Bharat vs India

Today I am writing this blog to give a very tiny tribute to honorable Dr. APJ Abdul Kalam.
I am too small a human being to express what he has done for our Nation. A very sad day for me and for the entire country.
He was one of my heroes and an inspirational figure.I am dedicating this blog to him.

Recently, there has been a lot of talk about how to address our country. Our's is the only country which is addressed using 3 different names : - India, Bharat, Hindustan. The name Bharat has its own historical meaning . There seems to be confusion over which of the terms represent 'real' India. India is English. Bharat is about regional languages. India is urban. Bharat is rural. English media caters to India. Regional media caters to Bharat. India values Western ideals. Bharat upholds traditional thoughts. India belongs to the rich and powerful. Bharat is of the poor simple folk.


Never had the British thought that their one word, India, for  our nation , Bharat would actually be competing with each other. However the truth stands so; while half of the nation has moved up the status of India, the other half is still in the archaic remains of Bharat. Our nation today stands divided ,apart from religion and states, in its representational form.On one hand where it represents future world leader, India, on the other it remains  a nation put down by poverty and corruption, Bharat. While our economists and educationalists are scaling heights in the world giving name and fame to India, the fact remains that we have one of the highest illiteracy rate in Bharat. At a time when India is showing great efforts to improve healthcare with cashless cards and medical insurance schemes, there are millions in Bharat who die because of lack of healthcare.

A quick introspection reveals that a non inclusive growth strategy of the government along with the vote bank politics has created this divide between India and Bharat. India’s political leaders work for India using vote banks that are located in Bharat. Both fiction and non-fiction authors who seek to decode India for the world endorse this divide. Even business people and consumer experts speak of this divide but prefer referring to India as India Urban and Bharat as India rural.While large impetuous is given on the growth of revenue generating hubs/metros , the other sectors/cities have been devoid of even a cinch of development and attention. 


India and Bharat view each other with suspicion. For India, Bharat is about ‘khap-panchayats’ that kill daughters that dare stand up against the family pressure. For Bharat, India is where ‘homosexuality is acceptable’. For India, Bharat is where minorities are massacred. For Bharat, India is where traditions are not respected. For India, Bharat is the land where Dalits are butchered. For Bharat, India is the land of loose moral values. For India, Bharat is the land of Hindu fundamentalists. For Bharat, India the land of pseudo-secularists.


Real or artificial, this divide is used to create heroes and villains. And so, depending on the context, India becomes liberal to patriarchal Bharat, or India becomes Western to traditional, rooted and grounded Bharat. Depending on the context, government policies seem to favour either India or Bharat. Depending on the context, India has to learn from Bharat or Bharat has to learn from India.One can only conclude that the British left India, but we continue to uphold their ‘divide and rule’ policy in our public discourse.

But there are some questions in my mind which are still unanswered. Are we trying to convert Bharat to India in terms of development ?  the first noble prize winner of India Ravindranath Tagore is from Bharat. Almost 95 % Bharatratna award winners are from Bharat. Famous economists,scientists are leaders who represent India on global level are from Bharat. The Missile man of India, who inspired every Indian (rural and urban both)  with his vision 2020 is from Bharat. So, don't you think that Bharat is representing us on Global level ?

What is urban India doing ? Working for foreign companies, feeling proud of 5 day week and talking about Japanese work spirit and Chinese business minds. Urban India makes good amount of money. But, while paying taxes they feel there is large inequality and injustice in India. Urban India is busy discussing 20 - 20 tournament than vision 20 - 20., Feeling proud and patriotic for wearing Gandhi topi in a protest, and discussing rural development programs in a five star hotel.

To conclude, I think Indian government should start and focus more on "Urban Mind Development Program"  along with rural "economy" development program.

"Be Patient" - Nation under development.

India is one of the fastest developing countries in the world. But you can see the fact that the speed of the development is not as good when compared to the other countries that are already developed and that are much superior to India. There are many reasons behind the lack in the development criteria of India. Most of the people realize that India is very slow in development but they don't focus on what to be done in order to make India a developed country. There are many issues in our country that are preventing India from becoming the developed nation.

