As a budding Data Scientist, you need to understand just how many opportunities are available to you through the field of Data Science.

Surely, Data Scientist is not the only job role out there. Are you clear about the profiles that you are aiming for? These different roles might be that of a Data Scientist, Data Analyst or a Machine Learning Engineer, etc.

Do you know the difference? How are these three roles different from each other? 

Who is a Data Analyst?

The professionals who clean the data and make it answer the business’ queries are the Data Analysts. These answers are the stepping stones to arrive at crucial business decisions.

Data Analysts are Detectives with a magnifying glass in their hand, always searching for that right set of data which will reveal the insights.

A Data Analyst is typically expected to do the following tasks.

– Clean and organize the data
– Broad data analysis through descriptive statistics
– Analyze the trends 
– Build dashboards and visualizations to help business with data interpretation and taking decisions based on it
– Storytelling– ideating the interpretation to the internal teams and clients

Who is a Data Scientist?

Data Analysts move up the hierarchy to become Data Scientists. Mind you, they are highly in demand!

Just like you can barely spot a unicorn, skilled Data Scientists are also hard to find!

Times are changing and so is the scarcity of Data Scientists. With the increase in understanding of this rewarding profession, more people are opting for this profile and getting trained in it.

An expert in machine learning and statistics, Data Scientists build machine learning models that predict and solve critical business problems. What a Data Analyst can do, a Data Scientist can do that and even more. A Data Scientist is adept at cleaning, analyzing and visualizing data-sets and instructs and improves machine learning models.

Here are some important tasks for a Data Scientist:

• Evaluating statistical models to gauge the validity of the models’ analysis
• Creating refined predictive algorithms with the use of machine learning
• Regularly testing and improving the efficiency of machine learning models
• Using data visualization to summarize advanced analysis

Who is a Machine Learning Engineer?

More than just a software programmer, machine learning engineers are the brains behind complicated machines that learn and perform with minimal human supervision. 

Machine learning engineers go beyond this and develop programs that let the machine perform actions, even when they are not instructed to do it. 

Here are the tasks that a machine learning engineer typically performs.

– Use a programming language and machine learning libraries to test the competency and performance of machine learning algorithms, by conducting different experiments
– Code to carry out prospective machine learning solutions
– Improve the system’s performance and scalability
– Ensure smooth data access through database and back-end systems
– Deploy custom-made machine learning codes; codes specific to the system or end goal
– Build use cases for the programs and algorithms you develop

What do Data Analysts do?

Data analysts research and produce insights.

They aim at searching for answers to the questions posed by the data scientists, using the available data. Data analysts work on what is before their eyes, i.e., they work on the evident facets of the business with a fresh perspective. Their approach, however, depends on the problem before them. 

They use a variety of languages- Python, R, SQL, Javascript and HTML and the tools they use are- Tableau.

What do Data Scientists do?

Data scientists garner insights and communicate these findings to the business stakeholders who have a non-technical side.

Picture them as a bridge between the technical and non-technical people. This is why skills like data visualization and storytelling make more sense for them. They design illustrated visualizations and make insights comprehensible by everyone. Data scientists are a combination of data analysts and a step more than that. 

MATLAB, Python, R, SAS and SQL are the languages they use in their daily modus operandi. Python and R are the only tools that a data scientist uses to communicate findings and for other processes.

What do the Machine Learning Engineers do?

Grouped at the juncture of data science and software engineering, a highly-skilled machine learning engineer ensures that the raw data is restructured as a data science model that can be adjusted as per need. They prepare the production level model to process massive data.

Do you know the most interesting part of their profile?

They build programs and develop algorithms that help a machine to comprehend commands to think on their own. 

Because experimentation is the base of what they do, entrepreneurial instinct and creativity are the basic criteria of a machine learning engineer profile.

Writing sophisticated codes and learning advanced technologies to make AI a common notion for all, is the highlight of this profile. Python, Java, R, C++, C and JavaScript are the languages, while MATLAB is the tool, machine learning engineers use.

How different are the three jobs?

It’s great that by now, you are aware of the basic difference between these same yet different companions- Data Analysts, Data Scientists and Machine Learning Engineers. It’s time for some real talks now; that of job sphere.

Here are the points of differentiation between the three roles:

– Qualifications
– Skills
– Roles and Responsibilities
– Salary
– Application Areas

	Data Analyst	Data Scientist	ML Engineer
Qualifications	– Knows BI and data       warehousing – Proficient in analytics based on Hadoop – In-depth knowledge of analytics and SQLTools and skills related to data storing – Master of data architecture tools and components	– Fair knowledge of database systems – Expert in a variety of analytical functions – Having a sound knowledge of MapReduce, Javascript and Python etc. – Expert in predictive analysis, mathematics, data mining & correlation – Knowledge of ‘R’  – Master of machine learning and deep statistical insights	– Python and/ or C++ or C expert – Proficient in a minimum of one machine learning sphere  – Expert in the use of machine learning methodologies to products and services – Exceptional data structure and algorithm skills – Adept in changing large existing code base
Skills	– Statistics – Mathematics – Programming tools – Spreadsheet tools  – Visualization tools	– Statistics – Mathematics – Programming tools – Hadoop Storytelling & data visualization – Exceptional machine learning skills	– Python/Java TensorFlow – Linear algebra – Probability – Statistics – Knowledge of neural network architecture – Predictive modeling techniques – Hadoop/Spark
Roles and Responsibilities	– Handle reporting to meet the business objective – Perform a dedicated data analysis that answers the stakeholders’ queries – Communicate research results and put them into business-specific actions	– Gather complex data from the database through queries – Expert in analytics tools and languages to present actionable insights  – Coordinate and work with the business stakeholders – Provide assistance to them in technical issues relating to data	– Comprehending business objectives and building models to realize them – Create a matrix to measure the model’s progress – Analysis of ML algorithms to select the best suited as per the problem at hand – Ranking algorithms based on their success probability – Identifying data pipelines
Salary	Average Base Pay ₹500,000 p.a. Source: Glassdoor (Updated 19th July 2019)	Average Base Pay ₹1,025,000 p.a. Source: Glassdoor (Updated 19th July 2019)	Average Base Pay ₹751,469 p.a. Source: Glassdoor (Updated 19th July 2019)
Application Areas	– Healthcare – Travel – Energy management – Gaming	– Recommender system – Digital advertisement – Image recognition – Speech recognition – Internet research	– Speech recognition – Medical diagnosis – Learning association – Prediction

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

These three companions are just like three different departments working in the same organization. Data Analysts, Data Scientists and Machine Learning Engineers work for the same objective – derive useful insights from data, realize business objectives and build such machines that can not only think on their own but also work with minimal human instructions.

I hope you are now clear of these three roles. If you have any questions, feel free to write to us in the comments section below.

All the best!