Category Archives for "Analytics Career"

Jan 22

What is Business Analytics: A Hiring Manager’s Perspective

By ganpati | Analytics Career

As per Wiki, The term Business Analytics is defined as “The skills, technologies, practices for continuous iterative exploration and investigation of past business performance to gain insight and drive business planning.”

Since this definition is a broad one, we looked into the job postings from a number of industries and analyzed over 100+ job postings from companies across the industries to shortlist some common attributes for a Business Analytics professional (which includes titles such as business analytics manager, business analytics consultant, Sr. Analyst, Business Analytics etc.). As per those, a business analytics professional should have following attributes:

Transform business questions into fact based analysis that delivers clear insights and actionable recommendations

This is one of the most important attributes of a senior business analytics professional to serve as a bridge between business and analytics department. Business managers face many problems that may have a solution hidden in data. A business analytics professional should be able to understand those problem and

Recommend appropriate performance measures to be produced including lifts, efficiency, confidence intervals, and other statistical metrics.

Once the business quetions have been identified, the next job is to think of appropriate metrics that are to be tracked (like ROI for sales or attrition rate for HR) so that any change in performance can be measured and come up with suggestions on how much improvement can be achieved in a certain time horizon based on historic data on industry data.

Analyze and process data, build and maintain models and report templates, and develop dynamic, data-driven solutions.

This is the most challenging and technical part of a analytics manager’s job. They are expected to create models based on data, that can justify taking certain measures to bring about a positive change. For example, in order to check the high attrition rates a manager may have to find ways to collect new data points like average compensation of people who are leaving the organization, their experience level and average stay in the company. Then they have to prove how some of these factors are contributing to the metrics being targeted (that is attrition). They may come up with suggestions on what measures may lead to a positive change based on industry data or external research reports.

Provide business clients with detailed, actionable reports documenting the findings from, data processing, and data analysis.

Different functions in an organization often track different metrics. The sales, marketing, operations and HR may each have their own set of objectives and consequently they have their own metrics to track. It’s therefore critical for the business analytics professionals to have a birds eye view of all these functions and cater to their business needs (which can at times be conflicting) in a balanced manner. In larger organizations there can be multiple analytics teams across the functions to crunch data for these functions. In that case it’s important for the Business Analytics manager to partner with cross functional analytics teams to develop well-rounded perspective and bring insights together to tell a cohesive story to the senior leadership and drive strategic decision.

Consult on using business intelligence data for predictive analytics and facilitate implementation of new tools and data marts.

Last but not the least, the job of a business analytics manager is to always look forward in terms of technology and changing business environment to ensure smarter decision making for the organization. They may often have to take cognizance of these changes surrounding them and consult the leadership about appropriate strategy for adopting new tools, data sources etc.

As we just observed, the duties of a business analytics professional may often transcend the functional or departmental boundaries. Sometimes junior level analytics professionals are also hired by the companies who mostly focus on data from a business group or function. Instead of looking at organization wide pictures they are often concerned about finding patterns in particular data sets and convey their analysis to the senior professionals. For example, an analyst with marketing department may have to analyze market and account planning data, market intelligence data, and draw the right insights and communicate it to the managers. They also need to work closely with business and function leaders to scope strategic focus, targets, and metrics for the group or unit that they focus on.

Jan 14

Exploring the Job Roles in Marketing Analytics

By ganpati | Analytics Career

The most important aspect of a marketing analytics job is to understand how business leaders consume and use marketing data to drive decisions and strategies.

Develop the firms marketing analytics strategy, oversee the technical implementation of related systems that assess programs and deliver actionable insights to drive improved marketing and business performance, and identify strategic opportunities.

Lead the definition and implementation of integrated measurement framework and KPIs for consistency in measuring and reporting marketing performance

Extract, analyze, and synthesize qualitative and quantitative data from a variety of sources and across multiple dimensions, including campaigns, channels, audience segments, and timeframes to measure and report on content performance, audience behavior, and effectiveness of marketing efforts.

Direct and manage analytics project lifecycles, including defining deliverables based on requirements, defining KPIs, coordinating data retrieval and aggregation from multiple sources, and presenting insights in a digestible and actionable format that will drive change, new initiatives, and decision-making.

Analyze brand, reputation, marketing, and communications activities to synthesize data, identify trends, develop predictive findings, and evaluate impact on goals, including sales, recruitment, and reputation to develop strategic insights to inform the development of internal and external communications.

Lead the development of dynamic reporting dashboards that integrate data from different measurement tools to provide business partners with relevant, easy to access and easy to understand information.

Aug 20

10 Steps to a Successful Big Data Career

By ganpati | Analytics Career

Source: Bigstockphotos

Source: Bigstockphotos

What all do you need to master Big Data Analytics. Here are 10 steps that will catapult you to success in this field:

1) Learn about matrix factorizations

Take the Computational Linear Algebra course (it is sometimes called Applied Linear Algebra or Matrix Computations or Numerical Analysis or Matrix Analysis and it can be either CS or Applied Math course). Matrix decomposition algorithms are fundamental to many data mining applications and are usually underrepresented in a standard “machine learning” curriculum. With TBs of data traditional tools such as Matlab become not suitable for the job, you cannot just run eig() on Big Data. Distributed matrix computation packages such as those included in Apache Mahout [1] are trying to fill this void but you need to understand how the numeric algorithms/LAPACK/BLAS routines [2][3][4][5] work in order to use them properly, adjust for special cases, build your own and scale them up to terabytes of data on a cluster of commodity machines.[6] Usually numerics courses are built upon undergraduate algebra and calculus so you should be good with prerequisites. I’d recommend these resources for self study/reference material:
See Jack Dongarra

2) Learn about distributed computing

It is important to learn how to work with a Linux cluster and how to design scalable distributed algorithms if you want to work with big data

Crays and Connection Machines of the past can now be replaced with farms of cheap cloud instances, the computing costs dropped to less than $1.80/GFlop in 2011 vs $15M in 1984: http://en.wikipedia.org/wiki/FLOPS .
If you want to squeeze the most out of your (rented) hardware it is also becoming increasingly important to be able to utilize the full power of multicore (see Moore’s law)
Note: this topic is not part of a standard Machine Learning track but you can probably find courses such as Distributed Systems or Parallel Programming in your CS/EE catalog. See distributed computing resources, a systems course at UIUC, key works, and for starters: Introduction to Computer Networking.