For now,let's keep aside all the global issues and all the highlighted local issues.Let's forget about the growth in industrial sector, forget about the economy, forget about the technological growth. I want to address a very basic issue that is not allowing us to develop as a nation.

I was inspired to think and write about this, by one of the most inspiring personalities and innate speaker, Mr. Charanpreet Singh(Associate Dean Praxis Busniess School and former Country Manager of HP).

Let me start with a very common day to day situation. You are late for work, sitting in your car, the traffic is heavy. In a car next to you, a man is thumping on his steering wheel almost turning blue. If you really study him, it looks quite funny. He tries his best to disturb himself and his anger and frustration take control of him. He abuses everyone he is looking at. then he sees a small opening and forces his way through that.Due to this, the traffic which was already starting to make way is again jammed by this activity. Then you start wondering Where is he trying to rush off to? and Why? 

After I thought about this situation I came with this answer.…Because he is good-for-nothing.

This person’s impatience stems from his belief that he is a good-for-nothing; hence, he’s always in a rush to do more and be more in a bid to prove his ability. This is the case with most of the individuals in this country. They always want to prove something. they always think to contradict rather than appreciate and this I think leads to impatience.If you had patience in this case, you would sit back and just wait for the traffic to move, no doubt making use of your time spent waiting in the car thinking about what you have to say for the meeting you're due at that morning, or perhaps you just sit back, listen to music and wait happily till the traffic improves. This is just one incident. There are countless incidents that we can list in our day to day life. 

"Patience" ! This is the eight letter word that is on the verge of being obsolete in this country. No one in this country wants to wait. They are all in hurry to get somewhere , to prove something. But the question is "At the cost of what ?". Quoting Charanpreet Sir "India would be on the path of development if all the citizens of the country become patient".  It is true.
Patience brings out respect, and unless we wont respect each other we will never develop as a nation.

The system has programmed us to believe that patience is against success, and material success is necessary for a good life. “Why is this so slow?”, “Why can’t this be faster?” or “What’s next?” are generally the questions asked.



I will ask you one more interesting question. Just think about it and retrospect.

How many of you will prefer 10 videos of 5 minutes over a long video of 50 minutes?
I don't want to get into which approach is right or wrong, but the point I want to make is "How many of you are ready for a long video and will be able to concentrate ?".

Patience is often something that needs to be taught to us in our childhood, along with the lesson of sharing. Watch any young child and if left to its own devices and its own will, it will be a selfish child. I want that and I want it right now. So teaching a child to share its toys is also the start of teaching that child patience. Unfortunately this does not seem to be done so much these days. I believe that we are all born selfish by nature, down to natural instincts and actual survival, so of course its important that we learn. 

For some its easy to learn, for others especially those not correct when a child, then they become impatient adults, not getting their own way instantly, or not getting an answer straight away will literally send them into a raging temper. Until others point it out to them and make them wait their turn, they will not even learn patience when fully adult. Having patience is good in many ways, and that is why its important. On any scale in life patience plays a part. Be it waiting for that hopefully expected new job from a company we want to work for, or any other situation.
One important point I want to address in this article is How does one change from being an impatient person, to a naturally patient one?

The first thing is to realize that patience, like other traits you have, is simply the amalgamation of your underlying beliefs, which are the result of your upbringing or past encounters. This means that you’re not impatient because “that’s just how you are” or “that’s what you were born with”. You are impatient because of certain beliefs you have about yourself and the world.  I want to state a cause of why are we impatient and how could we tackle it.

So, what causes impatience then?

An impatient person never wants to wait for others, or does so with great reluctance. An impatient person feels angsty when things do not go to plan. An impatient person usually feels a great sense of urgency to get things over with and to move on to the next thing, the next task, the next place, the next stop. He/she usually has little regard or interest to what’s going on at this moment in time, because in his/her mind, he/she is already thinking about what he/she has to do next.

