Analytics Education — A Primer & Learning Path

A PRIMER & LEARNING PATH
ANALYTICS
EDUCATION
A REPORT BY
ANALYTICS INDIA MAGAZINE

ANALYTICS EDUCATION | A PRIMER & LEARNING PATH
REPORT BY AIMREPORT BY AIM
PAGE 2 PAGE 3
Executive Summary
01
CONTENTS
TABLE OF
04 20
06 22
10 24
12
14
26
18
Career Tracks
07
Different Models Of
Analytics Education
Providers In India
02
Cost benefits of
E-learning
08
How to choose the
right model
03
Networking
Opportunity
09
How to choose the
right course
04
Pre-requisites in
terms of skills
and tools
05
Continued Learning
10
Learning Path
06

PAGE 4 PAGE 5
SUMMARY
EXECUTIVE Fostering Close Partnership With
Corporate/Industry Players
Well-Developed Digital
Learning Environment
Winning Pedagogy
Innovative Learning
Tools
Keeping A Pulse On
Analytics Market
Tie-Ups With Highly Ranked Foreign
Universities
Some Of The
Defining
Characteristics
And Strengths Of
Institutions
There is a tremendous uptake for
data science education in India and
the preferred model of delivery is
blended or wholly online. For learn-
ers, who are looking to pivot to data
science, there is a trove of informa-
tion out there on how to get start-
ed. While there’s heaps of informa-
tion on data science for beginners,
what’s lacking is a primer on how
to choose the right institute that
could be crucial in your long terms
strategy of success. In this highly
competitive education market, it can
be hard to find a “career-focused”
learning model, featuring excellent
faculty and delivering the right ac-
creditation—the most valuable as-
set for potential students.
Through this primer, Analytics In-
dia Magazine explains which learn-
ing model is best suited for you, the
learning path and how to choose
the right education partner. In the
analytics education market, online
courses serve non-traditional stu-
dents, working professionals and
graduates in need of continuous
learning. There is a growing number
of high-profile institutions and niche
institutions that provide specialized,
career-advancing education.

PAGE 6 PAGE 7
DIFFERENT MODELS OF
ANALYTICS EDUCATION
PROVIDERS IN INDIA
Thenatureofanalyticseducationhas
drastically evolved over the years
and a mix of models have emerged
in the online space to accommo-
date the changing requirements of
learners. Our survey revealed re-
spondents seek a career-focused
analytics education augmented by
classroom setting that prepares
them for job functions in data ana-
lytics space. In cases where learn-
ing is delivered purely online, par-
ticipants look for real-time learning
in a format that allows learners to
pursue it at their own pace. Candi-
dates look for course content creat-
ed by top instructors, with industry
and university collaboration to pro-
vide a well-rounded analytics edu-
cation. Executive courses also make
for a high demand as these are in-
tended for senior professionals who
want to renew their skillset and un-
derstand how data can be helpful
in managerial decision making. In
case of executive analytics courses,
technical skills such as data man-
agement are augmented by soft
skills such as business understand-
ing and communication.
Analytics education providers in
India mostly offer Business Analyt-
ics (BA) and Business Intelligence
(BI) programs that combine analyt-
ical number crunching, reporting
and visualization techniques.

