How I scaled my startup ?

  • As a starting point, I never wanted my startup to scale with the philosophy of people increasing linearly with revenue. What I wanted to build right from beginning was a startup focused on one of my passions which is education with right philosophy and associated people who have similar goals
  • My idea was clear: Focus on education & academics at Gujarat level and provide services & products around it. Maybe we will scale beyond Gujarat, maybe not – we can decide at the appropriate time in future
  • Build a small consulting type firm with under 5-6 contractors / employees / partners / consultants rather than outsourcing type large firm
  • Keep the company organization, offerings, relations, taxation and other things simple
  • Allow all to work digitally from wherever they are, they only move to customer locations to fulfill their work for them, rest of the time they are at home. On top of this there is no restriction for a person associated with my startup around what they can do in off time with other customers that they build on their own
  • Focus on relationships and good deliverable to customers over billing rates and revenue
  • Narrow down focus on what you can and cannot do. Learn to say no when things are beyond you
  • Bootstrap through and through on own money for all parts: digital, physical, taxation, legal, etc. Avoid investor money till as long as possible to continue on the initial direction that the startup has
  • Have a professional website, blog, email, number, references and other details to build a good digital presence
  • Build a community of supporters and network around your startup built via genuine help & mentor-ship to them. If you are not authentic, don’t expect others to support you. Provide good services at high quality and you are very likely to have a good network
  • Be generous in sharing revenue and credit with people who are associated with you. Put them on your website, promote them, endorse and recommend them, share revenue no matter how small or big – you can’t win alone.
  • Raise the bar of your ecosystem and network rather than only yourself
  • Don’t give up on people who helped you in your journey
  • Find clear revenue sources for yourself outside of your startup domain as well to sustain the venture for long term
  • Have good reading habits, devote time to reading daily around your industry and domain
  • As of now we have 4+ universities & colleges around Gujarat as our customers
  • It’s not easy doing multiple things around startup, consulting and balancing work / life / family / network. Be ready to sacrifice lot of time, effort, money and other things on the way to achieving what you want. If you are not ready for all this, it will be difficult to scale or sustain. Be ready for at-least 12 to 24+ months of real hard work to make your brand known in your market
  • You will have bad days and you will have good days. Learn to live with them, both will come and go. Keep the count of good days larger than bad days
  • Reach out to me at neil@TechAndTrain.com if you want to discuss Data Science / R / Java / Python / etc. or want to conduct a training for MBA / BE / MCA / MSc students or are interested in having a workshop for on Data Science / R / Java / AWS / Excel / etc.

Automation problems in Boeing 737 Max in times of machine learning & artificial intelligence

Quote – French air accident investigation agency BEA said on Tuesday the flight data recorder in the Ethiopian crash that killed 157 people showed “clear similarities” to the Lion Air disaster. Since the Lion Air crash, Boeing has been pursuing a software upgrade to change how much authority is given to the Manoeuvring Characteristics Augmentation System, or MCAS, a new anti-stall system developed for the 737 MAX. – Unqoute.

Quote – The captain fought to climb, but the computer, still incorrectly sensing a stall, continued to push the nose down using the plane’s trim system. Normally, trim adjusts an aircraft’s control surfaces to ensure it flies straight and level – Unquote 

Analysis from publicly available news & articles, sources and references at the end of article.

Sequence of events:

  1. 737 Max has heavier engines which are mounted higher than other planes
  2. This may cause stall at lower speeds by pushing nose up
  3. To correct this a mechanical sensor is kept at the front of the plane to detect air flowing in parallel or at an angle known as angle of attack
  4. This sensor is connected to MCAS automation software based system which controls TRIM electro-mechanical system which keeps aircraft level and straight
  5. MCAS typically pushes nose down due to stall created by higher nose level as above
  6. In this case the sensor seemed to be faulty so MCAS wrongly took over
  7. Pilots didn’t know how to disengage the MCAS. They had to cut power to the motor that pushes nose down
  8. Another pilot who was in one of the same flights earlier could disengage MCAS by stopping the motor for a similar problem by cutting power to motor pushing nose down

