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Data Scientist vs Data Analyst vs Data Engineer - Role, Skills, Salary, Demand | Intellipaat - YouTube
Channel: Intellipaat
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It is no more a secret today that the key to a successful business is a data driven decision making.
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Data lies at the heart of the decision-making
process of all the organizations today and
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that has prompted the evolution of data-based
job roles in the industry like Data Analysts,
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Data engineer and Data scientists.
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Hey guys I'm Shubham from Intellipaat, and
I welcome you all to this video on the key
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differences between the top three Data based job roles such as Data Analysts, Data engineer &
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Data scientists.
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There is a large amount of vagueness when it comes to the use of these titles which creates a lot of
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confusions.
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Moving on with the video, let's clear out
all the confusions and find out how are these
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job titles different from each other.
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So, without any further ado, let's get started.
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Brief description of each Job role
Let's begin the session by first understanding
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who is a Data Analyst?
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A data analyst is the one that gathers, investigates and represents data in a way so that everyone
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can understand it.
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The Data that is gathered by Data Analysts usually comes from a single source.
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They are responsible for cleaning, Organizing and translating raw data into actionable business
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insights, which are further used by the organization to make data driven decisions.
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Data visualization is a vital part of their
professional day to day routine.
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Next is Data Engineer
Data engineers are the ones who are responsible
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for building and optimizing the systems that are needed by the data scientists and Data
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analysts to perform their tasks.
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They construct Data pipelines for the organizations, meaning that they ensure that the data is
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accessible to anyone who needs to work on it.
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Along with that, the primary responsibilities
of Data engineer include, ensuring that the
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data is properly received, transformed and stored along with building infrastructure
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or framework necessary for data generation.
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Data engineers and data scientists work closely together, and as a result, many interchanges
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these two roles.
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Data engineers report to data scientists with "big data" that they prepare in order
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to be analyzed by the scientist.
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Coming to the next Job title, that is Data
Scientist.
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So, a data scientist is a professional who
analyses the data strictly from a business
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point of view and is responsible for delivering
the predictions that aid in business value.
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They deal with both structured and unstructured
data.
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The job of Data scientists doesn't end there,
they are also expected to identify the right
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arenas of data from where they can find relevant
patterns so as to help in case any business-related
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problem arises.
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They extensively use machine learning for
their prediction purposes, so training and
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optimizing data models is a vital part of
their professional day to day routine.
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Although Data Scientist can perform most of
the tasks that Data Analysts perform, but
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data scientists are different in terms of
source of the data that they work on, that
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is, the data may come from multiple and disconnected
sources.
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They are also more adept to making better
business judgments.
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Job Roles and Responsibilities
The Job roles and responsibilities of a Data
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analyst lies around
>Collecting and Interpreting the data from
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the source, analyzing the results using statistical
techniques.
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> Acquiring data from primary or secondary
data sources and maintain databases/data systems
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> Data Mining - Where they have to structure
the raw data through various pattern or mathematical
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or computational algorithms.
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Also, to extract data from a company or external
database to perform any type of research.
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> Identifying patterns and trends in data
sets
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> create data dashboards, graphs and visualizations,
then provide sector and competitor benchmarking.
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Next, the job roles of a Data Engineer
So, their daily job roles include tasks like:
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> To develop, construct, test and maintaining
the architectures, like large-scale processing
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systems and databases.
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Their Architecture is what makes sure that business needs are being fulfilled.
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�>To provide and implement the ways to improve the reliability, efficiency and quality of
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the data.
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> To build the data pipelines
> Creating and Integrating APIs
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>To develop the data set processes for
data modelling, mining and production.
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Next is the job role of a data scientist
>Their main job role revolves around selecting
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features, building, and optimizing classifiers by using the machine learning techniques.
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> Performing data mining and analyzing by using the latest techniques
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> To perform a proper data analysis by processing,
sorting and data integration
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> Developing data algorithms and models
best suited for a particular business need.
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> Performing the predictive analysis by
using the concepts of machine learning and
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predictive algorithms
Skillset and the Educational Qualification
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Moving forward, now that we are clear as to
what these job roles actually mean, let's
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compare them on the basis of the skill set
and the educational qualification that you
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will need to start a career in these job roles.
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Going with the same order as before, let's
start off by discussing the skillset and educational
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background needed for Data Analysts.
