Module Overview | Careers in Analytics In this module, we will evaluate the various quantitative data collection and analysis methods in standard industry practice. These methods are what will be used throughout this program, so you should become familiar with the terminology. The second part of this module presents a variety of career paths for data analysts and an overview of how several industries are currently using data analytics. Pay special attention to the intersection of skills necessary for a data analyst to possess, and think of the steps you can take to gain or improve on these in your own skill set. This may give you an idea of the career path and industry you would like to pursue, or enhance your understanding of a career path and industry you have already chosen. Industry Practice Learning Objectives Explain the technical elements and steps associated with analytics practices and processes Explore industry practice of data analytics Typical Quantitative Techniques Used in Advanced Analytics Several quantitative techniques apply to analytics projects, including: Type Description Simulation Randomized repetitions of a set of discrete events in order to model real-world systems and phenomena (e.g., queues) Optimization Algorithm selects the best possible outcome, subject to satisfying constraints Matrix Algebra Calculations involving matrices solve multidimensional problems Fitting Functions to Data Also called “curve fitting,” using numerical methods to interpolate data Survival Analysis Originally used by life scientists, but adopted by marketers and actuaries Time Series When data are “auto-correlated,” such as time-dependent data (also called “Box-Jenkins”) Predictive Analytics and Machine Learning Classical Statistics Descriptive: calculates metrics to characterize the distribution of values of data (mean, standard deviation, range, etc.) Predictive: estimates parameters using historical data and making predictions of future outcomes (multivariate regression, generalized linear regression, etc.) Learning Unsupervised learning: characterizes the data to establish classes without using explicit metrics, e.g., k-means clustering Supervised learning: Classify and describe the data with pre- defined ‘labels,’ e.g., decision trees Bayesian Used to augment classical analysis when there is prior knowledge about how the data was generated Typical Challenges and Pitfalls in an Analytics Project 1. Poorly defined problem • Unclear goal of problem-solving • Scope is unclear, e.g., how many SKUs to analyze • Mixed objectives, e.g., economic analysis of a product category promotion for retailer versus CPG mixed 2. Limited IT resources • Cloud data can’t be acquired off-line within a reasonable time • Can’t run the complete model due to computation limitation • Too slow to generate results in real time • Can’t share ...

1. Module Overview | Careers in
Analytics
In this module, we will evaluate the various quantitative data
collection and analysis methods in
standard industry practice. These methods are what will be used
throughout this program, so
you should become familiar with the terminology.
The second part of this module presents a variety of career
paths for data analysts and an
overview of how several industries are currently using data
analytics. Pay special attention to
the intersection of skills necessary for a data analyst to possess,
and think of the steps you can
take to gain or improve on these in your own skill set. This may
give you an idea of the career
path and industry you would like to pursue, or enhance your
understanding of a career path and
industry you have already chosen.

2. Industry Practice
Learning Objectives
Explain the technical elements and steps associated with
analytics practices and processes
Explore industry practice of data analytics
Typical Quantitative Techniques Used in Advanced Analytics
Several quantitative techniques apply to analytics projects,
including:
Type Description
Simulation
Randomized repetitions of a set of discrete events in order to
model real-world systems and phenomena (e.g., queues)
Optimization
Algorithm selects the best possible outcome, subject to
satisfying constraints
Matrix Algebra
Calculations involving matrices solve multidimensional
problems
Fitting Functions to Data
Also called “curve fitting,” using numerical methods to
interpolate data

3. Survival Analysis
Originally used by life scientists, but adopted by marketers and
actuaries
Time Series
When data are “auto-correlated,” such as time-dependent data
(also called “Box-Jenkins”)
Predictive Analytics and Machine Learning
Classical Statistics
Descriptive: calculates metrics to characterize the distribution
of
values of data (mean, standard deviation, range, etc.)
Predictive: estimates parameters using historical data and
making predictions of future outcomes (multivariate regression,
generalized linear regression, etc.)
Learning
Unsupervised learning: characterizes the data to establish
classes without using explicit metrics, e.g., k-means clustering
Supervised learning: Classify and describe the data with pre-
defined ‘labels,’ e.g., decision trees
Bayesian

4. Used to augment classical analysis when there is prior
knowledge about how the data was generated
Typical Challenges and Pitfalls in an Analytics Project
1. Poorly defined problem
• Unclear goal of problem-solving
• Scope is unclear, e.g., how many SKUs to analyze
• Mixed objectives, e.g., economic analysis of a product
category promotion for retailer versus
CPG mixed
2. Limited IT resources
• Cloud data can’t be acquired off-line within a reasonable time
• Can’t run the complete model due to computation limitation
• Too slow to generate results in real time
• Can’t share the data and results with network limitation
3. Less-best approach
• Selected less effective modeling method
• Incremental accuracy doesn’t offset the extra complexity
• Inadequate or incorrect performance monitoring criteria

