Data engineers and data scientists operate in an area of specialty that overlaps one another. Their skills and responsibility are on the same path; they only differ in focus. 

Data engineers are focused on the construction and deployment of infrastructure for data generation, while data scientists are focused on statistical and mathematical analysis of the generated data. Essentially, data scientists are engaged in constant interaction with the infrastructural data constructed and managed by data engineers.

 Data science.

 Data science is a field with core mathematical and statistical root. They create an advanced analysis using math and create artificial intelligence and machine learning models using applied math. Data scientists learn to program as a part of their basic need to analyze complex data and solve tough problems, but their level of programming is way below the standard for a data engineer or programmer. 

 Data Engineering.

 Data engineering is a field built on a programming core. A data engineer's programming background is mostly Python, Java, and Scala. Data engineers are specialized in big data and distributed systems with well-honed system creation and programming skills. A data engineer has advanced knowledge of numerous technologies and frameworks and also the technical know-how of combining them to develop solutions that activate a company’s business processes to utilize data pipelines. 

 Specialty overlap.

 Data engineers and data scientists' skills overlap in the areas of analysis, programming, and bid data.

  1. Analysis:Both data scientists and data engineers analyze data. A data engineer can analyze data on a basic and intermediate level, but a data scientist analyzes data on an advanced level. The analytical prowess of a data engineer can be said to be substandard when compared with the analytical prowess of a data scientist.
  2. Programming:Both data engineers and scientists can program, but programming for a data engineer is a necessity, and they operate on an advanced level of programming, whereas a data scientist can only program on a basic and intermediate level to keep up with their work when needed. 
  3. Big data: Data engineers create big data pipelines using their advanced system creation and programming skills, while the data scientist uses their advanced math skills and basic programming skills to create progressive data products with the use of data pipelines created by data engineers. In big data, the intersection between both parties comes from the development and use, as bot the data engineers and data scientists have to have a developmental knowledge of the solution being deployed. 

The image below shows the overlapping skills of a data scientist and data engineer and at the same time, explains their roles in a simple and memorable diagram.

To get ahead in data science and data engineering, constant knowledge must be acquired through learning. Learning can be done online or offline. Learning can be done personal teaching with data science online training courses or through accredited centers in your vicinity. 

 I’ll briefly outline some great data science online training courses and data analytic training that can get you on your way to becoming a great data scientist or data engineer.

  1. Data science specialization - Coursera.

  This course series includes a vast focus on statistics, which is the foundation of data science. This particular data science online training certification is not totally free but very affordable. It costs $49 per month for the certificate and materials. This data science course needs adequate programming knowledge, and an understanding of algebra since specializing in data science requires a combination of both theoretical and practical use of R programming language.

  1. Data Quest.

  Data quest is a resource that augments your data science training certification online. Data quest prides itself in organization and also allows you practicality by learning with the use of realistic data science projects and a helpful Slack community where you can ask questions. When you subscribe to data quest for your data science online training certification, you have unlimited access to either R or Python programming languages or both; the choice is yours.

  1. CS109 Data science - Harvard.

  This data science online certification course makes use of python programming language, touching details of differing data science libraries to proffer solutions to real-world problems. A combination of theory and practical applications, exploring every part of the data science process, makes this course one of the most sought after by beginners. This course does not offer any form of certification and does not have an interactive platform like edX or Coursera, but it’s totally free and worth it.

  1. Data Science fundamentals - IBM.

  The data science fundamentals online certification from IBM provides various free online courses through its cognitive class online portal. This data science online certification course uses the R programming language, with hands-on experience, methodology, open-source tools, and basic data science. An individual with knowledge of computer science will learn faster than an absolute beginner without prior experience.

  1. The open-source data masters.

   This data science online certification course encapsulates a collection of open-source resources and materials is available for free online and is not offered by any specific institution or organization. This course has no certification and can be completed at your own pace. It helps develop a basic algebra and statistics knowledge as the foundation of data science. The course also includes data visualizations, SQL and NoSQL databases, Hadoop MapReduce, and Twitter API using python, all being processed in natural language.

 Data analysis is an integral part of data science, hence data analytics training. Data analytics training can be done either online through accredited data analytics training websites or through accredited physical data analytics training facilities in your vicinity. 

 A data analyst must be able to gather data from numerous sources, organize the data according to relevance, search for patterns and trends, generate ideas for advancement, and lastly, to collate and present detailed reports, these skills are acquired through the data analytics training. 

 Getting data analytics training gives a huge room for growth. The amount of data circulating within the data universe, increases with each passing day, creating room for growth and better paying job opportunities. A certified data analyst who has completed the data analytics training course can earn as much as $300,000 annually, depending on the organization. 

 Data technology has come to stay, and the data technology industry keeps getting bigger each day, this is the time to harness the opportunity.