Sources & references below:

https://www.ndtv.com/india-news/all-boeing-737-max-8-aircraft-in-india-will-be-grounded-by-4-pm-today-says-dgca-official-2006734

https://timesofindia.indiatimes.com/world/rest-of-world/pilot-who-hitched-a-ride-saved-lion-air-boeing-737-max-day-before-deadly-crash/articleshow/68499359.cms

https://www.ndtv.com/world-news/lion-air-plane-cockpit-voice-recorder-reveals-pilots-frantic-search-for-fix-report-2010292

https://en.wikipedia.org/wiki/Automation

https://airlinerwatch.com/boeing-737-max-8-design-or-software-problem/

What should be the subjects & course structure for teaching Data Analytics / Data Science in MBA?

Data Science & Analytics including Operations / Decision Science are evolving fields which are in demand currently for various reasons. Most companies are experimenting and creating projects / products around analytics / data science. I am listing the subjects & courses that an MBA student should take to cover Data Science / Analytics:

  1. Mathematics — Intermediate level statistics, linear algebra, discreet mathematics & basic calculus
  2. Introduction to Business Analytics & Data Science — covering basics of the subjects like what is machine learning, artificial intelligence, major software / products, data science / analytics basics including various types of data, sentiment analysis, basics of algorithms and contemporary topics
  3. BigData ecosystem — Concepts of Hadoop, Spark, MapReduce, NoSQL and the ecosystem around it
  4. Business Intelligence — covering reporting, dash-boarding, visualization and contemporary topics around it
  5. Business Analysis — covering concepts of how to collect requirements, build a project plan / statement of work, proposals, proof of concepts, concepts of AGILE / DevOps, data analysis, business process re-engineering and similar
  6. Programming in R & Python for managers — Intermediate level topics including data manipulation / cleaning, charting / visualization, running major machine learning algorithms, mathematics functions and libraries
  7. Data warehousing — covering the introduction of it and multi-dimensional cubes, business dimensions, star / snowflake schema, process of ETL and similar
  8. Data mining — covering major algorithms in supervised / unsupervised / semi-supervised areas and their implementation
  9. Cloud computing — covering cloud architecture, offerings & major product companies
  10. Operations subjects — which should include Operations management, Operations research, Project Management, Logistics & Supply Chain management, Total Quality Management
  11. Case study, use case and industry driven internships and projects which give exposure to students using proprietary / open source tools & products mapped to domains like Digital marketing, Financial analytics, HR analytics, Web / Mobile analytics, Advertising, Operational Analytics, eCommerce, Manufacturing, Banking, etc. used in industry to join all of the above together into implementation
  12. Above goes with an assumption that students already have intermediate level skills in productivity tools like MS-Office / Google Docs/Sheets, Linux, Year 1 general management subjects like Finance, HR, Marketing, etc.

Reach out to me at neil@TechAndTrain.com if you want to discuss Data Science / R / Java / Python / etc. or want to conduct a training for MBA / BE / MCA / MSc students or are interested in having a workshop for on Data Science / R / Java / AWS / Excel / etc.

Sales – From Showing up to Consulting and Design thinking

Over the years Indian IT industry has had success in getting and executing projects from the western world. Indian IT industry is now US$ 160+ billion a year in exports and domestic market combined. With India largely importing electrical & industrial machinery, oil and few other items like precious stones, it stays as an economy that needs large exports to cover it’s imports especially oil & electrical / industrial machinery. Overall it stays a net importer as of 2018 with a current account deficit which needs FDI / FII / External debt / Foreign exchange reserves to cover the same directly / indirectly.