The impatient person is someone who often feels a great deal of urgency, internal pressure, and internal stress. It’s as if he/she is trying to rush to somewhere, or has something that needs to be completed ASAP.
But why? Why does the impatient person feel so much urgency, more so than others? What’s causing it? What exactly is this rush for?“Why are we always in such a rush?”

It is because,we have always have a subconscious belief that we are not good enough.We always think about changing our-self, achieving bigger visions, and accomplishing more goals. Hence, we keep projecting to the future, using it as our source of salvation.
Does it work though? May be. But, whenever we achieve our goals, we feel happy for that moment. However, it is just a matter of time before we think, “What’s next?”, and get back into that impatient persona. We are constantly in a rush, trying to get from one place (be it a mental vision or a physical place) to another. We do this just to be finally happy one day.





Do you see the problem here?  The problem is not  that we have not achieved our ultimate end goal yet. There is never going to be an end our my goals or visions, because there is always room to grow, be better. The problem is not about our desire to set goals either as, the desire for betterment is part and parcel of being a human. There is nothing worse than being a man who has no vision or dreams.





The problem here is (a) our inherent unhappiness with ourselves and (b) our belief that we should be  considered good enough. It is this flawed belief that creates endless urge to always hurry, act faster, stop being a slow poke, and get moving, so we could get on to the next big thing.


This is precisely why almost every single thing gets on our nerves.Each little thing—be it the bus being late, to the copier machine being jammed—would be something that stands in our way of achieving our end vision, which in turn stands in  the way of becoming a more desirable, less hateable, person.

Thinking about being less hateble person and more desirable makes you hate yourself more. 

The resolution to this is addressing self limiting belief about yourself .Stop giving utter importance to consequence and stop hating yourselves for unnecessary reasons. When you do that, the feelings of impatience will melt away like water rolling off skin.

The cause of your impatience might not be the same as the cause of your stated in this article. While it’s natural to look forward to the future and want to achieve your goals as soon as possible, there is definitely something amiss when this desire repeatedly manifests itself as a constant feeling of impatience, a source of self-pressure (in an unhealthy way), and an annoyance at things that stand in your way.Know that one can desire to achieve a better future and still be at peace with the present moment. They are not mutually exclusive with each other.

I hope you find this post helpful in cultivating the virtue of patience. Know that patience is already inside you; you don’t have to intentionally enforce it on yourself. What you should do, to become a truly patient person in the mind, heart, body, and soul, is to address the broken beliefs that are making you impatient (which is what the whole article has been about). When you do that, patience will become an effortless virtue. If you need your nation to develop, this virtue is mandatory quality you should posses.

Your nation has given you an identity. Lets pay back by giving your nation one!

Data Mining - A small hole in Privacy?

Few days ago I visited a hotel. It was a great experience and to add cherry on top the gave a feedback form as well. As a feedback enthusiast, I put in all the correct information and gave them an honest feedback. Few days later, I got a call from the hotel manager wishing me a Happy birthday! I was amazed, overjoyed. Nowadays, your relatives seldom wish  you on your birthday, but this man called out of nowhere and wished me. It made me take my family to that hotel and I was given a great discount.

While returning, I thought how do they manage this.  Being into technology i figured out that they might have a database of customer details. But, to wish a customer on his birthday was still an amazing thing. How could they achieve this. The answer is Data Mining. The term may be new for most of us, but the idea exists since many years. It has crated a revolution in market now a days.

After digging deep into the topic, I got to know some techniques of gathering data about the customers. In one shopping mall they calculate how much time customer is standing outside a shop staring at items, and which shop is he standing opposite to. Is it apparels, food , footwear's, bags or some other ? By getting the image data or a video footage from CCTV, we get the offers messages when we are at shop. Isn't this fast ? CCTV also gives the information about the person accompanying you. The offers are generated accordingly.

This is amazing technique to lure the customers in buying the product. but on the other hand it is a double edged sword. There is an unintended consequence. Now, your private life is known to most of the retail shops, hotels and other similar places. You are called on your birthday, your anniversary, and on each special da you had mentioned in the feedback form. Your feedback is taken seriously but your information is taken seriously as well. Some may argue that this is not harmful as we get all the benefits.

