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Data Scientist vs Data Analyst vs Data Engineer

Welcome you all to this article on the key differences between the top three Data based job roles such as Data Analysts, Data engineer & Data scientists. There is a large amount of vagueness when it comes to the use of these titles which creates a lot of confusions. Moving on with the article, let's clear out all the confusions and find out how are these job titles different from each other. 

So, without any further ado, let's get started. Brief description of each Job role Let's begin the session by first understanding who is a Data Analyst? A data analyst is the one that gathers, investigates and represents data in a way so that everyone can understand it. The Data that is gathered by Data Analysts usually comes from a single source. They are responsible for cleaning, Organizing and translating raw data into actionable business insights, which are further used by the organization to make data-driven decisions. Data visualization is a vital part of their professional day to day routine. Next is Data Engineer Data engineers are the ones who are responsible for building and optimizing the systems that are needed by the data scientists and data analysts to perform their tasks. 

They construct Data pipelines for the organizations, meaning that they ensure that the data is accessible to anyone who needs to work on it. Along with that, the primary responsibilities of Data engineer include, ensuring that the data is properly received, transformed and stored along with building infrastructure or framework necessary for data generation. Data engineers and data scientists work closely together, and as a result, many interchanges these two roles. Data engineers report to data scientists with "big data" that they prepare in order to be analyzed by the scientist. Coming to the next Job title, that is Data Scientist. So, a data scientist is a professional who analyses the data strictly from a business point of view and is responsible for delivering the predictions that aid in business value. They deal with both structured and unstructured data. 

The job of Data scientists doesn't end there, they are also expected to identify the right arenas of data from where they can find relevant patterns so as to help in case any business-related problem arises. They extensively use machine learning for their prediction purposes, so training and optimizing data models is a vital part of their professional day to day routine. Although Data Scientist can perform most of the tasks that Data Analysts perform, data scientists are different in terms of the source of the data that they work on, that is, the data may come from multiple and disconnected sources. They are also more adept to making better business judgments. 

Job Roles and Responsibilities The Job roles and responsibilities of a Data analyst lies around Collecting and Interpreting the data from the source, analyzing the results using statistical techniques. Acquiring data from primary or secondary data sources and maintain databases/data systems Data Mining - Where they have to structure the raw data through various pattern or mathematical or computational algorithms. Also, to extract data from a company or external database to perform any type of research. Identifying patterns and trends in data sets create data dashboards, graphs and visualizations, then provide sector and competitor benchmarking. Next, the job roles of a Data Engineer So, their daily job roles include tasks like To develop, construct, test and maintain the architectures, like large-scale processing systems and databases. Architecture is what makes sure that business needs are being fulfilled. 

To provide and implement the ways to improve the reliability, efficiency and quality of the data. To build the data pipelines Creating and Integrating APIs To develop the data set processes for data modelling, mining and production. Next is the job role of a data scientist Their main job role revolves around selecting features, building, and optimizing classifiers by using the machine learning techniques. Performing data mining and analyzing by using the latest techniques To perform a proper data analysis by processing, sorting and data integration Developing data algorithms and models best suited for a particular business need. Performing the predictive analysis by using the concepts of machine learning and predictive algorithms Skillset and the Educational Qualification Moving forward, now that we are clear as to what these job roles actually mean, let's compare them on the basis of the skill set and the educational qualification that you will need to start a career in these job roles.

Going with the same order as before, let's start off by discussing the skillset and educational background needed for Data Analysts. Basic programming knowledge in languages such as R, Python, SAS etc. is recommended here. SQL/ Database knowledge and the knowledge in any data visualization tools such as Tableau, Qlikview and PowerBI would be an added advantage for you. A Bachelor's degree in computer science, math, statistics, information management or economics would be enough for you to start your career as a Data Analyst. Now, for Data Engineers. Major skills measured for this profile, like experience in Hadoop, MapReduce, Pig, Hive programming, Data Streaming. Since they are architect and caretaker, their role mainly concentrates on database systems, with an exhaustive knowledge in SQL and NoSQL database. The knowledge in both of these technologies is essential if you want to expand your career horizon over the data engineering domain. 

Bachelor's degree in computer science, software engineering, applied math or statistics. Master's degree is not at all mandatory but serves as an added advantage. Alright! Now we have Data Scientist, the job of a data scientist requires both strong business acumen and advanced data visualization competencies. Their conclusions must narrate a clear and compelling story to serve business needs. For that, proficiency in any programming languages such as Python, R, Java, C/C++ or SAS is a must. Also, you must be acquainted with the skill sets in latest technologies such as Big data Hadoop, machine learning or deep learning. And as far as Education qualification is concerned, a bachelor's degree in computer science or software engineering, math, or statistics is preferred. However, Master's degree would come as an added advantage for you because if we look into current scenarios, half of all the data scientists hold PHD's. 

If we talk about the type of COMPANIES HIRING for these positions, well Since in IT Industries everything is about data, there is always a need for each of these roles. So, more than 100+ MNC's and startups are actively hiring for the job roles of data analyst, data engineer and data scientist in order to solve the data-driven problems and making the decisions based on the analytics. I am listing down some of the major companies like Google, Facebook, IBM, Amazon, Accenture,  Intel, Walmart, Oracle, Apple, Spotify, Adobe, Microsoft and the list goes on And now let's discuss the SALARY offered for each of these roles. According to indeed, the average salary for a data analyst ranges around - 65K dollars per annum and an experienced data analyst can earn up to 107K dollars per annum. Data engineers, on average, they grab around 80K dollars and an experienced data engineer can earn up to 170K dollars per annum. 

Despite a recent influx of early-career professionals, the median starting salary for a data scientist remains high at $95,000. The median salary for experienced data science professionals is $165,000, while the median salary for experienced manager-level professionals is considerably higher at $250,000. Isn't that interesting? Now, if this has convinced you enough, I'd suggest you should go for Intellipaat's Data Science Architect Master's Course which is in collaboration with IBM, this course is curated by the data science experts which covers 12 courses and consisting of 6 Instructor-led pieces of training in data science with R, Python for Data Science, apache-spark and scala, AI & Deep Learning, Tableau Desktop 10, Data Science with SAS and 6 self-paced courses in Statistics & Probability, Advanced excel, MongoDB, MS-SQL, Machine learning, Hadoop Developer, you will also work on 48 industry-based projects with 1 Capstone Project. 

Guys that are not it, by analyzing the current market scenarios and seeing the exhaustive job descriptions, we have also come up with additional 2 courses co-created with IBM named as Deep learning with TensorFlow, build chatbots with Watson assistant, which will help you in boosting your skillsets in your resume and also, you will get exclusive access to IBM Watson cloud lab for the Chatbots course. Upon the completion of your training, you will have quizzes that will help you prepare for the above-mentioned certification exams and score the top marks. And last but not the least, upon the completion of this course and on successfully completing the project work and after reviewed by experts, you will be rewarded with a Data Scientist Certificate provided by IBM. And this certificate will be recognized globally and amongst major MNCs like Cisco, Cognizant, Mu Sigma, IBM, TCS, Ericsson, Genpact and many more. So, guys, that's all for today, I hope now you understand how these jobs roles are different from each other.