Integrated Structural Health Management: Fill & Download for Free

Palantir - Palantir Gotham (formerly known as Palantir Government) integrates structured and unstructured data, provides advanced search and discovery capabilities and enables knowledge management.It runs the site AnalyzeThe.US - Home, which allows the public to use Palantir Gotham to perform analysis on publicly available data from Home | Data.gov, USASpending.gov, the Center for Responsive Politics’ Open Secrets Database, and Community Health Data from http://HHS.gov.It is used by counter-terrorism analysts at offices of the FBI and CIA, fraud investigators at the Recovery Accountability and Transparency Board, and cyber analysts at Information Warfare Monitor (responsible for the GhostNet and the Shadow Network investigation).Apart from Gotham they also have Palantir Metropolis, which is used by well know hedge funds, banks and financial services to study the markets, test and refine trading strategies, and generate complex signals across asset classes.From wikipedia

Supply chain forms very important structures with responsiveness and agility in its flow. Today, big firms produce billions of data points daily which can be used to make operations, supply chain and work flow efficient. But intricacies of data mangement make things very unorganised and that's what opening new scope for individual startups these days to help big firms. Sectors like E commerce, FMCGs, Manufacturing etc. need to work a lot on organising data properly so that responsiveness to customer demands can be improved. This would only be possible if supply chain is agile. So, this is a combination which needs support of data knowledge and its application with real time experiments.So when we are already talking about data then data analytics is what we need urgently. Following factors are very crucial these days:Weather forecastTransportation efficiencyAvailability of stockCustomer preferencesGPS integration in supply chain flowOrder trackingRaw materials movementHealth management of warehousesAutomation in supply chainLosses in supply chainThese factors in itself generate lot of data and if companie are able to organise that data into some structure then we can't even imagine what could it possibly bring a new revolution in supply chain efficiency. Amazon has built new concept - Checkoutfree Store -AMAZON GO (https://www.google.co.in/amp/s/www.wired.com/2016/12/amazon-go-grocery-store/amp/?client=safari ), which is a perfect example of best combination of responsiveness and agility in supply chain. What it does is all about data gathering through sensors and then organises it so that operations can become hassle free for customers (please read shared link above).Supply chain forms 40% of the cost of whole process of manufacturing any product. Savings could be done through managing transportation logistics, accurate forecasting and inventory management. Stock replenishment, forecating error more than 10% (FMCGs generally), delays in transportation of products, warehouse movements etc. create losses. Now companies could use data analytics to do savings in these areas by learning the different algorithms which could run operations smartly. Doing any work in one minute which was taking five minutes earlier is possible through data management and usage.There are lot of startups working in field of data analytics in India: 10 Emerging Analytics Startups in India to watch for in 2016 - Analytics India MagazineSome information on what is happening in this area:McKinsey's 2016 Analytics Study Defines The Future Of Machine Learninghttps://m.yourstory.com/2016/12/growth-data-analytics-india/Future Fortunes: why predictive data is the key to successManufacturers failing on supply chain data analysis - Logistics ManagerData analytics and the changing world of retail in 2017http://www.networksasia.net/article/iot-big-data-analytics-changing-supply-chain-ecosystem-asia.1481505923

It has always excited me how I shaped my career in last 5 years and still doing it. I’ll explain you over the years how I took decisions which changed my career.2007–2010I was pursuing bachelors degree in Electrical & Electronics and I was sure I didn’t like electrical machine at all. I was more interested in Embedded systems at that time. I had studied few AI, Machine Learning courses but they didn’t excite me.2010–2011I started by internship at National Aerospace Laboratories(NAL) in the filed of Integrated Vehicle Health Management. The subject was very new to me. It was a mixture of System Engineering, Computer Science, Data Mining, Aerospace Engineering, Electrical & Electronics, Embedded Systems. I think this is the best decision of my career to opt for such a diverse project as it has shown me the pros and cons of all these field.I worked in embedded system specially Wireless Sensor Networking and electrical systems mostly.2011–2012I joined NAL full time to continue on the project. By this time I was exhausted with Embedded system and I never liked Electrical systems anyway when Andrew Ng launched Coursera. There was an opening in my team to work on Machine Learning aspects of the project so I thought why not give it a try. At that time not much was talked about Data Science as compared to today’s situation.Andrew Ng’s Machine Learning was the first course I studied to get into Data Science. It was and still amazing course. The kind of enthusiasm it builds is remarkable.I studied a lot of topics and gained basic theoretical knowledge of most of the ML algorithms. I knew if I had to succeed in this field the best way is to play with lot and lot of different kind of data. I wanted to cover both breadth and depth.2012–2013I joined big data consulting firm where I worked on more than 6 different projects in 6 months. I wanted to cover breadth at this time. I did projects from Text mining, Fraud detection, Manufacturing fault detection, etc domains. I was happy that I found some really helpful seniors which saved my time by telling what was BS and what was actually useful.2013–2015I joined India’s biggest e-commerce and have built their entire personalization and recommendation layer single handedly (from DS perspective).Now I knew how to handle big data, small data, structured and unstructured data. I knew what is important and what’s not.2015–2017I have joined a Data Science product based startup which is building a cognitive data platform for Data Science professionals.2017–CurrentWorking on things I don’t have much experience on like Image processing, Natural Language Processing, AI… Data Science is a huge domain which is changing everyday.During this journey I read lot of books, read & followed lot of blogs, studied Moocs. I have written couple of blogs and Quora posts on how to build DS knowledge.Road to become a Data ScientistHow to keep yourself Data Science up to date?Important data science booksIntro to RWhat Does a Data Scientist Do?A Practical Intro to Data SciencePros and Cons of Data Science jobsProsWork on cutting edge technologiesReally make a difference with your work for your companyWell payingConsCompanies don’t understand the complexity of problemsLot of adhoc research results in deadlines not metSolving real world problems is actually very difficultI can add more references to read but I think there’s lot of content available online. I have made personal repository of data science material per topic on Trello and I call it Great Data Science Material . I keep adding interesting stuff to it from time to time. I hope it will be useful to someone. If anyone has some interesting things which I can put in the repository then I’ll be happy to.