This argument is true for certain extent. When we can share the most of the things on social media and make it our private life public then what's the harm in this. So, this is the reason I have asked a question in my title. Is this creating a hole in your privacy ? Is it forcing you to visit a place, you were not planning to go. We should think on this.

I would like to mention some more issues about mining. It is a great technology but you should know some technological drawbacks as well.

As discussed earlier,one of the key issues raised by data mining technology is not a business or technological one, but a social one. It is the issue of individual privacy. Data mining makes it possible to analyze routine business transactions and glean a significant amount of information about individuals buying habits and preferences.

Another issue is that of data integrity. Clearly, data analysis can only be as good as the data that is being analyzed. A key implementation challenge is integrating conflicting or redundant data from different sources. For example, a bank may maintain credit cards accounts on several different databases. The addresses (or even the names) of a single cardholder may be different in each. Software must translate data from one system to another and select the address most recently entered.

A hotly debated technical issue is whether it is better to set up a relational database structure or a multidimensional one. In a relational structure, data is stored in tables, permitting ad hoc queries. In a multidimensional structure, on the other hand, sets of cubes are arranged in arrays, with subsets created according to category. While multidimensional structures facilitate multidimensional data mining, relational structures thus far have performed better in client/server environments. And, with the explosion of the Internet, the world is becoming one big client/server environment.

Finally, there is the issue of cost. While system hardware costs have dropped dramatically within the past five years, data mining and data warehousing tend to be self-reinforcing. The more powerful the data mining queries, the greater the utility of the information being gleaned from the data, and the greater the pressure to increase the amount of data being collected and maintained, which increases the pressure for faster, more powerful data mining queries. This increases pressure for larger, faster systems, which are more expensive. 

 

So, before we move in more depth about mining, it is always good to know about the issues first. In my next few blogs I will talk about mining techniques and how it can be effectively used by various organizations.

A Basic Statistical problem Solved using Python.

An organization surveyed the price of petrol at petrol stations in Mumbai and Bangalore. The data, in rs per liter, are given below.

Mumbai         73.96 73.76 74.00 73.91 73.69 73.72

Bangalore        73.97 73.81 73.52 74.08 73.88 73.68

Which city has the more consistently priced petrol? Give reasons for your answer.

This is a very basic form of data science question. we will try to solve this problem using Python and come up with an appropriate reason for this question.

Note : For now, you will think that we can calculate this manually. But , you will be dealing with thousands of records. At that time, this basic tutorial also proves beneficial.

Steps to solve a problem :

- Understand the problem

- Design an approach

- Solve using Python

- Quote your answer with a reason.

For more information on Python please visit m previous blog.

1. Understand the problem.

We have been asked that which city has consistently priced petrol. After reading the problem we should be able to think what exactly we should find to ans this question. The price should not vary a great deal if we want a consistent result. So, by calculating the standard deviation we will be able to answer the question. So, we have to find standard deviation for both the cities and compare the values.

2. Design an approach.

- Find mean for both the cities.

- Find Standard Deviation.

- Compare both the values.

3. Solve using Python.

Lets solve this problem using Python.

Define functions for mean and standard deviation.

Create a main function to add the values to list , calculate the std deviation on that values and compare the std dev.

Note : (If data is large, then we will store it in excel sheet and then import that excel sheet.This will be shown in one of the upcoming tutorial)

After executing this program the answer that we get is :

4. Quote your answer with reason :

When we calculate consistency, standard deviation plays an important role. Lower the standard deviation, consistent are the values. So, for our problem, the std dev is low for the petrol prices in Mumbai than for Bangalore. So the prices in Mumbai are consistent as compared to Bangalore.

My first Python Application - Add, Multiply ,Subtract, Divide

Whenever I learn a new programming language, a calculator is the first application I build. It is because, calculator is always useful and you can build it b some basic programming knowledge.

To work with data , lets get used to the language.

Requirements  : basic knowledge of Python.

To get acquainted with python before starting this application please click here. 