You can get more details about economy, export and import of India in below links:

https://globaledge.msu.edu/countries/india/tradestats

https://en.wikipedia.org/wiki/Economy_of_India

Services & IT industry have been a large net exporter and medium to earn precious foreign currency. It has also created a large middle class of employees who have uplifted themselves out of lower income groups moving to upper middle class and taking up further education to move up the value chain of IT industry.

Till few years back IT industry sales was relatively normal process compared to what it’s now.

  1. Show up at the right company in the western world
  2. Showcase good past projects or capability around IT and domain if required
  3. Consider doing a Proof of concept / Small project / Consulting assignment
  4. Finalize the contract and your project would start
  5. Expand it into a larger back office extended development center for maintenance and support. You now have steady revenue
  6. People & Technology / Delivery issues in terms learning, salaries, churn, attrition, renewals of contracts, compliance and similar were the main problems
  7. Price and skills around project delivery were the main criteria for customers

In came Agile, Cloud, DevOps, Scaled agile, Digital transformation, Bigdata, Automation, Analytics & recession. The paradigm from customer’s side now has changed to:

  1. What is the value you bring to my program ?
  2. Why should I outsource to you ? How many jobs are you creating in my country ?
  3. How well do you know my domain / industry ?
  4. How can you help me in my company’s success via increased sales or operational efficiency ?
  5. Will you deliver quality output for code, domain, knowledge transfer and maintenance ?
  6. Will you follow best global practices ?
  7. Can you provide help in my local timezone in my geographical area ?
  8. Instead of sales pitches do you have a consulting and design thinking mindset ?
  9. Are your employees skilled in digital, analytics & cloud to begin with and are they willing to pick up more skills as we go ahead and evolve ?
  10. What products / intellectual property do you bring to the table ?
  11. How will you align to my business model or upgrade it ?
  12. Cost is no longer one to the top 3 criteria, it’s the above questions that matter.

In response to this existential change in IT industry many firms and employees are going through reskilling and updating their knowledge / way of working.

Result will be in terms of two groups:

  1. The ones who accept the change and upgrade themselves whether it’s a company or set of employees
  2. The ones who can’t accept this change, they will be sidelined and move to other industries or roles that don’t need to cater to this evolution

Indian IT industry is changing and there is no looking back !!! Rather than thinking cost and basic skills think of the newer questions above. That is the only way to collaborate with clients and take the industry forward.

Reach out to me at neil@TechAndTrain.com if you want to discuss Data Science / R / Java / Python / etc. or want to conduct a training for MBA / BE / MCA / MSc students or are interested in having a workshop for on Data Science / R / Java / AWS / Excel / etc.


How to solve a machine learning problem ? – 1

 

  1. Select a language like for example either of R or Python
  2. Select a machine learning package to use and associated data manipulation, charting, output, etc. packages
  3. Get and explore the data using techniques like Exploratory Data Analysis for an initial understanding of data and some inferences
  4. Break your original data set into training set and testing set. Clarify what you want to predict in testing set – for example do you want to give loan to customer based on his profile OR what services to offer based on their past recorded behavior in data sets. Typically testing set is smaller than training set and testing set would not have the prediction output (result) column in data set. That would be available in training set
  5. Find out dependent / independent variables, skewness, outliers in data, check if any values need to be converted into categorical values from numeric if they have only few states – typical examples: levels like 1, 2, 3 or YES/NO type fields / columns
  6. Plot histograms, box plots, etc. in above step 4 for help
  7. Add missing values using various techniques: Either simpler options like add mean, median, mode depending on type of data OR you can use machine learning algorithm for the same for replacing missing value or creating dummy fields / columns
  8. Move onto feature engineering by creating completely new variables from available data OR / AND transform by adding thresholds, etc. to remove outliers. Find out the important feature/s and check the relevance of the newly created features. If the new features have high co-relation to earlier features / variables you may not get many new inferences (mostly) so it would be good to do some more manipulation to get create new variables which have new inferences / results / observations
  9. Select your statistical model and create the tasks for machine learning (ML)
  10. Train your ML tasks with training data using the selected algorithm like decision tree, regression, random forest, etc. based on the fitment and suitability
  11. Predict using prediction task based on your testing data set from the trained model in step 10
  12. Check your accuracy by observing the result in real situation versus your result from step 11

This is part 1 of the series on Machine Learning. Treat this as a generic guideline. Many times we will be required to tailor this to various situations and data sets in which case the steps will get enhanced / substituted / refined as per requirement.