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Basic programming knowledge in languages such as R, Python, SAS etc. is recommended here.
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SQL/ Data base knowledge, and the knowledge
in any data visualization tools such as Tableau,
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Qlikview and PowerBI would be an added advantage for you.
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A Bachelor's degree in computer science, math, statistics, information management or economics
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would be enough for you to start your career as a Data Analyst.
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Now, for Data Engineers.
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Major skills measured for this profile, like
experience in Hadoop, MapReduce, Pig, Hive
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programming, Data Streaming.
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Since they are architect and caretaker, their role mainly concentrates on database systems,
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with an exhaustive knowledge in SQL and NoSQL database.
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The knowledge in both of these technologies is essential if you want to expand your career
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horizon over the data engineering domain.
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Bachelor's degree in computer science, software engineering, applied math or statistics.
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Master's degree is not at all mandatory,
but serves as an added advantage.
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Alright!
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Now we have Data Scientist, the job of a data scientist requires both strong business acumen
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and advanced data visualization competencies.
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Their conclusions must narrate a clear and compelling story to serve business needs.
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For that, proficiency in any programming languages such as Python, R, Java, C/C++ or SAS are
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must.
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Also, you must be acquainted with the skill sets in latest technologies such as Big data Hadoop,
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machine learning or deep learning.
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And as far as Education qualification is concerned, a bachelor's degree in computer science or
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software engineering, math, or statistics
is preferred.
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However, Master's degree would come as an added advantage for you because if we look
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into current scenarios, half of all the data
scientists hold PHD's.
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And if we talk about the type of COMPANIES HIRING for these positions, well
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Since in IT Industries everything is about
data, there is always a need for each of these
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roles.
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So, more than 100+ MNC's and startups are actively hiring for the job roles of data
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analyst, data engineer and data scientist
in order to solve the data-driven problems
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and making the decisions based on the analytics.
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I am listing down some of the major companies
like:
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> Google
> Facebook
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> IBM
> Amazon
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> Accenture
> Intel
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> Walmart
> Oracle
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> Apple
> Spotify
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> Adobe
> Microsoft and the list goes on
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And now let's discuss the SALARY offered
for each of these roles.
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According to indeed, the average salary for a data analyst ranges around - 65K dollars per
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annum and an experienced data analyst can earn up to 107K dollars per annum.
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For Data engineers, on an average, they grab around 80K dollars and an experienced data
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engineer can earn up to 170K dollars per annum.
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And despite a recent influx of early-career
professionals, the median starting salary
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for a data scientist remains high at $95,000.
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The median salary for experienced data science professionals is $165,000, while the median
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salary for experienced manager-level professionals
is considerably higher at $250,000.
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Isn't that interesting?
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Now, if this has convinced you enough, I'd
suggest you should go for Intellipaat's
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Data Science Architect Master's Course which is in collaboration with IBM, this course
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is curated by the data science experts which covers 12 courses and consisting of 6 Instructor-led
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trainings in data science with R, Python for Data science, apache spark and scala, AI & Deep
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learning, Tableau Desktop 10, Data Science with SAS and 6 self-paced courses in Statistics
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& Probability, Advanced excel, MongoDB, MS-SQL, Machine learning , Hadoop Developer , you
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will also work on 48 industry-based projects with 1 Capstone Project.
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And guys that is not it, by analyzing the
current market scenarios and seeing the exhaustive
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job descriptions, we have also come up with additional 2 courses co-created with IBM named
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as Deep learning with tensorflow, build chatbots with watson assistant, which will help you
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in boosting your skillsets in your resume
and also, you will get an exclusive access
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to IBM watson cloud lab for the Chatbots course.
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Upon the completion of your training, you
will have quizzes that will help you prepare
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for the above-mentioned certification exams and score the top marks.
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And last but not the least, upon the completion of this course and on successfully completing
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the project work and after reviewed by experts, you will be rewarded with a Data Scientist
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Certificate provided by IBM.
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And this certificate will be recognized globally and amongst major MNCs like Cisco, Cognizant,
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Mu Sigma, IBM, TCS, Ericsson, Genpact and many more.
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So guys, that's all for today, i hope now you understand how these jobs roles are different from each other
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so the link for the above mentioned course is
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in the description box.
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Thank you so much for giving us your precious time!
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See you again!
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