5. 4. Incomplete or incorrect data
• Primary dataset unavailable
• Complementary data unavailable, e.g., missing competitor
pricing data
• Coarse data or aggregated data
• Very sparse data with missing values
5. Insufficient communication
• Insufficient data dashboard to communicate the analysis result
• Lack of soft skills to sell the results and insights
• Long feedback cycle to make the results less relevant
• Isolated org structure to stifle collaboration
Learn by Doing
Of the typical quantitative techniques used in advanced
analytics, the types used in machine
learning and predictive analytics include: (Select all that apply.)
historical data and making
predictions of future outcomes (multivariate regression,
generalized linear regression,

6. etc.)
outcome, subject to
satisfying constraints
with pre-defined ‘labels,’
e.g., decision trees
ics, which calculate metrics to characterize
the distribution of values of
data (mean, standard deviation, range, etc.)
Answers:
Supervised learning, which classifies and describes the data
with pre-defined
‘labels,’ e.g., decision trees; Descriptive statistics, which
calculate metrics to
characterize the distribution of values of data (mean, standard
deviation,
range, etc.)
Careers in Analytics

7. Learning Objectives
Explain the technical elements and steps associated with
analytics practices and processes
Connect the context of analytics to marketing, risk, financial,
etc., within a company and industry
Many skill sets come to play during the course of a data
analysis project workflow. These
include hacking skills, math and statistics knowledge, and
substantive expertise. Whereas
traditional research relies primarily on math/statistics and
domain expertise, modern data
science typically draws from all three sets. The hacking skills
reflect themselves in the
understanding of available tools and technologies.
Full Suite of Data Scientist Skill Set
Technical area Technology Academic discipline Domain
knowledge
Data Mining
Predictive Modeling

8. Machine Learning
NLP
Text Analytics
Data storage and
processing
Computing
environment
Computer
programming
Visualization
BI / reporting
Probability Theory
Statistics
Computer Science
Operations
Research
Economics
Healthcare

9. Retail marketing
Financial services
Manufacturing
Telecommunication
Rapid Growth Projected in Big Data Market
Wikibon projects the Big Data market will top $84B in 2026,
attaining a 17% Compound Annual
Growth Rate (CAGR) for the forecast period 2011 to 2026.
Line chart describing Wikibon Big Data Market Forecast 2011-
2026 ($US B)
The Big Data market reached $27.36B in 2014, up from $19.6B
in 2013. These and other
insights are from Wikibon’s excellent research of Big Data
market adoption and growth. The

10. graphic above provides an overview of their Big Data Market
Forecast.
Source: Executive Summary: Big Data Vendor Revenue and
Market Forecast, 2011-2026.
Note–Amazon’s annual revenue is about $100B
Significant Gap in Data Analytics Demand and Talent
The demand for deep analytical talent in the United States could
be 50 to 60 percent greater
than its projected supply by 2018.
The demand and supply are projected to be noticeably
mismatched in the 2018 forecast. This
would be a striking change from 2008 when 150,000 slots were
filled by ‘Data Analytics’
graduates with an exceeded supply of 30,000. In 2018, the
projected demand (est. over
440,000) is expected to exceed the projected supply (est.
300,000) by 140,000.
http://premium.wikibon.com/executive-summary-big-data-
vendor-revenue-and-market-forecast-2011-2026/
http://premium.wikibon.com/executive-summary-big-data-
vendor-revenue-and-market-forecast-2011-2026/

11. Learn by Doing
The main skill set for a Data Scientist consists of: (Check all
that apply.)
(e.g., Chemistry, Genetics, etc.)
etc.)
etc.)
Computing Environment, Data Storage and
Processing, etc.)
Correct Answers:
Technical Area; Technology; Domain Knowledge
Module Summary | Careers in Analytics
In this module, we presented a terminology overview of
quantitative data collection and analysis
methods that you will become more familiar with as you
progress through this program. These
methods are used in common practice throughout most

12. industries and are enhancing the data
collection and analysis trends of today.
We also discussed a variety of career paths for data scientists
and an overview of how several
industries are currently using data analytics. We learned about
the growing demand for data
analytics talent and the talent gap that exists in the field. To be
a part of that talent demand,
there is a vast skill set that data analysts need to possess, and
throughout this course, we will
examine each of them in more detail, while giving you the tools
you need to gain or enhance
these skills for your future career in data analytics.
Module Overview | Careers in AnalyticsIndustry
PracticeLearning ObjectivesTypical Quantitative Techniques
Used in Advanced AnalyticsTypical Challenges and Pitfalls in
an Analytics Project1. Poorly defined problem2. Limited IT
resources3. Less-best approach4. Incomplete or incorrect data5.
Insufficient communicationCareers in AnalyticsLearning
ObjectivesFull Suite of Data Scientist Skill SetRapid Growth
Projected in Big Data MarketSignificant Gap in Data Analytics
Demand and TalentModule Summary | Careers in Analytics
Careers within Data Analytics
Overview and Rationale
As you look to start your program and look towards your future

