Data Science or Data Engineer – What path should you follow?

Data Scientist or Data Engineer

A notoriously large number of individuals are often unable to differentiate between data scientists and data engineers. Hence the difficulties associated with choosing a path for data science prospects.

Data scientists are more involved with statistical and mathematical analysis of presented data for better understanding, while the data engineers are responsible for building data infrastructure.

This article takes a detailed look at both paths to help prospective data enthusiasts pitch their tents fully informed.

In recent times, data scientists are often in the media limelight. Their salaries also seem to be growing exponentially, creating an earning gap against data engineers who are often touted to possess superior skills in data analysis.

To effectively understand the difference between both paths, let’s take a detailed look into each field.


What is Data Science?

Data science is the field of study that involves large quantities of data using present-day tools and techniques to identify patterns that are not obvious at first glance, collect coherent information, and make business decisions. Data science uses advanced machine learning algorithms to construct models capable of prediction. 

The data can be sourced from various sources and presented in a myriad of formats. A study was done as recently as 2013, and it revealed that ninety percent (90%) of data in the world was created in the preceding two years. This suggests an exponential increase in data generation, a trend likely to continue especially considering technological advancements and internet applications. 

The vastness of data science application and its growing importance has created a new avenue for profit as it is often touted as “the oil of the 21st century”. Data science is very dependent on artificial intelligence, especially the aspects of deep learning and machine learning. It can be simplified into five stages:

  1. Capture: Data collection, entry of data, reception of the signal, and data extraction.
  2. Maintain Warehousing of data, data cleansing, staging of data, processing of data, and data architecture.
  3. Process: Mining of data, classifying or clustering, modeling data, and summarizing data.
  4. Communicate Reporting of data, visualization of data, making of decisions, and business intelligence.
  5. Analyze Exploration/confirmation, mining of text, predictive analysis, qualitative analysis, and regression.

Industrial Applications of Data Science

As previously explained, data science has numerous applications in a vast number of fields but below are some examples for appreciation:

  • Healthcare:  Data science has helped medical professionals find new means of understanding diseases, practicing preventive medicine by the quicker diagnosis of conditions, and exploring new avenues for treatment.
  • Self-driving cars: Main players in the quest for autonomous vehicles such as Ford, Volkswagen, and Tesla have incorporated predictive analysis in some of their vehicles. A combination of data science, machine learning, and predictive analytics help in navigation, lane alignments, and speed limit adjustments.
  • Logistics: Companies like UPS use data science to improve data collection and processing, and delivery routes.
  • Entertainment: Music streaming platforms use data science to suggest songs identical to the genre the user seems to favor; the same goes for movie platforms like Netflix.
  • Finance: Finance technology (Fintech) companies such as PayPal and Stripe have been investing heavily in data science in a bid to generate machine learning tools capable of flagging fraudulent activity.
  • Cybersecurity: Cybersecurity firms use data science to identify numerous samples of malware that would previously have to take much longer. Considering how vital data and the internet are to all aspects of modern society, the importance of a competent security framework cannot be over-emphasized.
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Having explored what data science is and its ever-increasing relevance to almost all facets of life, an examination of the fields, skillets, and tools required is necessary:

  • Data Analysis: The skills required for data analysis are R (Python for data science), Statistics, and Python. Special tools are Jupyter, any IDE (for scripting. E.g., Spyder, Visual Studio Code, etc.), RapidMiner, SAS, Excel, MATLAB, and R Studio.
  • Data Warehousing: The skills required for data warehousing are Hadoop, ETL, Apache Spark, and SQL. The tools include AWS Redshift and Talend/Informatica.
  • Machine Learning: Machine learning skills are Statistics, Algebra, Python, and ML Algorithms. The tools are Mahout, Spark Mlib, and Azure ML studio.
  • Data Visualization: The skills required for data visualization are Python libraries and R, while the tools include Cognos, Jupyter, RAW, Google Data Studio, PowerBi, and Tableau.

Data Engineering

One of the keys to identifying the difference between data science and data engineering lies in the word “engineering.” Engineers are builders and designers; data engineers are tasked with designing and building systems that transform and convey data to a form useful to data scientists and other users. Data engineering deals with the movement and organization of large volumes of data, while data science is more concerned with using the data.

Though previously not seen as a role by itself, the last few years have seen a surge in demand for data engineers. Data engineering is a subset of software engineering that deals with data warehousing, crunching data, mining data, managing metadata, data infrastructure, and data modeling. 

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Below are skill areas in which a data engineer should be reasonably proficient:

  • Foundation knowledge of software engineering.
  • Distributed systems.
  • Open frameworks: This includes Kafka, Hadoop, MapReduce, and Apache Spark, among others.
  • SQL, which is one of the most important.
  • Programming: The most widely accepted programming language is Python. Scala is another; it serves as a base for Kafka and Apache Spark.
  • Pandas: A Python-based library that manipulates and cleans data.
  • Dashboard/Visualizations.
  • Cloud platforms: The most prominent cloud skill set required is probably AWS.
  • Analytics: A grasp of mathematical (particularly probability) principles is needed to manipulate data properly.
  • Data modeling: Knowledge of the means of structuring tables and partitions and situations that call for data to be normalized or denormalized is essential.

The interdependence of data science and data engineering indicates that data engineers can work in institutions that employ data scientists and vice versa.

Employability and Earnings

As recently as June 2021, the average salary of a data engineer was quoted at $92,496 per year (payscale.com). The more the experience, the higher the engineer’s salary; those with experience tending towards twenty years earn $115,000 and above. 

The 2020 Dice Tech Jobs Report identified data engineering as the fastest-growing tech occupational field in 2019, with a growth rate of fifty percent (50%) more than the preceding year.

Glassdoor estimates the average salary of a data scientist to be $113,000, significantly higher than the amount earned by the data engineer. The U.S. Bureau of Labour Statistics predicts that the increased demand for data scientists will create as many as 11.5 million jobs by 2026.

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Job opportunities would be a non-issue for aspiring data scientists and engineers. An assessment of an individual’s skill set and natural inclination would better inform which of the two would make a suitable career path.

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