If you are new or learning about the world of data or maybe data analytics, you would be familiar with the job titles that you would be joining such as Data Analyst, Data Engineer , Data Scientists… Although these roles might sound very familiar or you may think that there is a small difference, it NOT. In this writing I would be going through two well known job descriptions in the world of data. Data Scientist and Data Engineer. I would keep this article crisp and to the point and would try to summarize all my understanding without putting irrelevant details.
Who are Data Scientists and Data Engineers?
Data Scientists are the people who analyze and interpret complex large data, such as the usage statistics of a website, especially in order to assist a business in its decision-making.
Whereas on other side a Data Engineer is an engineering role within a data science team or any data related project that requires creating and managing technological infrastructure of a data platform.
From above two paragraphs I hope you get at least an idea what exactly both these profiles do. Data Scientists use their statistical , domain knowledge to analyse large datasets whereas Data Engineers are responsible to create the infrastructure related to the data that Data Scientists work upon.
What skills it take to be a good Data Scientist?
Now lets talk about the skills which are required to be a good Data Scientist. I would be listing my top three skills which I feel that is minimum required to be good at your job.
If we are talking about data scientists, statistics is the primary skill you would require. From small aggregates such as mean,median to see the data distribution , significance levels etc.
2. SQL/ Databases
When you are working with data, you ought to have knowledge about data storage and some medium to extract that data out for your analysis. Here comes the use of SQL and knowledge of databases. SQL is a programming language that helps you to carry out operations like add, delete and extract data from a database.
3. Python/R Programming
It had to be included… You cant just rely on SQL to get hold of data, you need some powerful language to transform the data and do some visualizations in order to understand your data better.
Using Python Libraries like pandas, scikit-learn, pytorch a lot of operations can be performed including machine learning models.
What skills it takes to be a good Data Engineer?
Whenever I first think of Data Engineer I think of Hadoop! So would start with that only!
- Apache Hadoop and Spark
Its framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage. Supported by languages such as Scala, Java, Python and R, I would say it is one of the essential tool that all aspiring or Data Engineers need to learn.
2. AWS / Redshift
So, we covered the distribution of the datasets now lets talk about datawarehousing.
Quick Recap: What’s DataWarehouse?
Data Warehousing is a process for collecting and managing data from varied sources to provide meaningful business insights. Thus , a Data warehouse is typically used to connect and analyze business data from heterogeneous sources. The data warehouse is the core of the BI system which is built for data analysis and reporting.
Okay so now back to AWS and Redshift .Data engineers must be familiar with the most popular data warehousing applications, including Amazon Web Services and Amazon Redshift. Most data engineer job descriptions specifically list AWS as a requirement.
RedShift : Amazon Redshift is a data warehouse product which forms part of the larger cloud-computing platform Amazon Web Services.
AWS: Amazon Web Services (AWS) is a secure cloud services platform, offering compute power, database storage, content delivery and other functionality to help businesses scale and grow. Running web and application servers in the cloud to host dynamic websites.
3. ETL Tools
ETL stands for Extract ,Transform and Load. Basically, you extract the data from the dataset/ source then transform it according to your business needs and then load it somewhere safe. In more technical terms, this process uses batch processing to help users analyze data relevant to a specific business problem. The ETL pulls data from various sources, applies certain rules to the data according to business requirements, and then loads the transformed data into a database or business intelligence platform so it can be used and viewed by anyone in the organization.
4. Programming Languages
If you want to be a good Data Engineer and want to help building data pipelines, you need programming languages knowledge most likely Java, python or Scala. These three languages are most commonly used in this field.
The Main difference in nutshell?
Data Engineers are focused on building infrastructure and architecture for data generation. In contrast, data scientists are focused on advanced mathematics and statistical analysis on that generated data.
So What’s for you?
If you like statistics and love to play with numbers along with having your hands dirty with messy data, then Data Science is the field you should go for.
If if you are really good in programming and want to build the data architectures for your data, Data Engineer is way to go.
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