PAGE 8 PAGE 9
BROADLY SPEAKING, COURSES FALL INTO THE FOLLOWING BUCKETS
FULLY ONLINE COURSES BLENDED COURSES HYBRID COURSES
SELF PACED LEARNING
INSTRUCTOR LED ONLINE
INSTRUCTOR LED ONLINE + WEEKEND CLASSES
SELF-PACED + WEEKEND CLASSES
HYBRID - SELF PACED + INSTRUCTOR LED ONLINE
T h e s e c a n b e f u r t h e r c a t e g o r i z e d i n t o
The self-paced, fully online courses are instructor-led
courses that offer an excellent, personalized learning
experience and are targeted at working professionals
who want to transition to this lucrative field. Online
courses offer basic communication forums such as Q &
A forums, chat-rooms where students can interact and
network. These programs meet the rising demand for
data analysts and data scientists.
Mostly short-term, online programs give working stu-
dents a chance to upskill and an opportunity to be men-
tored by industry veterans. While they may fall short
on personalization and student-centricity, the upside
is they are cost effective and offer a way to upskill in
a short period of time. A lot depends on the learners’
ingenuity as well to imbibe skills quickly in a remote,
online environment. Geared at early, mid and senior
working professionals, the flexibility of online courses
have made them popular academic choice among this
segment.
Personalized instruction is one of the key features of
blended learning model that combines the best of class-
room rigor and convenience of an online learning. An
extremely popular learning model, blended learning
offers on-site support to students and even features in-
dustry meet-ups. Blended courses offer a guided, self-
paced learning environment with small class sizes. This
type of instructor based, online mentoring is best suited
for data science field that requires concept-based train-
ing.
Executive online courses usually fall in the blended cat-
egory. Executive courses are intended for professionals
who want to renew their skillset and understand how
data can be helpful in managerial decision making. Rec-
ognizing this growing need for business-focused, da-
ta-oriented analytics programs, private analytics train-
ing institutes or data science training institutes in India
have developed cutting-edge programs in close part-
nership with industry leaders such as IBM, Genpact,
Tech Mahindra, SAS among others.
While there is a thin line between hybrid and blended
courses, hybrid courses combine classroom learning
and face time with online content and project work.
This format allows for more flexibility and results in
greater student engagement. Hybrid experiences allow
students to balance their work-life effectively and the
online component effectively supplement traditional
classroom learning. What sets apart hybrid from blend-
ed learning format is that in the former, a significant
amount of learning is moved to online and classroom
time is reduced.

PAGE 10 PAGE 11
HOW TO CHOOSE
THE RIGHT LEARNING MODEL
1
3
5
4
2
Identify the skill gap & your future
role, based on that, choose the right
model. One of the key ideas is to
identify where you are at your learn-
ing journey. If you are just starting
out on the data science career track,
you will need more than just a bun-
dled knowledge set to make a tran-
sition. If you are learning data sci-
ence with the intention to advance
your career, you would ideally look
for a Learning Model that offers
personalized mentorship by indus-
try experts, coupled with capstone
projects and career advice such as
mock interviews and job placement
opportunities.
DEFINE YOUR GOALS, BOTH ACADEMICALLY AND PROFESSIONALLY
BEFORE DIVING INTO A LEARNING MODEL AND ASK YOURSELF THESE
QUESTIONS:
Is the objective to gain a deep understanding of big data tools and
technologies? Can you set aside six-eight hours per week to build
your knowledge. In this case, an exhaustive PG program would
be better than a short-term tool based big data program.
Looking to build a foundation base in data science and maximize your
skillset? Do you also plan to go for interviews – then blended
learning model is your answer. Plus, classroom sessions would also
help in networking opportunities.
Do you want to build your career on the business analytics side
or big data technology side. Choose the program that fits your
needs and requirements effectively.
Before zeroing in on a particular model, you should ideally grade
them on various parameters – content style, depth of subject covered,
instructor profile, relative certificate weightage given by prospective
employers and job prospects
Do you want to build your career on the business analytics side
or big data technology side. Choose the program that fits your
needs and requirements effectively.

