefficient, Understanding Parametric Tests

1. Machine learning Methods- K- Nearest neighbors using Python, Decision tree and Random forest with python, Support Vector Machines, Principal Component Analysis, Clustering in unsupervised learning, Association Algorithms

1. Data Analysis- Data Analysis using Python, Advance Excel, Python libraries like NumPy, Pandas etc.

1. Data Visualizations- Data Visualization with python, Power BI, Tableau.

Data Engineer vs Data Scientist – Tools, Programming Languages Used

1. Data Engineer 

Data Engineer work with the tools are as follows-

1. Containerization tools like Docker, Kubernetes 

1. Infrastructure as code tools like Terraform, Pulumi
2. Workflow tools like Prefect, Luigi
3. Data warehouse tools like Snowflake, PostgreSQL, MongoDb, MySQL
4. Analytics tools like dbt(data build tool), Metabase
5. Batch Processing Tools like Apache Spark, Apache Hadoop, Hive

   2. Data Scientist

To build high machine learning and predictive  models, Data Scientist works on different tools are as follows-

1. Data scientist works on programming language like Python
2. Statistical tools like R tool, 
3. Data Visualization tools like Matplotlib,Tableau,
4. Statistical tools like TensorFlow
5. Machine learning tools like Scikit-learn
6. Statistical Analysis tools like SAS

Data Engineer vs Data Scientist – Scope, Career Path and Salaries 

Data Engineer Salary

The demand for Data Engineers is ranging high and the salaries are also increasing.

According to referral sites, The Data Engineer Salaries are ranging are as follows-

• Data engineer: ₹7L – ₹17L per year
• Senior Data Engineer- ₹12L – ₹28L per year
• Lead Data Engineer- ₹21L – ₹38L per year

Data Scientist Salary

By Reviewing the  referral sites article, Here we specify the Data Scientist Salaries which are as follows-

• Data Scientist Intern Salary- ₹1L – ₹6.7L per year
• Data Scientist Salary- ₹4L – ₹23L per year
• Lead Data Scientist Salary- ₹13L – ₹50L per year
• Senior Data Scientist Salary- ₹10L – ₹40.5L per year

Data Engineer: Career Path

After Becoming the Data Engineer,  they have various opportunities which opens the doors of success, let’s discuss them-

1. Entry level Data Engineer (Junior Engineer) – As a fresher, these positions mostly focus on fundamental knowledge of Data Engineer.

1. Data Engineer- Maintains the Database for the structured format of data , builds effective pipelines for data transformation.

1. Senior Data Engineer- Deal with complex data and guide the junior Data Engineers also.

Data Scientist: Career Path

  Here is the discussed data scientist career path 

1. Junior Data Scientist

Data  cleaning, managing, and analysis purposes for learning how to build the different predictive models.

1. Data Scientist

Machine learning techniques to build predictive models and discuss the strategies as per business requirements

1. Lead Data Scientist

Manage multiple projects, Manage the team, take appropriate decisions for business gains.  

Summary:

Data Engineer and Data scientist are the emerging fields, and the scope of them is highly increasing not only in the IT sector but also a non- IT sector like finance, healthcare, marketing, technology etc.  If you want to start a career as a Data Scientist or Data Engineer, you can take the step by enrolling in our valued IT courses which will guide you to find a way better. We hope the blog data engineer vs data scientist will definitely guide you to find an appropriate way for your successful career.

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