Addicted to Tech

Data Science v/s Artificial Intelligence v/s Machine Learning v/s Deep Learning What is Data Science? Let’s break the term into its composite parts – data and science.  Science works fundamentally through the formulation of hypotheses – educated guesses that seek to explain how something works and then finding enough reasonable evidence through observations in the real world to either prove the hypothesis right, or falsify it. Data, on the other hand, refers simply to numbers and statistics which we gather for the sake of analysis. By combining these two, we get data science. What exactly does it mean? Data science is an umbrella term for all techniques and methods that we use to analyze massive amounts of data with the purpose of extracting knowledge from them. Example of Data Science: Let’s say you are crazy about Cricket, which I am sure you are, and there is an ongoing series between India and Australia. India loses the first two matches, m

Basic Knowledge a Full Stack developer MUST have: Server, Network, and Hosting Environment. This involves understanding what can break and why, taking no resource for granted. Appropriate use of the file system, cloud storage, network resources, and an understanding of data redundancy and availability is necessary. How does the application scale given the hardware constraints? What about multi-threading and race conditions? Guess what, you won’t see those on your development machine, but they can and do happen in the real world. Full stack developers can work side by side with DevOps. The system should provide useful error messages and logging capabilities. DevOps will see the messages before you will, so make them count. Data Modeling If the data model is flawed, the business logic and higher layers start to need strange (ugly) code to compensate for corner cases the data model doesn’t cover. Full stack developers know how to create a reasonably normalize

Organic search: Organic search is a method for entering one or several search terms as a single string of text into a search engine. Organic search results, appear as paginated lists, are based on relevance to the search terms; and exclude advertisements. Whereas, non-organic search results do not filter out pay per click advertising. Background: The Google, Yahoo!, and Bing search engines insert advertising on their search results pages. The ads are designed to look similar to the search results, though different enough for readers to distinguish between ads and actual results. This is done with various differences in background, text, link colors, and/or placement on the page. However, the appearance of ads on all major search engines is so similar to genuine search results that a majority of search engine users cannot effectively distinguish between the two. Because so few ordinary users (38% according to Pew Research Center) realized that many of the highest

Post-Panda/Penguin SEO Strategy for Bloggers Not in distant past (2011) when Google Panda rolled out, many Bloggers expected it like just another Google algorithm change but honestly it changed the Blogging industry drastically. Every professional Blogger has to re-work on their strategies. One thing, which people have talked about after Panda updates is, SEO is not relevant and now it’s dead and believe it, SEO is not dead and it never will be, SEO is basically a quality guidelines and set of rules a Website and a Webpage should follow to meet Google SEO standards. What is SEO In Post-Panda World? Before 2011, SEO and domain value used to be an important factor but it never used to be ranking factor for complete domain. After panda updates, many things changed and one of them is domain level penalty. Earlier, few shallow and low quality pages only impact particular posts and not complete domain but now in post-Panda SEO world( Post Penguin now ), this has ch

Monolithic Architecture v/s Microservices Recently, there are a lot of discussions happening about microservices in almost all the IT companies. Microservices architecture can be easily understood when we compare it with traditional monolithic architecture. When developing a server-side application you can start it with a modular hexagonal or layered architecture.Almost every enterprise application has a similar kind of layered architecture: Presentation: The user interface. Responsible for handling HTTP requests and responding with either HTML or JSON/XML (for web services APIs). Business logic: The application’s internal business logic. Database access: Almost all applications need data access objects(like SQL or NoSQL) to access DB. Application integration: Quite often, the application needs integration with other applications. This is usually achieved via web service calls (SOAP or REST API), or via messaging.  Despite having a logically modular architect

Using Frameworks to Build Websites and Web Applications Even if you only build websites using Content Management Systems(CMSs) , you've probably heard the word "framework" before. You've probably also heard of a few famous web frameworks, including Flask, Django and Bootstrap. Many experienced web developers build websites using frameworks and often find them easier and enjoyable to use. In this post, we're going to explain what a framework is, and when you might use a framework. If you are just using a CMS, this post will still contain some valuable insights, as many CMS systems can and are built using frameworks. For example, Drupal 8 is current

Core skills for CS and IS students When it comes to computer science (CS) and information systems (IS), one size simply doesn't fit all. These umbrella terms encompass many diverse fields of study and areas of expertise, which eventually pan into very different career paths. With so many avenues, it's tough pinpointing exactly what to teach students before they step out into the real world. Thankfully, there are several core skills that each and every CS student and IS scholar will need to know by graduation; skills that will help them get a head start in the tech industry, no matter which path they choose. Some of them are: Technology must provide business value   Often, with technology, we're lured by the shiny new gadget or software that promises to solve all IT problems. But students need to know that the technology they're using is only a tool, one that provides value. The key is to not implement technology just for technology's sake.