New certification: Deep Learning by Andrew Ng (2/5)

A seminar on career in data science at PDIT Collage Hosapete

I recently received an opportunity to speak on a topic called "career in data science" this 3rd September 2018, Monday.

Below are few pictures taken during the session

Open Letter Of Thanks

Dear Reader,

This is an open letter of thanks to each and everyone who had helped me, guided me in every single aspect. I just connected the dots and it is you people who played a vital role in my professional and career aspect.

I would like to thank everyone with a  small description explaining how you played an important role in my life. I think told isn't possible without you. I won't be able to name all, but want to name few who are the game changer in my life

The story of a Data Scientist begins between 2013-2014 when I 1st heard R and Python mentioned by Kora Reddy in his office than in Domlur, Bangalore. I never knew, what is Data Science. Again in 2014-15 when I was with Nikhil Jain in CredR's initial accommodation I met my 1st Data Scientist who was a close friend of Nikhil and then he was working for Fractal Analytics now for Oyo room was speaking some data science terminologies and I was not able to get it.

I told Nikhil; who is this weird guy?

He replied: "He is a Data Scientist"

I still wasn't able to get who is a data Scientist! what he does.

It was 2015, a chap from IIT-B was doing image processing for us and then got introduced to Python and its image processing techniques, yet unable to understand. But was able to see the difference in the image; fascinated!!! made my eyebrows rise...

After August 2015; I got disconnected with the data science team and involved completely into Market Research, Product Launch, and Business Development.

In was 1st September 2016; then City head of Cars24; Chirag Takkar was able to identify my analytical skills and told me to take analytics rather than doing sales before putting a full stop in cars24. I was not convinced by Chirag and had a halo effect or mindset.

It was Infurnia when I was introduced to various software that I hardly knew and in 2017 I kept on reading software patches of the product I used to sell and gained some confidence that I can write any code.

It was in March 2017 when Infurnia told we can't go with the sales team. I was broke and it took 2 months to figure out what went wrong. Chirag’s word was humming in my mind and some codes in Infurnia product boosted my confidence.

It was the end of May 2017, I called Siddhart Biyani who in 2014 suggested me to learn R and in 2014 he used work in Exon Mobil in a Data Science team. He guided me or suggested me a roadmap on learning R.

It took a couple of months to learn R and then I contacted Aniket Bokde who in 2015 showed me image processing. He motivated me and told how I can learn Machine Learning.

Later in 2018, I contacted a couple of people like Sudheer Katta who was then the data science head for credr who evaluated my data science portfolio. Krill who also evaluated my portfolio and both suggested me how to go ahead with data science.

I thank each and everyone in my life who played some or the other role directly or indirectly. I want to take an opportunity to thank some of them as follows:

• Kora Reddy: For 4 years I was doing Technical Analysis and you are the one who helped me understand how its done on quantitative aspect. Again you are the 1st person who told about R and Pyhton. I was blindly doing technical analysis and you helped me understand what is going on.
• Nikhil: You provided me an opportunity. I didn't knew anything, associating with you; help know some fascinating people from data scientist 
• Chirag: Thank you for letting me know my capabilities. You played a role of Jambvant, understanding my strength 
• Lovepreet: It was in your company I was able to know I can code. I used to look into the product codes and tried to understand how it is written and understand the structure of a software. 
• Biyani: You are my 1st motivator, remember the day we 1st met Marathahalli in 2014. You told me to lean R; I still remember it clearly. Thank you for guiding me again in 2017. You are my game changer 
• Bokde: Bhai, your guidance on Machine Learning was essential and you helped me in every step. Starting from machine learning to landing my 1st job. 
• Katta: Thank you for reviewing my portfolio. Your insights on Scala and big data technology were valuable input and I am on it. 
• Kirill Eremenko: Thank you for reviewing my portfolio and assisting me in my data science learning. 

There are numerous people who I need to thank i.e Imran, Rajesh, Apoorva, Bharath, Raju etc. Thank you, everyone, for paying a critical path in my data science learning path.

Thank you, everyone!

Regards,
Sangamesh K S

Customer 360: Revolutionizing The Traditional Approach?

In the era of data-driven decision making, analyzing data is becoming more essential and critical for every organization. Traditional companies had limited information with the structured way of capturing data, processing and prescribing.

Today, in the technology-driven era we are exposed to various information bursting from the traditional data source and nontraditional data source which are more personalized thus can't be overlooked by analytical perspective.

 Now, here is a catch! aggregating, processing and analyzing become challenging to achieve. Companies these days are using an array of tools including customer 360 to aggregate structured and unstructured data.

Then what is customer 360?

In customer 360 help an organization to view customer holistic by understanding his/her buying pattern, a way of living, spending, locality he lives in etc. This information will be the building blocks of the analysis of the customer.

