Today's world is just about data. In fact, data is present everywhere in the world. Every year, the amount of digital data is rapidly increasing, thus, increasing its worth every day.
An article in Forbes stated that:
"Data is growing continuously and rapidly with the speed that by the year 2010, about 1.7 megabytes of new data will be generated per second for every person on the planet."
Every decision making lies on the available data as it has become the primary need of every organization. In fact, data is used to collect the insights on consumer activities. It aids in fighting the market challenges and help compete rival organizations.
Big and Small companies are more and more investing in the management of this data by hiring data scientists and data engineers.
Each has a different job role, and each is crucial for the industry. If you are planning to extend your career in any one of both of the fields and want to learn data science and data engineering, then you should know the different between the two.
Let’s decode and see how data engineers are different from data scientists.
Significant Difference between the Two – Date Engineers vs. Data Scientists
Date Engineers
If you are acquiring to become a data engineer, then you must have sharp technical skills to instantly build and maintain the architecture of a system.
Data engineers work to harvest the data from the different resources and combine the carefully gathered data and information to build data pipelines.
A data engineer plays an important role of a mediator between the data scientist and data analyst. In fact, a data engineer joins forces with the data scientist and work together to bring out the best results. The data engineers are required is responsible to make the data (or big data) available for analysis. Moreover, the job role focuses on building the mechanisms for the smooth access to the information and make sure the data can further enhance to the organization’s requirement and need.
Data Engineers areas of expertise includes:
- Data Synchronization
- Data Models
- Information Flow
- Expertise in logical operations
- Data ingestion
To be an expert data engineer, you must have amalgamated skillset in the following programming languages and frameworks.
Programming Languages
- Python
- SQL
- SAS
- JAVA
- R.
Frameworks
Being a data engineer or learning to become a data engineer, one must have hands-on expertise on:
- Hive
- MapReduce
- Apache Spark
- Hadoop
- Data Streaming
- NoSQL
These are the programming languages and frameworks a data engineer should be expert of and have full command on. The familiarity with the technical tools is necessary to become an expert in the field of data engineering.
Data Scientists
Data scientists, in simple term, evaluate and extract the data using the tools and technology, help create the data models to get the desired results and put them into action.
As mentioned above, data scientist works together with the data analyst and data engineers. They process the freshly acquired insights from the data and provide the result oriented conclusions to serve company’s benefit.
You can say they are the data wranglers and their focus is on organizing the big data. In an industry and organization, data scientists are usually the problem solver but they are also the curious bodies as they ask a lot of questions, relevant question to be precise, to solve the problems.
Their job position entails them to know the business problems, collect data from logs, web servers, build complex models, perform in-dept business research and make recommendations accordingly to the complex business scenarios.
Data scientists have the specialties in the following:
- Data acquisition
- Data visualization
- Data analyzing and data cleaning
- Problem solving
- Decision making and
- Result driven recommendations
If you are interested in data science want to have a career in data science, then you should have hands-on skill-set on the programming languages and framework:
Programming Languages
- Python
- SQL
- Julia
- MATLAB
- JAVA
- R
- TensorFlow
- Numpy
- Pandas
- Spark
- Hadoop
- Matplotlib
Frameworks
A data scientist should have be well-versed in the following machine learning algorithms as their designation required them to execute complex tasks and extract analytical insights:
Algorithms
- Decision Tree
- Logistic Regression
- Support Vector Machine
- Feed-Forward Neutral Networks
- Random Forest and K-means
These are just a few of the algorithms I have listed down which data scientist use to collecting and reporting data.
What Does the Data Engineers and Data Scientists Do?
In the survey conducted in 2018, Data engineers earned the position on number 5 in the highest paying salaries in the Information technology field. It’s the hottest and through-the-roof demanding industry to get into.
More and more people are now interested in the fields and you can easily get data science online training and data engineering courses.
The job role of date engineer can be described as managing, optimizing and integrating data collected from the various resources. As data engineer have the computer science degree and technical background, It would not be wrong to say that their job role is more on the technical side: map and create infrastructure out of the data collected, build queries to guarantee the easy access to the data and assembling data (data pipelines).
Whereas Data scientists, analyze the data shared by the data engineer, collecting of data and managing the raw data, filter out clean data, deal with the clients and team needs by creating visualization and create data which can positively impact the company in the larger scale. But data scientist jobs is not strictly limited to these only.
There are several resources available online which you can use to be proficient in data science. To get data science online training and data engineering, you have easy access to the vast collection of video tutorials, books and websites.
Select the one which you are more interested in. To give you a head start, I would like to divulge this news that by the year 2020, the need for data scientists in the industries will increase and there will be more job opportunities in the market for data scientists.