Reach out to me at neil@techandtrain.com if you want to discuss Data Science / R / Java / Python / etc. or want to conduct a training for MBA / BE / MCA / MSc students or are interested in having a workshop for your managers / executives on Data Science / R / Java / AWS / Excel / etc.

How to explore and learn “Analytics & Data Science” ?

One of my students asked me as to how can someone explore and learn Analytics / Data Science domain with an intention to build their career in it ?

There are three types of roles available in Data Science / Analytics:

  1. Functional consultant like a Business / Data Analyst
  2. Technical Consultant like a Data Engineer.
  3. Mixed profiles like a Data Scientist where you need to know the business domain and technology both

Here are some suggestions to start your journey in Analytics:

1. Learn either of R or Python to start with. Its good if you know Java & AWS as well.

2. Explore concepts of Machine Learning, Artificial Intelligence, BigData, BlockChain, NoSQL & IoT

3. Check the free or cheap courses on Coursera, edX, Udemy, Khan Academy, NPTEL, MIT OCW, etc. for above topics

4. If you have LinkedIn premium account, good courses are available in LinkedIn Learning as well

5. Regularly check job descriptions for Data Scientist, Data Analyst & Data Engineer – This tells you what’s happening in the market and where to align your skills

6. Follow people on LinkedIn / Twitter / Medium / etc. who are into Data Science / Analytics. They post really good information there

7. Regularly read EconomicTimes, LiveMint, Business Standard, CNN Money, BBC Business, Bloomberg, similar sites and update yourself in at-least one functional domain like Digital Marketing, Finance, HR, Operations, Banking, Insurance, etc. via NPTEL, MIT OCW, DataScienceCentral.com, Quora, etc. Explore certifications like Google Analytics.

8. You especially may want to follow people like Andriy Burkov, Andrew NG, Liz Ryan, etc. and sites like Harvard Business Review, Inc., Forbes, Technology Review, ZDNet.com, Kaggle & Sloan Management Review. Here is an example list.

9. Make a list of blogs to follow around these topics too. Here is an example list. 

10. Meet like minded professionals and students in your area using Meetup app. Build your own blog / website / small startup on what you are learning, write articles on LinkedIn / Medium, etc. which will help you to network. Offer some consulting to startups in and around your area. You can get a target list here for Gujarat (some are proper established companies, some are small / young): https://www.techandtrain.com/gujjobs.html – I update this once a month

11. Revise / study concepts of statistics, calculus, linear algebra, operations research and discrete mathematics

12. Explore the tools used by Data Scientists. Here is an example list. 

Many jobs in Analytics / Data Science are available. You can go light on technical topics if you intend to be a functional consultant. This is an evolving field and one website or one book won’t give you full information. Get into a habit of surfing from across the net and buy few good books around above topics. Things change / update / evolve in Analytics every few months.

Reach out to me at neil@techandtrain.com if you want to discuss Data Science / R / Java / etc. or want to conduct a training for MBA / BE / MCA / MSc students or are interested in having a workshop for your managers / executives on Data Science / R / Java / AWS / Excel / etc.

History & Future of Information Technology

Late 1970s – Ethernet

Late 1980s & Early 1990s – Internet

Late 1990s – Y2K was the trend and revolution of easy operating systems like Windows with word processing and spreadsheets happened. Also, LINUX. ERP, CRM, BPM, HRM and similar software pick up in market.