The main objective of customer 360 is to understand the customer in every single aspect and offer him the plausible product or service or run a loyalty program to retain the customer.  

You aggregate every information from the various source by collaborating the structured and unstructured data using which we can perform various type of analysis.

Customer profiling with Customer 360

Customer data is every ware and Customer 360 enable you to gather these data. If you are like most marketers, your data is stored in various CRM tools, customer applications, and sales teams.

Achieving a unified 360 requires some systematic approach. It requires consolidation of data starting from the basic things like accurate name, contact, id. Those profiles will be enriched with information like locality, customer preference and other information which are available. This information will align with marketing analytics and define the strategies to tackle customer.  

How customer 360 help my organization?

Using the collaborated data we can perform analysis to do better predictive analytics and prescriptive analytics i.e. customer segmentation, running effective loyalty programs, perform claim analytics, fraud detection, retain customers and so on.

Which indeed help us in direct marketing and improve the ROI, help us to identify the most profitable customers, identify new market opportunities as per organizational requirement.

Conclusion

Various organizations use customer 360 to understand and analyze and target which indeed optimize the organizational expenditure on identifying the prospective clients.

Customer 360 has revolutionized the traditional ideology of data and had contributed aggregating data for a whole new level of customer analytics. Which enables you to expert strategies and execution.

A Review On Tensorflow Course By LinkedIn

Hey aspirant, let me give a quick overview answering 4 major questions related to course, who can enrol, what language you require and who can benefit from the course?

The course is offered since August 2017, if you remember correctly Tensorflow stable version was be launched after 2017 and the course only concentrate on Tensorflow. As you know, tensorflow is one of the finest ML skill after, Spark ML, Keras, H20 etc.

What is all about the course?

The course is divided into 6 parts. Initial chapters i.e 1 and 2 discuss Tensorflow, installing it, hardware requirements, its competent Libraries, how the data need to be fed to tensorflow and others.

In the 3 chapters, you will find how many types of data importing techniques you can use and which need to be used when. The 4th chapter discusses training a model using tensorflow. The 5th chapter discusses the data visualisation and output using tensorboard.

In the final chapter, it speaks about using tensorflow for development on google cloud.

Who can benefit from this course?

As most of the data science aspirants are a self-learnt data scientist and it becomes essential to know when to enrol this course.

 If you are well aware of Neural Network main terminologies like Nodes, Layers, Epoch, Loss functions, Weights, training and testing models then it is good to enroll the course.

If you are able to write a neural net on your own, understand the above technologies and have a overview how to use it. Then you will find easy to understand this course. Even a intermediate understanding on Neural Net is good for the course.

The course is good for the people who have good knowledge on Machine Learning and Deep Learning. The course does not emphasise on ML/DL and needs knowledge of Neural Networks.

Tensorflow on Python or R?

The course is completely thought on Python and if you use R alone the course will give a complete tutorial on installing python to using tensorflow on python A-Z.

Can I use Tensorflow on R? 

Obviously, you do have a package on R and works similar. Only the data importing technique is different compared to Python

Is it value for money?

LinkedIn is always a economical study technique and the course is filled with right amount of theory and practical but speak intuitive level of Deep Learning. You can also know  

Indian Telecom Voice Call User Experience & Rating Between June-2017 to Feb-2018

Sangamesh K S

March 14, 2018

Introduction

The dataset is from data.gov.in and can find the dataset here: https://data.gov.in/catalog/voice-call-quality-customer-experience

The data consist of 9 month user rating of 9 popular telecom companies in India. The data collection is between June 2017 to February 2018.

You can see the sample of the data below

##   Month Operator   Rating       Type Year
## 1     6      Jio 3.175126    OutDoor 2017
## 2     6      Jio 3.555710     Indoor 2017
## 3     6      Jio 3.307873 Travelling 2017
## 4     6     Idea 3.952443     Indoor 2017
## 5     6     Idea 3.515842    OutDoor 2017
## 6     6     Idea 4.474982 Travelling 2017

Average rating of Telecom Brands

Now lets find the average rating of all networks

As per the data Jio is leading in rating followed by BSNL and Reliance with the lowest rating

Time Series of Rating

Now lets look at the overall rating of all the companies month wise

We can see the fluctuation in the rating on month on month basis it might be due to customer churn, bad network, customer moving to other location etc.We can see a sudden drop in the rating after December 2017

Overall rating of all brands

The data is been collected in 3 phase. Indoor,Outdoor and Travelling. So lets look at the rating of all the telecom companies.

Conclusion

Jio turns to be a market leader followed by BSNL. We do not see any significant difference between networks but reliance is slightly on a lower side.