For programmers, this application may be a cake walk. If you are a beginner then I will encourage you to use this as application as a guideline to build your own application. 

In this application, we will perform basic Mathematical Operations i.e. add,subtract,multiply and divide. We will write four functions for these operations. the functions are shown below.

Learn more about writing functions in Python.

Note : I am executing this application in Spyder that comes with Anaconda. You may choose any IDE to write this Application.

Add function will look like this :

Similarly, let us do it for other three operations.

Try to call these operations from a main function a shown below :

It is not necessary to define a main function in Python. But try to define it as the code becomes more readable and it is very easy to spot a logical error if there is any.

When I run this application the interpreter starts from the top and looks out for an call. For now, it is main() as all other are "def" i.e. all others are function signatures. So, your program will start from a line that is not a function signature.

In my next blog, I will try to come up with a more of a data science related tutorial.

Python - A Snake or a Ladder ?

Let me start with something like this :
"If you want to survive in analytics then you need to hunt Python".
Don't be afraid. Learning Python is not as difficult as hunting one. But, it needs considerable effort to be a master in Python.

Python is a high level,interpreted,interactive and object oriented language.Python is most familiar and commonly used programming language in the field of  analytics. What makes Python different from other languages is it is open source. Anyone can just download Python and can start learning it. Moreover,Python is a dynamic and strongly typed programming language that is designed to emphasize usability. To download Python please click here.

Features of Python :

- As Python is an interpreted language , it does not have compiler to pre-proccess the instructions as the instructions are processed at run time.

- Supports Functional,Procedural as well as Object Oriented Programming technique.

- Easily Integrated with C, C++ , COM, Active X, Java.

- Automatic garbage collection.

- High level dynamic data types.

- Can be used as scripting language or can be compiled to byte code for building large applications.


Before starting with Python, let us first know why Python is preferred for data analytics over other languages such as C#, Java, C++, etc. .

Every sector of business is being transformed by the modern deluge of data. This spells doom for some, and creates massive opportunity for others. Those who thrive in this environment will do so only by quickly converting data into meaningful business insights and competitive advantage. Business analysts and data scientists need to wield agile tools, instead of being enslaved by legacy information architectures.

Python is easy for analysts to learn and use, but powerful enough to tackle even the most difficult problems in virtually any domain. It integrates well with existing IT infrastructure, and is very platform independent. Among modern languages, its agility and the productivity of Python-based solutions is legendary. Companies of all sizes and in all areas - from the biggest investment banks to the smallest social/mobile web app startups - are using Python to run their business and manage their data.

A Pill inside Peanut Butter !

I have dog and he has fallen ill. So, what do I do ? I call up a veterinary doctor and he suggests me some pills. Now, when I am trying to give my dog a pill he just spits out the pill every time. Then I try again by opening his mouth forcefully but in vain. I call up a friend asking for an advice. He tells me to embed my pill in a peanut butter. And, I get the desired task completed successfully. The point here is to provide people information in a way they like.  If you will force them to accept things in a very crude way they will spit out the things. So for you, dog is your client (Please don't use this example while presenting to the client else you will get a pill that will not be embedded in a peanut butter), Pill is the data you need to present and peanut butter is the story which you are building around that data.

Most organizations recognize that being a successful, data-driven company requires skilled developers and analysts. Fewer grasp how to use data to tell a meaningful story that resonates both intellectually and emotionally with an audience. Marketers are responsible for this story; as such, they're often the bridge between the data and those who need to learn something from it, or make decisions based on its analysis. As marketers, we can tailor the story to the audience and effectively use data visualization to complement our narrative. We know that data is powerful. But with a good story, it's unforgettable.

Rudyard Kipling once wrote, "If history were taught in the form of stories, it would never be forgotten."€ The same applies to data. Companies must understand that data will be remembered only if presented in the right way. And often a slide, spreadsheet or graph is not the right way; a story is. Executives and managers are being bombarded with dashboards brimming with analytics. They struggle with data-driven decision making because they don'€™t know the story behind the data. In this article, I explain how marketers can make that data more meaningful through the use of storytelling.

