It is no more a secret today that the key to a successful business is a data driven decision making.
Data lies at the heart of the decision-making process of all the organizations today and
that has prompted the evolution of data-based job roles in the industry like Data Analysts,
Data engineer and Data scientists.
Hey guys I'm Shubham from Intellipaat, and I welcome you all to this video 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 video, 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, but
data scientists are different in terms of 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 maintaining the architectures, like large-scale processing
systems and databases.
Their 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/ Data base 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 are
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.
And 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.
For Data engineers, on an average, they grab around 80K dollars and an experienced data
engineer can earn up to 170K dollars per annum.
And 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
trainings 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.
And guys that is 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 an 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
so the link for the above mentioned course is
in the description box.
Thank you so much for giving us your precious time!
See you again!