PAGE 12 PAGE 13
HOW TO CHOOSE
THE RIGHT COURSE
NOW THAT YOU HAVE DECIDED TO GO FOR A PARTICULAR MODEL
HERE ARE THE FACTORS TO CONSIDER
BRAND VALUE SKILLS & TOOLS
COVERED
RESOURCES, ACCESS
TO DATA SETS
ALUMNI SUCCESS FACULTY
Besidesthesekeyfactors,thecourse
has to be worth the investment and
offer placement opportunity. Un-
derstand the length and breadth of
the course material as the more in-
depth the course, the better. Proj-
ect-based courses can offer more
data solutions examples to add to
your portfolio and the hands-on an-
alytics experience can help you land
a plum job. Also, find out what kind
of theoretical background special-
ization is required before taking up
a particular course. Online instruc-
tor-led courses and weekend cours-
es can help you get more feedback
from faculty, career advice and en-
sure you stay right on the track.
Make sure you do more diligence on
the ROI of these courses by tapping
into alumni network and find out
more about the quality of diploma/
certificate offered.]1[ ]2[ ]3[
]4[ ]5[
Place more emphasis on renowned
institutions that can offer better
career opportunities.
Data science is a diverse field so look
out for a program that covers all the
necessary pipeline effectively. If you
are focused on specific topics, such
as deep learning, you can take it up
as a side course. Even though tools
are important, you should also base
the decision on the core data science
skills the program
imparts.
Seek out a project-oriented,
practical portfolio building program
that will empower you with both
academic and technical expertise.
Real-life projects can boost
employability chances as well.
Your course has to give you back a
sound ROI, if not then it’s not worth
it. So, find out about the placement
prospects and career coaching
advice that can lead to future jobs.
World-renowned faculty backed
by industry experts also offers the
opportunity to maximize contacts.

PAGE 14 PAGE 15
PRE-REQUISITES
IN TERMS OF SKILLS & TOOLS
At first, the data science field can
seem a bit overwhelming but it
doesn’t have to be if you chalk out
a clear learning journey. Before
you sign up, understand the kind
of skills required, based on your
years of experience, background &
domain experience to transition into
the field effectively. Find out the
skills you want to learn (deep learn-
ing, advanced machine learning
techniques, R programming). Based
on the skill-set, you can sign up the
course that best covers it and make
sure you put these skills to work
with lots of project work, because
eventually analytics has to meet
business goals and work on the ROI.
Learners hailing from an IT and pro-
gramming background find it easy
to pick up other programming lan-
guages than non-IT learners, and
therefore it would be easier to pick
up technological concepts if you
have a decent knowledge of coding,
statistics and business acumen. Be-
fore you deep dive into the program,
here are the three essential things
you must have, before signing up
for a course.
TECHNOLOGY BUSINESS
STATISTICS

PAGE 16 PAGE 17
TECHNOLOGY
STATISTICS
Diving into a space which is highly technical, it is important
to have the basic understanding, if not advanced, of what the
subject is about. Some of the technicalities are an understand-
ing of what are coding languages and how do they work. It is
important to have an idea of what are open sources, functions,
object oriented programming etc. Data and databases are other
areas that a candidate should be well versed with such as with
RDBMS, Structures and SQL. Another must-have is a deep
knowledge of Excel. Advanced Excel is still used as a major
analytics tool in many companies.
A statistics refresher is a must for those candidates who are
aiming to get into the field of analytics and data science. Hav-
ing a basic understanding of statistics will add an edge and
ease out the overall learning process. It is good to have famil-
iarity with concepts such as distribution, statistical tests and
maximum likelihood estimators. In fact, questions related to
multivariable calculus and linear algebra are frequently asked
at interviews since they form a basis of lot of machine learning
techniques.
DISTRIBUTION,
CALCULUS,
LINEAR ALGEBRA,
STATISTICAL TEST
SQL,
RDBMS,
DATABASES,
EXCEL,
OPEN SOURCE,
FUNCTIONS
BUSINESS
Another important part of the pre-requisites is the business
acumen, which might come with the growing experience in the
field.
BUSINESS
ACUMEN
IN A NUTSHELL, THINGS THAT SHOULD BE A PART OF DATA ANALYST’S
TOOLKIT ARE:
• Understanding of databases, RDBMS, structures, Excel, func-
tions, statistics etc.
• Big data tools such as BigSQL, Hive, SQL, Pig etc.
• Visualisation tools such as Qlik, Tableau, Power BI etc.
• Once you have mastered the basics, you can also learn Hadoop
along with others such as SAS and MATLAB