Early 2000s – Internet boom, Email, Browsers, Dot Com, Start of E-Commerce, Java, boom in Portals, Scanning solutions, Enterprise Content Management, Search and associated technologies

Post 2011- Shift to Mobiles, GPS, Location tracking, cameras, virtual / augmented reality on mobiles and so on. Lot of growth for companies like Google / Apple.

Post Mobiles – Cloud, Machine Learning, Artificial Intelligence, Analytics, IoT, Beacons, Business Intelligence & BigData

2010/11 onwards – RPA, SAAS/PAAS/IAAS, Facebook, Social Media, Agile, DevOps, Crypto currency, Focus on Internet, Network & Cloud security

Time is now right to have an AI / ML based System in Enterprise IT ecosystem that can create and manage systems on it’s own or at least most of it at Enterprise / Technical & Solution Architecture level along with automated creation, deployment and maintenance of software systems using Cloud

Reach out to me at neil@techandtrain.com if you want to discuss Data Science / R / Java / etc. or want to conduct a training for MBA / BE / MCA / MSc students or are interested in having a workshop for your managers / executives on Data Science / R / Java / AWS / Excel / etc.

Learning R Programming – Part 1

As per Glassdoor Top 5 skills in Data Science for job openings are:

  1. Python
  2. R
  3. SQL
  4. Hadoop
  5. Java

Most Java developers know SQL, Hadoop & Java to a good extent in today’s environment, two important skills Python & R should be learned by the Java developer / architect / manager if s/he wants to contribute / work in Data Science area. In this article you will find a structured step by step approach for learning programming in R.

  1. To start with install R and familiarize yourself with R Console & R Script interface. You can run commands on both but it’s best to write multiple commands and try them out in script editor. Use short cut CTRL + R to run your commands in R Script editor.
  2. Explore menu options like Package -> Install / Load / Choose CRAN Mirror. By default many commands for statistics, visualization, etc. are given in R by default. Big set of libraries are already loaded into R by default and 100s more are available. Select any mirror to download new packages and install / load them step by step. You will need working internet connection. Learn how to set working directory. You can see default libraries available in R using library()
  3. From there move on to various R Objects / Data Types – Explore Data Frame, Vector, List, Matrices, Arrays and Factors. Try out examples for the same.
  4. Next step learn to load / read and write datasets by commands like read.csv / write.csv. You can also read / write excel sheets but for it you will need other packages. See the basic commands like summary, structure & fix to analyze / edit your dataset
  5. Next step – go through various categories of operators (logical, mathematical, relational) and concepts like pipe %>%, constants, rules for naming identifiers followed by various statistical functions directly available in R. You can get help on a command by using ?<COMMAND>. Also, learn to create functions and use conditions like if
  6. By now you should revise basics of statistics & various visualization charts which are taught typically in Year 1 / Semester 1 of MBA. Explore various default commands for statistics built into R by default. Some examples – mean, variance, standard deviation, etc.
  7. Learn to manipulate / read / write datasets using subset, sample_n & sample_frac and using dplyr package which has commands like select & filter among others
  8. Check various types of default visualization commands in R for various charts like barplot & pie. Post this learn how to use ggplot2 package
  9. You will get many datasets at kaggle.com and various websites like stock exchanges – NSE / BSE, RBI, Open Data websites of various Governments and others
  10. Explore top 20 packages of R categorized by various areas as given below.

An advantage of learning R is that you will become better at statistics & data science. It’s much simpler than Java in terms of syntax and structure and is influenced by open source languages / scripting like Linux, etc.

Reach out to me at neil@techandtrain.com if you want to discuss R, conduct a training for MBA / BE / MCA / MSc students in R or want to conduct a workshop for your managers / executives on Data Science / R / Java / etc.