Identify the audience

 Most captivating storytellers grasp the importance of understanding the audience. They might tell the same story to a child and adult, but the intonation and delivery will be different. In the same way, a data-based story should be adjusted based on the listener. For example, when speaking to an executive, statistics are likely key to the conversation, but a business intelligence manager would likely find methods and techniques just as important to the story.

Let Audience visualize the data

Extremely efficient and visual way to tell a story is by using charts,graphs and maps. Large data set can be transformed and incorporated into an interesting story using these techniques. It takes data to the next level and adds value to the story.The visualization provides more content for those interested in diving deeper into the data.

Marketers are responsible for messaging; as such, they'€™re often the bridge between the data and those who need to learn something from it, or make decisions based on its analysis. By rethinking the way we use data and understanding our audience, we can create meaningful stories that influence and engage the audience on both an emotional and logical level. In short , the taste of peanut butter should make the pill easy to swallow. 

Business Statistics - An overview

The word statistics has many different meanings in our culture. Business statistics also has its own language and definition. the study of statistics is divided into two branches : descriptive statistics and inferential statistics.

In this post I will try to explain some basic differences between these two branches. To understand the difference definitions of population and sample will be useful.

A population can be a group of people("All people working in Microsoft") or set of objects ("All soaps produced on 15th April"). The researcher defines population to be whatever he or she is studying. A sample is portion of the whole, and if properly taken, then representative of the whole.
For many reasons the researchers prefer to work with sample  of the population rather than entire population.For e.g., i conducting quality control experiments to determine average life of light bulbs a researcher will base his conclusion on observation on sample of 75 light bulbs rather than total light bulbs. This approach saves time and money.


If a business analyst is using data gathered of whole group to reach conclusions of the same group, the statistics are called descriptive statistics. In short, if population is 75 and all the 75 records are considered to base conclusions then the statistics are descriptive. Most of the data generated by business is descriptive. For e.g., number of employees on vacation during June.

If researcher gathers data from sample to base his conclusion about the whole group then the statistics are inferential. The data generated is used to infer something about the larger set. The use an importance of inferential statistics continue to grow. One of the example of inferential statistics is in pharmaceutical research. When we want to test different medicines then our resources are limited, so we have to consider small sample of patients and then design our experiments to arrive at a conclusion.

Market researchers use inferential statistics to observe the effect of a advertisement on various market segments. If a cold drink company wants to see what effect its advertising of its new bottle has created, then researchers take sample of population from different age groups and based their conclusion on this  sample. Advantage of this method is a researcher can perform various experiments without conducting census.

A descriptive measure of population is called parameter and that of sample is called statistic. The former is denoted in Greek letters and the later is denoted in Roman letters.

These were some basics of Descriptive and Inferential statistics. In few of my next blogs, i will explain some more statistical concepts. Moreover, we will start with R : A Statistical and Interpretive programming language.   

Decision Dilemma

India is the second largest country in the world, with more than a billion people. Three quarter of population lives in rural area. there are total 6 million villages. Presently, the population in India is described as poor and semi literate. Moreover, rural population accounts for only one third of total annual national product sales. Less than 50 % of households in India have electricity and man of the roads are not paved. The annual per capita consumption of toothpaste in rural India is only 30 grams as compared to 160 grams in Urban India. In USA the consumption is 400 grams.

These is the kind of data company s want to target rural India as upcoming market for their products. The people in rural areas are becoming conscious about their life style and their living. So, the market rate in rural India is increasing 5 times the rate of Urban India. There is reduction of gap in the tastes of rural and urban customers and the literacy rate is also increasing in rural areas. Two third of population in rural India now have main source of income other than farming.Virtually, every house has a radio, 30 % of them have television and more than 40 % have at least one account in bank.

In early 1990's the toothpaste consumption in rural India doubled and the use of shampoo became fourfold. Recently, other products have also done well in rural India. So, man US and Indian firms have entered the rural market with enthusiasm. Marketing to rural India customer is very difficult and involves building categories persuading them to try and adopt the products that they may have never used before.

