3 Reasons to Pursue a Career in Data Science (includes High Pay)

Were you one of many that has been told by their parents in the past to take BS Nursing in college? This is just one example of how we were made to believe in what “has been” a booming and in demand job during those times. While it is somehow or may still be a booming career, I’m here to tell you that there will be another up and coming “hot” trend — Data Science.

With all the rising organizations nowadays it’s inevitable to use, analyze and visualize data – and WE must catch up. In a study by Mckinsey, they project that by 2018 demand for data scientists may be as much as greater than the supply (ain’t it a good news here?!). Here alone in the Philippines, much work is being outsourced from our country – we have call center agents, telemarketers, webmasters, programmers, social experts and many more.

I quote what Sun Tzu said, “Most battles are won before they are fought.” As a country, we must prepare for what is about to be the next BIG thing here while it isn’t yet that glitzy today.

So hop on and be convinced more that we need more data scientists here in the Philippines! You could be one, no joke.

 

1. You can start anywhere to be a data scientist.

The standardization of the definition of Data Science is still being debated around the world, which means that everyone has been defining Data Science at his own right. However, if we want to be consistent with most organizations that have tried to define it, a data scientist is a role doing a combination of statistics, mathematics and programming.

I’ve known marketing guys becoming data scientists by training themselves to learn how to code, how to analyze data and how to present business insights. They’re already proficient in story telling as what they’re paid to do and all they need is to learn how to collect data using certain machineries, interpret data and present insights. It may not be easy hearing it but we’re here to tell you that you can and will be enticed to do it.
One who wants to become a data scientist must display hard work and determination to get him where he wants to be. However, I’m not demeaning those with educational degrees in Statistics, Mathematics or Computer Science, of course they’ll have a huge advantage over those who are just still starting. I’m just saying that there are many ways available already that can act as knowledge sources of how to become a data scientist. It’s workable, attainable and possible.

2. The pay will be (competitively) high.

And that depends on how much of a value you can give to your clients and your organization. Data means business to others and if you can give them the insights that can improve their sales, you’re superb! For your information, there’s a rising demand for data scientists in most companies now such as Wall Street, Teradata, UBER, BlueCross, Walt Disney, etc. Just google the keywords “data scientist job post” and you’ll find tons of job postings locally and internationally.

Of course, the reason why you’re reading this is because you wanted to know the how much a data scientist is worth especially here in the Philippines.

Payscale, a website that enables individuals to research and compare average salaries, says that the average salary for the Data Scientist job this 2015 ranges from Php132,000 to Php381,000. These figures are just based here in the Philippines and there’ll be much higher forecast for the numbers abroad. Being a data scientist gives you the edge not just in salary but also in your other individual competitors in the industry.

3. You can START now before it fully blooms.

The Philippines has been the source of undeniably great talents for the BPO outsourcing world such as call centre agents, legal and medical transcriptionists, software developers, animators and many more. We’ve proven ourselves, as Filipinos, that we can serve the whole world with our skills and that we will still be the source of talents for the next BIG things.

While Data Scientists may still be rare to find here in the Philippines, what we do not know is that there’s a continued strong demand for this role this 2015. As many startup tech companies have been emerging, many as well would want to be cost-effective and lean in their processes. Countries are looking at the Philippines as one with talents who not just deliver quality outputs but are adaptable to any cultural differences. These startup companies are also using data as their tool in knowing how to compete and be ahead of the game, thus, the need for more data scientists who can help them in looking at their data.

Start now and be noticed in the industry!

Quality Excellence

The Quality Excellence Program introduces the principles, skills and tools needed to establish quality management in various business sectors and organisations such as Information Technology, Manufacturing, Healthcare, Education, etc.

Capability Maturity Model Integration (CMMi) Program

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Capability Maturity Model Integration (CMMi) by Parts Program

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Lean Six Sigma

Lean and Six Sigma is a data driven, customer focused, and result oriented methodology which uses statistical tools and techniques to systematically eliminate the defects and inefficiencies to improve processes. It is a systematic method to measure and analyze the business processes to identify critical factors affecting business results, thereby improving the processes and establishing controls around the improved processes.

