“Engineering, I think you can pick up. [A data scientist’s] curiosity is built-in.” – Scott Nicholson
As popularly said by William Deming – “In God we trust; all others must bring data.”, the world is driven by data. Two technical terminologies that deal with the huge heaps of organizational data are Machine Learning and Data Science. Both sound confusing to many, not understanding what lies underneath and how different they are. It is Artificial Intelligence Services that plays a pivotal role in leveraging data science and machine learning. The highly significant roles of Data Scientists and Machine Learning engineers have their own set of responsibilities to play and requirements to fulfill.
Before we plunge into understanding the different roles and responsibilities that each data scientist and machine learning engineer must play, let us quickly glance through the main technologies behind these roles.
Data Science – An Overview
“Data Scientists are involved with gathering data, massaging it into a tractable form, making it tell a story, and presenting that story to others.” – Mike Loukides
Data Science focusses on extracting the actionable perceptions from base data. It is all about working and managing data that forms the crux of all organizations and all businesses.
It consists of several specialties that offer a complete and valuable output to the base data.
It can be used for a variety of activities such as detecting anomalies, automating activities, forecasting different parameters, detecting patterns, facial/vocal/text recognition, recommending preferences, etc.
Data science depends on Artificial Intelligence for making models and predictions with the help of varied algorithms and methodologies.
Data science has been providing extensive services to different industry segments like
- Healthcare – Clinical database, practicing newer techniques to treat patients, faster diagnosis, personal fitness tracking, etc.
- Self-Driving Cars – Predictive analytics, cameras, and sensors to find real-time information adjusting speed limits, preventing dangers, finding the fastest routes, etc.
- Logistics – Maximizing efficiency with modern-day tools, statistical modeling, optimal route creation, weather forecasting, and actionable insights, etc.
Machine Learning – An Overview
“A breakthrough in machine learning would be worth ten Microsofts. – Bill Gates
Machine Learning is a direct AI application that gives the capability of learning and enhancing from experience, without the programming fuss. It is a complete process that guides the computer system to predict as the data is piled on.
ML leverages the power of statistics for patter extraction amongst the wide coverage of data. It analyses the data and extracts the rules that exist behind an issue, leading to complex tasks getting solved. It helps in enhanced decision-making with techniques such as sales forecasting etc.
It analyses and extracts hidden patterns from data with valued insights. It assists in enhancing the precision of financial models.
Application areas of machine learning include the following industry segments, and much more:
- Finance – Manages financial portfolios, finds out cyber frauds, assesses risks and other irregularities
- Healthcare – Helps the healthcare segment in searching for patients with complications and needing emergency treatment, improves patient experience
- Manufacturing – offers robust preventive maintenance plans with lesser risk-taking and avoiding failures
- Sales & Marketing – Analysis of customer background, needs, likings with ML models. Searches for hidden patterns and trends to find the most liked products, client wise.
Data Scientist vs Machine Learning Engineer -The Roles To Play
As we begin to compare the details of both these important roles, here are certain attributes that are looked for, in both, as common traits:
- Good grip on programming languages (C, C++, Python, R, Java, etc.)
- Experience with statistics, matrices, vectors, etc.
- Data visualization and cleaning
- Machine learning and Big Data frameworks
- Industry proficiency
- Problem-solving skills
- Strong understanding of the industry
Machine Learning Engineer vs Data Scientist: A Quick Overview
|Data Scientists||Machine Learning Engineers|
|Professionals/specialists who put into practice the art of Data Science with scientific disciplines||Modern-day programmers who create machines/systems that can understand and apply knowledge|
|Data scientists collect, manage, and gain useful insights from structured and unstructured data||ML engineers are at the intersection of software development and data science|
|A part mathematician, part computer scientist, and part data analyst||Combines the skillset of data engineers and data scientists to delivery single-handed|
Data Scientist vs Machine Learning Engineer: Key Roles & Responsibilities
|Roles Of Data Scientist||Roles Of Machine Learning Engineer|
|Perceive the client’s business requirement and offer them an appropriate solution||Create Machine Learning Systems according to needs|
|Process, cleanse, and verify data integrity||Research and transform Data science models|
|Do Market Research||Selection of datasets and data representation processes|
|Data mining using modern procedures||Implement suitable ML algorithms and tools|
|Identify developments, correlations, and patterns in complex data sets||Carry out statistical analysis and finetune with results|
|Find additional opportunities for process development||Execute Machine Learning tests and researches|
|Investigate all business angles and create programs to perform robust analytics||Make use of big data tools to see to it that raw data is redefined as data science models|
|Assist organizations grow business with online trials||They consume data into models specified by data scientists|
|Make use of personalized information to assist organizations to recognize themselves in a better way and make proper choices||Create programs that regulate computers and robots|
|Depend upon statistical research and analysis to understand which approach and algorithm to use||Algorithms developed by ML engineers enable a machine in pattern identification and understand it itself|
Machine Learning Engineer vs Data Scientist: Must-Have Skill Set
|Skill Set Of Data Scientist||Skill Set Of Machine Learning Engineer|
|Innovative and vital thinking about data, trends, and numbers||Good control over natural language, audio, and video processing skills|
|Python/R programming language||Strong knowledge of applied mathematics, probability, and statistics|
|Experience in model building and handling data sets||Exposure to distributed systems and messaging tools|
|Data mining, cleaning, and visualization||Insight of signal processing and relevant practices|
|Understanding SQL databases||Software development skills, data structures, memory management|
|Experience in web services||Machine Learning algorithms and deep neural networks|
|Creative ability to look at numbers and trends||Data architecture design|
Machine Learning Engineer vs Data Scientist: Demand And Limitations
|Demand And Limitations Of Data Scientist||Demand And Limitations Of Machine Learning Engineer|
|Job openings are more for data scientists as compared to ML engineers||ML engineers get slightly more paid than data scientists|
|Incomplete, flawed, or messy data can lead to incomplete business analysis by data scientists||ML algorithms need to be routinely updated for best usage by ML engineers|
|It is difficult for data scientists to be good in all areas pertaining to data science since it is a vast area||ML model must be effectively constructed else it won’t be able to offer the best of data quantity and quality to ML engineers|
On A Final Note
As we have already seen, both these significant roles have a few common attributes and others which are specific to their own role. Both roles have their own goals to achieve and responsibilities to perform. While building an effective AI model, it is essential that both these roles work in sync in collaboration with each other.
Hiring a Machine Learning Engineer or Hiring a Data Scientist is a tough task and best done through an experienced software services provider. It totally depends upon the organizational need and infrastructure that decides which one to choose from – Data Scientists or Machine Learning Engineers. It is like choosing the better from the best!