After studying the basics of networking and distributed systems, I’d focus on distributed databases, which will soon become ubiquitous with the data deluge and hitting the limits of vertical scaling. See key works, research trends and for starters: Introduction to relational databases and Introduction to distributed databases (HBase in Action).

3) Learn about statistical analysis

Start learning statistics by coding with R: What are essential references for R? and experiment with real-world data: Where can I find large datasets open to the public?

Cosma Shalizi compiled some great materials on computational statistics, check out his lecture slides, and also What are some good resources for learning about statistical analysis?
I’ve found that learning statistics in a particular domain (e.g. Natural Language Processing) is much more enjoyable than taking Stats 101. My personal recommendation is the course by Michael Collins at Columbia (also available on Coursera).

You can also choose a field where the use of quantitative statistics and causality principles [7] is inevitable, say molecular biology [8], or a fun sub-field such as cancer research [9], or even narrower domain, e.g. genetic analysis of tumor angiogenesis [10] and try answering important questions in that particular field, learning what you need in the process.

4) Learn about optimization

This subject is essentially prerequisite to understanding many Machine Learning and Signal Processing algorithms, besides being important in its own right.
Start with Stephen P. Boyd’s video lectures and also What are some good resources to learn about optimization?

5) Learn about machine learning

Before you get to think about algorithms look carefully at the data and select features that help you filter signal from noise. See this talk by Jeremy Howard : At Kaggle, It’s a Disadvantage To Know Too Much
Also see How do I learn machine learning? and What are some introductory resources for learning about large scale machine learning? Why?
Statistics vs. machine learning, fight!
You can structure your study program according to online course catalogs
and curricula of MIT, Stanford or other top schools. Experiment with
data a lot, hack some code, ask questions, talk to good people, set up a web crawler in your garage: The Anatomy of a Search Engine
You can join one of these startups and learn by doing: What startups are hiring engineers with strengths in machine learning/NLP?
The alternative (and rather expensive) option is to enroll in a CS
program/Machine Learning track if you prefer studying in a formal
setting. See: What makes a Master’s in Computer Science (MS CS) degree worth it and why?
Try to avoid overspecialization. The breadth-first approach often works best when learning a new field and dealing with hard problems, see the Second voyage of HMS Beagle on the adventures of an ingenious young data miner.

6) Learn about information retrieval

Machine learning Is not as cool as it sounds
What are some good resources to get started with Information Retrieval? Why?

7) Learn about signal detection and estimation

This is a classic topic and “data science” par excellence in my opinion.
Some of these methods were used to guide the Apollo mission or detect
enemy submarines and are still in active use in many fields. This is
often part of the EE curriculum.
Good references are Robert F. Stengel’ lecture slides on optimal control and estimation: Rob Stengel’s Home Page, Alan V. Oppenheim’s Signals and Systems. and What are some good resources for learning about signal estimation and detection? A good topic to focus on first is Kalman filter, widely used for Time series forecasting.

Talking about data, you probably want to know something about information: its transmission, compression and filtering signal from noise. The methods developed by communication engineers in the 60s (such as Viterbi decoder, now used in about a billion cellphones, or Gabor wavelet widely used in Iris recognition) are applicable to a surprising variety of data analysis tasks, from Statistical machine translation to understanding the organization and function of molecular networks. A good resource for starters is Information Theory and Reliable Communication: Robert G. Gallager: 9780471290483: Amazon.com: Books. Also What are some good resources for learning about information theory?

8) Master algorithms and data structures

What are the most learner-friendly resources for learning about algorithms?

9) Practice

Getting In Shape For The Sport Of Data Science
Carpentry
What are some good toy problems in data science?
Tools: What are some of the best data analysis tools?
Where can I find large datasets open to the public?

If you do decide to go for a Masters degree:

10) Study Engineering

I’d go for CS with a focus on either IR or Machine Learning or a combination of both and take some systems courses along the way. As a “data scientist” you will have to write a ton of code and probably develop distributed algorithms/systems to process massive amounts of data. MS in Statistics will teach you how to do modeling and regression analysis etc, not how to build systems, I think the latter is more urgently needed these days as the old tools become obsolete with the avalanche of data. There is a shortage of engineers who can build a data mining system from the ground up. You can pick up statistics from books and experiments with R (see item 3 above) or take some statistics classes as a part of your CS studies.

[1] http://mahout.apache.org/
[2] http://www.netlib.org/lapack/
[3] http://www.netlib.org/eispack/
[4] http://math.nist.gov/javanumeric…
[5] http://www.netlib.org/scalapack/
[6] http://labs.google.com/papers/ma…
[7] Amazon.com: Causality: Models, Reasoning and Inference (9780521895606): Judea Pearl: Books
[8] Introduction to Biology , MIT 7.012 video lectures
[9] Hanahan & Weinberg, The Hallmarks of Cancer, Next Generation: Page on Wisc
[10] The chaotic organization of tumor-associated vasculature, from The Biology of Cancer: Robert A. Weinberg: 9780815342205: Amazon.com: Books, p. 562

Author: Alex Kamil’s, See Alex Kamil’s original answer on Quora

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