Six sigma is a widely accepted quality concept in the corporate world today. Six Sigma started its journey in the 1980s as a data driven method to reduce variation in electronic manufacturing processes in Motorola Inc. in the USA. Six Sigma became famous when Jack Welch made it vital to his successful business strategy at General Electric in 1995. Today it is used as a business performance improvement methodology all over the world in diverse industry including general manufacturing, construction, banking and finance, healthcare, education, government, KPO/BPO, IT/ Software. At present IT/ ITES sector companies are dynamically implementing Six Sigma and it is no more confined into manufacturing sector

The term ‘six sigma’ comes from statistics and is used in statistical quality control (SQC) which evaluates process capability i.e. the numerical measure of the ability of a process to meet the customer specifications. It was originated from terminology associated with manufacturing which refers the ability of manufacturing process to produce a very high proportion of output within specifications. The sigma rating of a process indicates its yield or percentage of defect-free outputs it produces. A six sigma process is the one which produces 99.99966% statistically defect-free outputs which is equivalent to 3.4 defects per million opportunities (DPMO).

Six sigma uses a set of quality management and statistical methods and creates a team of experts within the organization (Executive Leadership, Champions, Black belt, Green Belt, Yellow Belt etc.) having specific skill sets required to carry out the six sigma project. Each six sigma project carried out within an organization follows a defined sequence of phases with quantifiable value targets e.g. reduction in process cycle time, reduce cost, increase in quality rating/ customer satisfaction index, reduction is defect rate.

Road Map of a Six Sigma project:

  1. Identify the areas of improvement
  2. Define the problem statement and goals for improvement in quantifiable terms (i.e. which can be measured numerically)
  3. Determine the resources required for the project
  4. Formulate a project deliverables timeline in a phased manner
  5. Establish performance parameters/ metrics
  6. Gather baseline information about the process
  7. Validate measurement system for the process output (Y)
  8. Examine the data collected in the earlier phase to determine a prioritized list of sources variation
  9. Explore potential causes (potential X’s for causation) and determine the impact of each X has on the response Y
  10. Determine the optimum level of vital few X’s
  11. Validate measurement system for X’s
  12. Verify process improvement
  13. Develop control mechanism to ensure sustenance of the improved process

Some of benefits of Six Sigma are given below:

  • Six Sigma helps companies to reduce cost and improve productivity
  • Six Sigma improves quality of projects output by reduction of inefficiencies and defects
  • Six Sigma increases customer satisfaction, and loyalty
  • Certified Six Sigma Professionals can help increasing ROI significantly

Learn how to be an indispensable resource in your company as they drive for market leadership and profitability. Make a significant contribution to the bottom line, help drive revenues up and help optimize costs. Move up in your career… Increase your value in the industry… Learn and Master Lean Six Sigma.

Below are the trainings & certifications in the Lean Six Sigma Program.

Six Sigma White Belt

———-  Six Sigma Yellow Belt

——————–   Lean and Six Sigma Green Belt

——————————  Lean and Six Sigma Black Belt

—————————————-  Lean and Six Sigma Master Black Belt

Parsimony Inc. provides training and certifications for these topics. You may inquire with us the rates, dates and how to achieve the highest level of expertise in the world of Lean and Six Sigma.

 

Registration

Please contact us to get a quote of this training whether for individual or on-site training. Avail of certain discounts when you register.

Register

Enterprise Big Data and Predictive Analytics

The Enterprise Big Data and Predictive Analytics Program will help you think strategies and will align your project activities with the strategic direction of your organisation. This program is designed to educate non-practicing (team leaders, managers, executives, etc.) and practicing data analysts with basic and advanced .

I. Basic Analytics Program
The courses in this program covers the theories, tools and techniques needed to have a basic understanding of analytics to understand your business performance and your customer behavior.

A. Basic Business Statistics
B. Introduction to Hypothesis Testing, ANOVA and Regression
C. Explaining analytics to Decision-makers: Insights to Actions
D. Minitab 101
E. Minitab 102
F. Organizational/Project Goal Setting
G. Google Analytics Overview
H. Data Visualization & Reporting (Tools: Tableau, BIME, etc.)
I. Statistical Graphs: Histogram & Pie, Scatter Plot, Line Charts
J. Big Data introduction
K. Picking the Right Metrics for Your Organization

 

II. Advanced Analytics Program
Advanced Analytics Program is designed to provide a more in-depth understanding of Analytics. It lets the users forecast and provide predictions to suggest actions that could improve future performance of their metrics. It also allows formulation of what-if hypotheses that lets users select the most optimum scenario and apply it to their respective organizations.  

A. MS Excel with Analytics Toolpak
B. Data-driven Business Analysis
C. Regression
D. Statistical Analysis
E. Creating Baselines
F. Predictive Modeling using Crystal Ball and Minitab
G. Building Analytics Governance (Processes, Policies, Training and Support)