Rural India is huge, relatively untapped market for business. However, entering such a market is not without risks and obstacles. The dilemma companies are facing is "Whether to enter this market place, and if so, to what extent".

These are the statistical questions generally companies ask before entering this market place :

1. Are the statistics presented in any report exact figure or estimate ?

2. How and where did the researchers gathered this data ?

3. In measuring the potential of rural Indian market what other statistics have been gathered ?

4. How managers can use this statistics to make better decisions ?

Solving this dilemma is where hiring an analyst proves an excellent move for any organization.

Source : Adapted from "Selling in rural India" and "Understanding the market environment".

B2C - e-commerce

This blog does not contribute much to analytics, but I am writing it because this is a new thing i learnt during last few days. I knew about this concept, But i would love to shed a bit more light on it.Moreover, these are some things if you want to join retail analytics.

This blog will cover three things :

- Business to consumer as a concept
- Models of B2C
- Advantages of B2C

"Business-to-Consumer," usually abbreviated B2C, is a phrase attached to electronic business activities that focus on retail transactions rather than activities conducted between two businesses. These uses appeared along with Internet commerce in the 1990s and have been current since then. The usage has expanded so that, in the mid-2000s, B2C is also used as a handy abbreviation in talking about retail trade where electronics is just one component of the transaction and other cases where simply "retail trade" is meant. 

Let us focus on an incident that happened in 1886. 

          

In 1886, a jeweler in USA is unhappy with a shipment of watches. So he refuses to accept them.

A local telegraphy operator who worked in that shop decides to buy the unwanted shipment.

He inventively uses the telegraph to sell all the watches to fellow operators and railroad employees. He becomes so successful that he quits his job and starts his own enterprise, specializing in catalog sales. This man then goes on to become an owner of one of the larges departmental stores in USA called "Sears". 

Name: Richards Sears.

The point  I trying to state here is the medium has changed from Catalogs and telegraphs to  World Wide Web , but the concept is same . i.e. Business to Consumer . Moreover, this concept has a electronic backing making it a heart of e-commerce now a days.

Models of B2C :

B2C is divided into different models. We have to do this division to categorize different types of businesses. You cannot compare flipkart with cricbuzz. Can you ?

Types of Models :

1 . Portal :

Have you ever imagined what is your identity when you connect to world wide web. Let us leave aside a technical term called "IP Address".  as a user what is your identity ?
It is and has to be your email address. So websites that provide you your home base come under this model. for e.g. : yahoo.com , google.com etc. .

 2. E tailer :

It is an Online version of retail shop where customers can buy 24/7 with comfort.

for e.g. : flipkart.com , amazon.com etc. .

3. Content Provider : 

These are types of websites that keep you updated all the time with the recent happenings. They provide you with a lot of information. eg. : sportsonline.com, CNN.com etc..

4. Transaction Broker:

Processor's pf online sales transaction such as stock broker and travel agents that increase the productivity by helping users to do things in a better way. The sell other's product rather than their own product. they are similar to brokers who help user to do things faster and with ease . eg. : monster.com,e-trade.com, etc..

5. Service provider :

Companies that make money by providing users a service, rather than a product. for eg : Google drive, Windows updates etc..

6. Market creater : 

Web based business that brings buyers and sellers together.

eg : OLX,quckier,etc..

7. Community provider : 

Sites where individuals with particular hobbies can come together compare. This type is particularly any type of forum that revolves around same interests and idea.

e.g. : stackoverflow.com, ivillage.com etc. .

B2C Pricing models  :

Demand Sensitive Pricing :

                - Individual buyers to shop in large groups to obtain group discount.

                - the more people who buy a product in a single purchase, lower is the cost per person.

                - demandling.com,mercata.com

Comparison Pricing :

                - Allows customer to poll variety of merchants and find desired products.

                - Uses Intelligent agent technology such as customer ratings , group discussions etc. 

Advantages of B2C :

- Instantaneous communication : It helps to reduce "Time to market".

- Global Access : Access to larger market.