PAGE 18 PAGE 19
There are a slew of
online courses that can
get you started on the
fundamental concepts.
LEARNING
PATH
On the basis of
experience, chalk out
a learning path
BEGINNERS
ONCE YOU ARE
DONE WITH
STRENGTHENING
THE BASICS,
YOU CAN FOCUS
DEEPER INTO
ADVANCED MACHINE
TECHNIQUES
While picking up a course in data science and analytics, there may be professionals
both from IT and non IT (such as MBA) field. While the one with IT background may
have a detailed knowledge of coding and statistics, a non IT guy may be bereft of these
concepts. Before taking a plunge into this field, it is important to analyse your under-
standing in the subject based on your experience and follow a learning path best suited
to you. The learning path and job roles may be different based on your knowledge in the
subject. It can be categorized as follows:
1.
We would classify beginners as
learners who come with little or no
background in technology. Ones with
very basic knowledge of how the
tools work, fall under this category.
JOBS SUITED
FOR BEGINNERS
While this cohort may lack
in experience and domain
knowledge, they can take
up jobes as
• Business analysts
• BI analyst
• SAS Analyst
• SAS Programmer, etc.
For this cohort, an
introductory course in
a particular language,
understanding of open
source, databases etc.
may help.
Learn languages like
Python and R and visu-
alization tools lsuch as
tableau, d3.js
INTERMEDIATE
Those with a little more
understanding of advanced
tools in analytics and data
science, than beginners
but lack substantially to
the level of experts are
intermediate learners
They may have
good programming
skills, statistical
background,
understanding
of big data
technologies
and form a large
chunk of analytics
professionals
ADVANCED These might have already
finished the advanced
learning in areas like
machine learning
techniques, NLP and are
modelling specialist
They have enough
domain experience.
Some of the key skills
are communicating
effectively with different
stakeholders and
developing deep subject
matter knowledge
2.
3. JOBS SUITED FOR
ADVANCED LEARNERS
Data scientist is one of the
job roles under advanced
level, which demands pro-
fessionals to have a strong
business acumen and define
business use cases. They
may work with big teams to
produce data driven prod-
ucts. Other roles are
• Senior Data Analyst in
areas like data mining
and modelling.
Learners who have dabbled in
data analytics roles and can
pick up specific topics such as
natural language processing and
computer vision, are advanced
learners
JOBS SUITED FOR
INTERMEDIATES
Usually pivot to data engi-
neering and data manage-
ment roles wherein they
manage data and infrastruc-
ture. Data engineers are sup-
posed to maintain database
architecture and build data
pipelines for data scientists
to pull relevant information
from. Some of the other job
roles are
• Data analyst,
• Statistical analyst,
• Analytics manager etc.

PAGE 20 PAGE 21
CAREER
TRACKS
TECHNOLOGY TRACK BUSINESS TRACK
Once you are well versed with the concepts, there are various professional
possibilities that a data science and analytics professional can explore. Based
on your expertise, you can dwell into any of these career tracks:
Good understanding of the technological as-
pect may help you craft a career in this track.
If you are someone with a good programming
knowledge or coding and loves playing with
complex data, then you can follow the tech-
nology track and dabble in areas like data
wrangling or data warehouse architect.
• DATA WAREHOUSE ARCHITECT
• DATA WRANGLING
• DATA MANAGEMENT ROLES
• SYSTEM ANALYST
Skills needed are programming tools,
Hadoop, Spark, Pig, Hive, and databases.
Have enough business expertise and a
business acumen? If you come from an MBA
background with little analytics experience
but with strong sense of conveying message,
then you are geared for a role in business
analytics. Apart from working on data that
has been put together by the data scientist,
you should have a fair idea of how business-
es work. Additionally, BAs also visualize the
insights to tell a compelling story to execu-
tives through dashboards.
• BUSINESS ANALYST
• MANAGEMENT ANALYST
• FINANCIAL ANALYST
• VISUALIZATION EXPERT
Skills needed for this role are SQL, ETL
skills, advanced data visualizations such as
tableau.
SKILLSJOBROLESDESCRIPTION
REPORTING TRACK
The world of data-driven reporting has
emerged as a lucrative option for story-tellers
who want to employ data visualization skills to
tell compelling stories. Internationally, storied
media houses such as NYT and a few back
home have set up dedicated departments to
deal with the data deluge. Increasingly, mar-
keters are also turning to big data to better
understand how to use historical data to im-
prove promotional activities and gain a better
understanding how marketing campaigns are
received on social media.
This track is geared for marketers and story-
tellers who have baseline skills such as website
scraping and know how to visualize dataset.
• DATA JOURNALISM
• DATA STORYTELLING
MODELLING TRACK
• DATA SCIENTIST
• ADVANCED ANALYTICS ROLES
This applies best to professionals who have
an advanced knowledge of the industry and
the tools used in the industry. With a deep
experience in programming, statistics and
the ability to deal with complex data are
some of the things that may lead you have a
career in modelling track. It may essentially
require years of experience.
Skills required may be an expertise in areas
such as machine learning, NLP, analytics,
amongst others.