13. job prospects, it is important to have a clear picture of your
career prospects. In this assignment, you will research career
opportunities in analytics.
Course Outcomes
This assignment is directly linked to the following key learning
outcomes from the course syllabus:
· Describe the major steps of an analytics project, the various
job functions and who performs them with a focus on
identifying where they want to participate in the data analytics
ecosystem.
· Articulate the value data analytics provides to a business’
goals and strategies based with respect to the business’
industry.
· Visualize data in a compelling way to enable data driven
storytelling.
Assignment Summary
You are asked to create a small 5 minute presentations that
provides an overview of the industries recruiting Data Analytic
and Data Scientist professionals. Some suggested ideas for
information you may highlight in your presentation include:
· Number of corporate search postings on LinkedIn.
· Classify the corporate postings into industry buckets.
· Select 3 corporations in industries of interest to you and write
a brief synopsis about what you learn about those corporations.
· What would you expect as a starting salary.
· Identify the key skills ought by corporations offering careers
in DA
· Identify the growth in career opportunities over the next 5
years.
· How will you market yourself to potential corporations
offering careers in DA
The above is not a list of required data points, but suggestions.
You are expected to go beyond these and think of other data
points and use these data points to provide an analysis of the
role and future expectations.

14. This is another opportunity to dive in and showcase your ability
to present data, as well as showcase your presentation skills. Be
creative in your presentation. Be sure to reference the data
visualization best practices resources to create powerful visuals.
You should also structure your presentation to tell a story, and
annotate your data to highlight key points you wish to cover.
Submission Requirements
You must include data visualization along with your
presentation. The data must be equivalent to 4-5 slides of a
PowerPoint presentation.
Cite all sources using APA.
If you are giving your presentation live, (face-to-face or online)
allow 2 minutes for Q&A. Submit your presentation file for
grading.
If not, record yourself giving the presentation and post it to the
discussion board and answer classmate questions via the
discussion board. Submit both your recording and presentation
file for grading.
Rubric
Category
Meets Standards
Approaching Standards
Below Standards
Not Evident
Job Function
ALY6000-CO6
Articulate a well-focused and insightful overview of the job
function and its future using storytelling and visualization.
Articulates an overview of the job function and its future using
story and visualizations, but the story and visualizations do not
connect or the overview is generic
Outlines a function and future of the role, but does not do so
with a story and data or the overview does not clearly

15. communicate a specific purpose or future of the role
Does not identify the function or future of the role. Does not
connect the visualizations and data with the role or function
Business Value of Function
ALY6000-CO6
Articulates a clear current and future business value for the job
function using story and visualization.
Articulates a current business value for the role using story and
visualizations, but the visualizations and story do not connect or
are generic
Outlines a business value for the job function, but does not do
so with story and data or does not clearly communicate a
specific business value of the role
Does not identify a business value for the job function. Does
not connect the visualizations and data with the business value.
Data Visualizations
ALY6000-CO6
Accurately and creatively visualizes data in a useful and
compelling way that tells a story.
Visualizes data in a useful way but visualizations may not be
compelling, or valid.
Data visualizations are actuate, but not visually appealing and
hinder the story being told.
Data visualizations are inaccurate, not creative and
inappropriate for the story being told.
Research
Effectively incorporates sufficient scholarly resources that
reflect depth and breadth of research
Incorporates scholarly resources but does so ineffectively or the
resources do not reflect a depth and breadth of research
Incorporates insufficient scholarly resources to suggest a depth
of research. Use of resources is awkward.
Does not incorporate scholarly resources that reflect depth and
breadth of research
Writing and Format
Assignment work follows normal conventions of grammar and

16. spelling and has been carefully proofread. Appropriate
conventions of style and format are used consistently.
Minimal errors in spelling, and grammar, and/or other writing
conventions. Some transitions are choppy but not difficult to
follow.
Frequent errors in spelling, grammar, and/or other writing
conventions that distract the reader. Transitions are choppy and
difficult to follow. Limited connection to the topic.
Writing contains numerous errors in spelling, and grammar that
interfere with comprehension. The viewer is unable to
understand the intended meaning. Limited connection to the
topic.
Presentation
Narration or delivery is clear and concise with variation in
volume and inflection, holds attention, and emphasize key
points.
Narration or delivery is clear and concise, holds attention, and
emphasizes key points.
Narration or delivery is clear and concise, emphasizes key
points.
Narration or delivery is unclear, does not holds attention or
emphasize key points.