References:

Top 10 skills for Data Science – Glassdoor Economic Research

Top 20 packages in R

Universities & Colleges offering courses & degrees in Analytics / Data Science in India – Part 1

  • BITS Pilani – Offering MSc in Analytics, MTech in Computer Science with specialization in Analytics
  • IIM Calcutta, ISI Kolkata & IIT Kharagpur – PG Diploma in Business Analytics
  • Great Lakes, Chennai – PGP in Analytics
  • IIIT Bangalore – PGP in Analytics via UpGrad
  • XLRI – Executive Program in Data Science
  • NIRMA University – MBA – Courses in 2nd year MBA covering Analytics & Data Science
  • Shanti Business School, Ahmedabad – PGDM having 2nd year specialization in Analytics / Decision Science
  • Gujarat University – MSc in Machine Learning & Artificial Intelligence
  • PG Diploma in Data Science – Manipal Global Academy in Data Science
  • Big Data & Visual Analytics – SP Jain School of Global Management
  • Master of Business Administration (Data Sciences and Data Analytics)- Symbiosis Center for Information Technology
  • Indian Institute of Information Technology and Management – Kerala (IITM-K) M.Sc. In Computer Science with Specialization in Data Analytics
  • Indian Institute of Science, Bangalore M.Tech. In Computational and Data Science & M.Tech.(Research) / PhD
  • Executive Program in Business Analytics- IIM Calcutta
  • St. Xavier’s College (Autonomous), Ahmedabad. (Collaboration with Tata Consultancy Services Limited) – M.Sc. in Big Data Analytics
  • IIIT Hyderabad – MTech in Data Science / Analytics

Not a complete list but gives you good data points on courses / degrees on Data Science / Analytics in India

Mystery of ‘Perfect Numbers’ Resolved – Perfect Number is Always Even and Predictable

Mystery of ‘Perfect Numbers’ Resolved – Perfect Number is Always Even and Predictable

Vrajlal Sapovadia (Ph.D.)
Sweta Patel (Ph.D.)

Introduction

In number theory, a perfect number is a positive integer that is equal to the sum of its proper positive divisors, excluding the number itself. In other words, a perfect number is a number that is half the sum of all of its positive divisors (including itself) i.e. σ1(n) = 2n. To explain in practical terms, we elaborate first few Perfect Numbers. It may be noted that ‘Perfect Numbers’ are sparse are thinly dispersed. Starting from 3rd Century BC, mathematicians are working on Perfect Numbers. Till April 2018, i.e. during last 2300 years active research, researchers could recognize only 50 perfect numbers. There are 2 perfect numbers in first 100 and 4 in first million. Absolute distance between two perfect numbers increase exponentially as you go higher to the next perfect number[1]. One can find at least one perfect number till 4 digit numbers, and then it becomes a real rarity. Subsequent perfect numbers appears at 8, 10, 12 and 19 digits. 15th perfect number has 770 digits while 16th have 1327 digits. 25th perfect number has 13066 digits. 50th perfect number has 46,498,850 digits.

The current literature is still debating on two issues:

  1. Can perfect number is predictable?
  2. Can perfect number be odd?

We argue that perfect number is predictable and we have developed a formula which answers both lead questions as follow:

  1. Perfect number is predictable
  2. Perfect number is always even

Predictability

Euclid proved that 2p−1(2p − 1) is an even perfect number whenever 2p − 1 is prime (Euclid, Prop. IX.36). the first four perfect numbers are generated by the formula 2p−1(2p − 1), with p a prime number, as follows:

for p = 2:   21(22 − 1) = 6

for p = 3:   22(23 − 1) = 28

for p = 5:   24(25 − 1) = 496

for p = 7:   26(27 − 1) = 8128.

Prime numbers of the form 2p − 1 are known as Mersenne primes, after the seventeenth-century monk Marin Mersenne, who studied number theory and perfect numbers. For 2p − 1 to be prime, it is necessary that p itself be prime. However, not all numbers of the form 2p − 1 with a prime p are prime; for example, 211 − 1 = 2047 = 23 × 89 is not a prime number.[11] In fact, Mersenne primes are very rare—of the 2,270,720 prime numbers p up to 37,156,667,[12] 2p − 1 is prime for only 45 of them.

