Data Science and Machine Learning are two building blocks of AI. Each of them serves its purpose in an organization. Artificial Intelligence constitutes an integral part of every present organization; hence, an AI Bootcamp will help you avail a greater array of job opportunities along with attractive remuneration packages.
As a result, numerous courses are offered on this subject, and students have become confused between data science and machine learning. In this article, we will give a comprehensive and holistic overview of both career options that are convenient for you. Read along to learn more.
Data Scientist
The subject matter professionals deal with large data. They analyze the huge amount of data and assess the company’s position. The examination also helps in detecting the drawbacks and successes of a certain product or service.
Moreover, based on this data, important decisions are made to achieve the preset goals, which help the business to flourish. They play a very significant role in an organization’s success. They are estimated to have grown by about 21% over the past ten years. Read along to learn the skills required and duties performed by Data scientists.
Skills
There are four basic skills every data scientist needs to have. They are
- First and foremost is programming knowledge. Dealing with data will require you to know various programming languages like Python and R. You will need such knowledge to handle complex algorithms.
- Your mathematical skills must be very strong to apply for data science jobs. Candidates need to clearly understand concepts like Probability, Algebra, Linear Algebra, Calculus, etc.
- To be precise, the central function of this profession is to analyze complicated and huge amounts of complicated data for important insights. Hence, mastering different analytical tools like Hadoop, Pig, and SAS is one of the basic requirements.
- Students need to be familiar with the operations of software like MySQL, Data Warehouse, Tableau, etc. These are the major tools for data collection, storage, cleaning, etc.
Duties
The four fundamental roles of a data scientist are,
- The first job of a data scientist is to collect data from different sources and analyze it for further use.
- Next comes the segregation and structuring of the data. Drawing useful information from a cluster of data is never possible. Hence, the data needs to be categorized and organized for future utilization.
- The third role of a data scientist is to analyze the data and provide the organization with useful information, such as the product or service’s failure and potential pitfalls. They also help comprehend consumer demands and use them for strategic planning.
- They also keep track of the market data and the ongoing trends to help an organization stay up-to-date with the competitive world.
Machine Learning Engineer
The professionals in this sector are trained to build all artificial intelligence software utilized by machine learning and upgrade existing systems. They structure the AI system to be able to hold huge amounts of data so that they create algorithms that will enable them to make predictions for future use.
If you require a more critical understanding of the subject, watch this video
Skills
Some of the major skills to be possessed by machine learning engineers are,
- The candidates must be well-versed in programming languages like Scala, C++, C, and many more for building systems.
- The function of mathematics here is more fundamental. Hence, having the capability to appreciate the subject is very important. Students must clearly understand concepts like nominal, multivariate calculus, binomial, statistics, etc., since these would help them create a system.
- They should have in-depth knowledge and skills to extract the best feature from a given signal. This is known as Advanced Signal Processing. Bandlets, contourlets, curvelets, etc., are some of the concepts they need to know well.
- Students must be satisfactorily familiar with the concept of Natural Language Processing. This enables them to create devices to interpret and manipulate human languages. Sentiment analysis and Summarization are two of the most important NLP concepts used.
Duties
The four main duties performed by Machine Learning Engineers are as follows,
- They analyze the data science technology to gain important insights. They utilize this information to build machine learning models. Moreover, they work closely with data engineers to create model pipelines.
- Machine learning is integral to data science; hence, candidates from both fields are needed to build models.
- The professionals not only create new models but also upgrade the existing ones. Hence, they write production-level codes that cater to the demands of production. They also make necessary alterations guided by the feedback received from the end user.
- Various other roles associated with this profession are data cleansing, statistical analysis, conducting machine learning tests, etc., and using this data to refine the models for enhanced experience.
A quick glance
We will give you an overall view of the comparative study,
| Point Of Difference | Data Scientist | Machine Learning Engineer |
| Skills | Major skills includeData ScienceProgrammingData AnalyticsStatisticsData VisualizationProblem solving | Major skills includeData ModelingSoftware EngineeringMachine Learning concepts and frameworkProgramming |
| Duties | The roles fulfilled includeIdentify issues that Machine Learning can solveCollect and analyze dataGenerate synthetic dataBuild data annotation strategiesCommunicate with Cross-functional Stakeholders. | The roles fulfilled includeUse ML and DL (deep learning) concepts to build modelsElevate existing models for better performanceMonitor the product’s performance and make necessary rectificationsDevelop tools to optimize the deployment workflow. |
| Annual Salary | $100,000 – $105,000 | $105,000 – $110,000 |
| Major Programming Skills | R/SQLSagemaker, Google Collab Notebooks | Python/ScalaLinux, Bash |
From the above machine learning engineer vs data scientist comparison, both professions are crucial for the upgrade of an organization. Also, the remuneration ranges are quite similar. Additionally, it is also dependent on experience, skills, and educational qualifications. The career choice should be made based on the candidate’s interest.
Final thoughts
If candidates still need clarification to choose between machine learning engineer and data scientist, they can enroll in the introductory course to check their interest. They can also attend workshops or intern in both subjects to comprehend their inclination. However, whether choosing the course certification or internship, opt for reputed organizations for better future opportunities.