PAGE 22 PAGE 23
COST BENEFIT
OF E-LEARNING
In today’s day and age, there are
many benefits of e-learning and the
chief technical benefit is the ease
and access of pursuing learning,
with the flexibility to learn a skill
anytime, anywhere. There is also
the added benefit of reduced
travel cost and cost of other
overheads. Plus, learners pursuing
Diploma/Certificate courses can
do it at their own convenience
without quitting the day jobs.
E-learning has become invaluable
since it helps in creating a more
competitive workforce by providing
sophisticated personalized learning
in a consistent manner
Ease of
Flexibility
Access to
excellent faculty
and in case of
international
university tie-
up, to top-notch
curriculum
In online
training, it is
easier to update
the course and
keep pace with
industry
demands
Consistent
delivery framework
ensures learners
don’t trail off the
learning path
leading to reduced
dropout rate
HERE ARE SOME OF THE KEY BENEFITS OF E-LEARNING Reduced
overheads
and travel
costs
Personalized
training in
small cohorts
leads and
individualized
study plans
There is a
better ROI on
e-learning rather
than traditional
classroom
learning

PAGE 24 PAGE 25
NETWORKING
OPPORTUNITY
Regardless of what learning
model you choose, recruiters and
employers look for candidates
who come with an understanding
of business context and have
a real understanding of data
science. And increasingly,
the market for data science
has heated up with intense
competition in the field. Needless
to say, even the best degree from
a reputed college can fail to land
a job, unless a candidate learns
to communicate their findings and
business objectives effectively
Here’s why networking is
important in this scenario
-- learners are working
professionals and it can be hard
to build relationships in an online
environment
Most programs offer MeetUps that serve as a great networking
opportunity and even inform candidates how to get hired
The class profile also serves as a good area to build
professional relationships
Interactive sessions with industry experts and partners allow to
build professional relationships

PAGE 26 PAGE 27
CONTINUED
LEARNING
Some of the ways to stay updated in this space and make continuous
learning a part of life are listed as below
• Even more important than learning a specific skill is to develop a framework to
constantly learn (formally or informally) a completely new area every couple of
years
• Inculcate the ability to pick up a new skill quickly and methodically
• Contribute to a community to develop reputational capital
• Work on an open source project you really enjoy
• Contribute to a community developed project
• Check out firsttimersonly.com to get started on a beginner friendly platform

#189, 1st Floor, 17th Main,
Sector 3, HSR Layout, Near
HSR Club, Bengaluru,
Karnataka 560102
info@analyticsindiamag.com
FOR MORE INFO
CONTACT;

Analytics Education — A Primer & Learning Path

More Related Content

What's hot (20)

Similar to Analytics Education — A Primer & Learning Path (20)

More from Analytics India Magazine (20)

Recently uploaded (20)

Analytics Education — A Primer & Learning Path