Nicomachus (60–120 AD) conjectured that every perfect number is of the form 2p−1(2p − 1) where 2p − 1 is prime.[13] Ibn al-Haytham (Alhazen) circa 1000 AD conjectured that every even perfect number is of that form.[14] It was not until the 18th century that Leonhard Euler proved that the formula 2p−1(2p − 1) will yield all the even perfect numbers. Thus, there is a one-to-one correspondence between even perfect numbers and Mersenne primes; each Mersenne prime generates one even perfect number, and vice versa. This result is often referred to as the Euclid–Euler theorem. As of January 2018, 50 Mersenne primes are known,[15] and therefore 50 even perfect numbers (the largest of which is 277232916 × (277232917 − 1) with 46,498,850 digits).

Owing to their form, 2p−1(2p − 1), every even perfect number is represented in binary as p ones followed by p − 1  zeros. Interestingly, when a perfect number is converted into binary, it is not only a pernicious number, but binary sequence is spectacular having all 1 on the left side followed by all 0. Interestingly count of 1 is a prime number (p) and 0 is p-1.

610 = 1102

1 (p = 2) and 0 (1)

2810 = 111002

1 (p = 3) 0 (2)

49610 = 1111100002

1 (p = 5) 0 (4)

812810 = 11111110000002

1 (p = 7) 0 (6)

3355033610 = 11111111111110000000000002

1 (p = 13) 0 (12)

858986905610 = 1111111111111111100000000000000002

1 (p = 17) 0 (16)

13743869132810 = 11111111111111111110000000000000000002

1 (p = 19) 0 (18)

230584300813995212810 = 11111111111111111111111111111110000000000000000000000000000002

1 (p = 31) 0 (30)

Thus every even perfect number is a pernicious number. Note that every even perfect number is also a practical number. Therefore a formula to find a perfect number can be developed as 1….(p) 0…(p-1), where 1 (p) and 0 (p-1) are binary symbol. Thus, a binary number so written equal to a PRIME (p) ‘1’ followed by p-1 ‘0’ could be a perfect number. It may be noted that all prime count does not result into perfect number. Therefore, it is pertinent to test each prime number with formula[2] will establish whether resultant number is perfect number or not. But in any case, this will reduce substantially the experiment time to find next perfect number or this formula provides a lead to find perfect number with less experiment time.

Odd vs. Even

It is unknown whether there is any odd perfect number, though various results have been obtained. In 1496, Jacques Lefèvre stated that Euclid’s rule gives all perfect numbers, thus implying that no odd perfect number exists. More recently, Carl Pomerance has presented a heuristic argument suggesting that indeed no odd perfect number should exist. All perfect numbers are also Ore’s harmonic numbers, and it has been conjectured as well that there are no odd Ore’s harmonic numbers other than 1. An exhaustive search by the GIMPS[3] distributed computing project has shown that the first 46 are all even numbers represented by 2p−1(2p − 1).

First 50 perfect numbers listed[4] are all even and their last one or two digits are always 6 or 28. In support of arguments made by various researchers, we found that a perfect number can be presented as binary with formula 1….(p) 0…(p-1). Any binary pattern as 1 (n) 0 (n-1) will always result into even number. Therefore any perfect number is always a even number.

[1] 6 (1), 28 (2), 496 (3), 8128 (4), 33550336 (8), 8589869056 (10), 137438691328 (12), 2305843008139952128 (19)
[2] Binary numbers 1….(p) 0…(p-1)
[3] The Great Internet Mersenne Prime Search (GIMPS) is a collaborative project of volunteers who use freely available software to search for Mersenne prime numbers.
[4] https://en.wikipedia.org/wiki/List_of_perfect_numbers

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