- Customization : Configure goods according to users need.

- Availability : Access to website 24/7, so that user can order goods any time.

- Elimination of middleman : Direct business with consumer without any distributer or agent.

- Collaborative processing : Support real time information exchange and automate transaction.  

There is much more detailed stuff about this topic , but I have tried to cover all the basics.To conclude this post i will define B2C as bricks-and-clicks. B2C is all about giving user a choice and being users choice.

Predicting Results.

With the almighty ASHES starting from today or Wimbledon "the hallowed ground of sports" as they call it, is in its final stages,Lets begin this discussion with couple of questions :
"Who will win the Ashes this time ? or "Who will be crowned as champion of Wimbledon?".

These are the classic examples of of predicting the results with the help of analytics. Many people can intuitively say that Mighty Aussies will thrash the Englishmen this time. Or, Novak with his current form will he champion undoubtedly,. If they think so,then they have not done their research. These intuitions may or may not be right. This is when data science comes into picture. 

The results or outcomes predicted using data science may not match the exact outcome all the time, but the probability of getting the predicted result is always high. After all it is all about the "probability". isn't it ?

A data scientist will start his process by gathering data related to the event occurring. Now, for Ashes & Wimbledon, we have a large data to handle. But,the data scientist should be smart enough to pick appropriate data. Lets see what questions we as a data scientist should ask before looking out for the data.

For ASHES :

Where will all the matches be held ?

What is the record of home team and visiting team on those venues?

what was the result When two teams met last time on that venue?

What was the result in the previous Ashes series ?
Who all players are included in the squad and what are their records when playing against each other?

...

You can form as many questions as you want to extract all the meaningful data that will help you predicting the outcome.

After you extract the data then it is all about the statistical methods to predict the outcome of an event. 

I will discuss these methods as we go along. But, for now, I hope you have got hands on with what data scientists do. Framing questions to extract data is very important part in predicting the outcome of an event. 

After reading this example, if you really feel that data science is for you then get going. You will be the one predicting all the results.

Similarly, when a data scientist works for an organisation he has to extract all the data related to the business problem. Believe me the business problem is more interesting than the example listed here.

Thinking Data...!!!

A few days ago I met a friend after a long time. During our school days if we wanted to list top five 5 back benches, he would have easily topped the list. And now, he had this completely nerdy look on his face.After a curious interaction, I came to know that he was working as a Data scientist in a well- known organization.

The term was interesting . A data scientist. It was the glamour of that term which made me search about it, and this is what I came up at a very amateur  level . I will deal with these five questions in this blog:

1. What is a data scientist ?

2. Is it just an attractive term for a mediocre job?

3. How is the term scientist associated with the term data ?

4. What are the skills a data scientist needs ?

5. What steps do we need to follow If we want to be Data Scientists ?

A data scientist is the one who finds patterns from a large amount of data, and gives us a meaningful and structured information out of it. Scientist who makes new discoveries,A data scientist is bound to  discover some new information from given data.

A good data scientist visualizes data, creates reports, looks for meaningful patterns, predicts an outcome, and draws a conclusion. He creates his own algorithms to extract data. Someone, who creates anything of his own is a scientist. So, the term "Data Scientist".

To be a data scientist, you need to have a fundamental knowledge of Mathematics ,Statistics ,Computer science and bit of Domain knowledge(this comes under Business Knowledge). You can learn all of these skills, but one fundamental quality you need to posses is "Curiosity". If you are not curious then this field is not for you. This is a very unique skill set and so, there are not many data scientists out there.

There are lot of myths about data scientists as well . Let me also tell you what a data scientist is not.

- Data scientists are not hardcore programmers. They should be good with basic coding techniques and logic and they are good to go.

-  Data scientists are not Business Intelligence analysts. They just may hypothesize what they think important. But, they will use all kind of algorithms to confirm those hypothesis.

These are the basic things about a data scientist. I will keep you posted as I learn many more things during my impeccable journey of being a data scientist.
In my next few blogs, I will try to summarize and write about how to get into data science and the difference between a data analyst and a data scientist.