Full Stack Data Science

In a highly competitive and dynamic business environment, HR role is no more limited to management of workforce. An intensely competitive marketplace demands that you play more roles than one. You have to be a critic, a qualified statistician and a keen observer of how your workforce functions in order to provide valuable insights and analytical input to keep your organization achieve its goals and objectives.  Data is the key to this strategy helping you manage your workforce efficiently and effectively. However, it is imperative to understand that having a large dataset is of little or no importance if you fail to utilise it properly. And the key to efficient utilisation is to first decode what is relevant and useful. But then the million dollar question is how you will find the time and resources to take advantage of the information at your command. This is where the importance of people analytics is realised. What is people analytics? People Analytics is not a passing f...

When we think of IoT applications, growing crops or raising livestock are not the first visuals that come to our mind. According to Markets and Markets, the market for smart agriculture is likely to evolve up to $11.23 billion by the year 2022. The reason for this being farmers are getting more connected as they realise the potential of IoT technologies in allowing to minimise operational costs while still achieving better results. The examples include less water usage, lower livestock losses, and higher crops. Planning to pursue your career in the field of IoT? Join Introduction to IoT program from a reputed institution to learn the fundamentals and applications of the subject matter. The providers of IoT technology continue to create platforms that can communicate, process, and sense accurately measured environmental data to assist improve farm performance. There is a range of technologies behind these IoT platforms that embraces drones, LED lights, energy harvesting, tra...

Machine Learning is a new and exciting field of study that focuses on the development of computer programs that gives it the ability to think like humans and solve problems based on previous experiences.  A truly exciting technology, ML is currently being used in multiple fields and industries like entertainment, videos surveillance, spam filtering, online customer support, search engine result refining, Social Media services, etc. ML enables analysis of massive quantities of data allowing computers to tackle tasks on their own without a need for express commands. Jigsaw academy reviews and ratings will show you as to how this reputable online training institute helps you build your career in the field of Big Data, AI and ML. It is rightly said that best way to learn is to do it. In order to understand the full capabilities and abilities of Machine Learning (ML), you have to get hands-on with it, which is what exactly people interested a career in this fast-growing tec...

When it comes to pursuing a career in machine learning, it is not as simple as learning and getting ahead in their careers. Beginners are often baffled with innumerable learning resources out there. This guide tells precisely where to kick start your expertise towards ML. Step 1: Maths and Statistics The foremost thing is the skill to comprehending problems through a mathematical instinct. It is highly recommended starting with fundamentals in linear algebra, then slowly moving to calculus. It may be difficult to master them at the beginning but given the time and practice with working, these areas will be acquainted and comfortable to work on. Another important subject that follows math is statistics. For any ML algorithm to be understood clearly, statistics is crucial. Hence, fundamental knowledge in stats should be learnt hand in hand. Step 2. Programming For beginners, programming is sometimes appalling to learn. Obviously, it might be daunting and difficult ...

When people want to launch data science careers but haven't yet started, they're in a scenario that's understandably intimidating and full of ambiguity. However, when they follow mapped-out methods that help them get into the field, success becomes easier to visualize and attain. Here are a few steps to get started: 1. Talk to People in the Field                                                                                        Although it's good to read articles written by data science experts or watch YouTube videos of such people, it's best if as...

Data scientists in academia and industry are increasingly identifying the importance of incorporating ethics into data science curricula. Lately, a group of faculty and students assembled at New York University before the annual FAT* conference to discuss the potentials and challenges of teaching data science ethics, and to learn from one another’s experiences in the classroom. This post is the first of two which will encapsulate the discussions had at this workshop. There is common agreement that data science ethics should be taught, but less consensus about what its objectives should be or how they should be pursued. As the field is so promising, there is considerable room for groundbreaking thinking about what data science ethics ought to mean. In some respects, its goal may be the formation of “future citizens” of data science who are invested in the welfare of their communities and the world, and comprehend the social and political role of data science therein. However th...

In a survey organized among 961 students across 18 cities of India, the top 5 executive data science programs have been revealed. The survey offered an instrumental insight into the data science learning of this country. How was it done? A dedicated online questionnaire was developed and the link was sent to over 30 schools offering data science programs, of which 21 retorted within the given time frame. The participants were requested to fill an elaborated form with four main parameters — course content, student experience, faculty, and other features like external collaboration. The Procedure of Information Collection Seven schools were rejected right away due to incomplete data, lack of supporting documents or non-fulfillment of eligibility criteria. The eligibility criteria for this ranking were:   The course should be an abiding program in data science/ analytics (at least 5 months), Should be executed by a university. Processing of Information ...

Data scientists are big data wranglers. They take a massive mass of muddled data points (unstructured and structured) and use their skills in math, statistics and programming to clean, manage and organize them. Then they implement all their analytic powers –contextual understanding, industry knowledge, and skepticism of existing assumptions – to uncover unseen solutions to business challenges. Learning Path to become a Data Scientist Getting Started : The major step of them all is commencing your data science journey. This stage is all about comprehending what data science is and what role a data scientist has to play. Further, it is here you should choose a programming language and tool of your choice. This will allow you to code through what you learn. Learning Basic Maths and Statistics: What are the core theories a data scientist must completely know? That would be mathematics and statistics. Where learning a tool will aid you to perform fast calculations and produce resu...

Organizations of all sizes have realized the potential of data science to drive productivities, mine new insights from years of collected data sets, and else transform their businesses. Today, from Zillow’s home price forecasts to Amazon’s recommendation engines, usages of data science have become more and more prevalent. But while data scientist has been rated the “#1 Job in America” for three consecutive years, according to Glassdoor, there’s still a shortage of talent to fill the massive requirement employers have. Faced with this dire scarcity of talent, business owners who want to make the most of data science can’t depend on half measures and casual recruitment processes. What they really need is- a planned roadmap toward building data science skills and an effective hiring and resourcing plan. In emerging with a roadmap to use data science effectually at your organization, you need first to analyze your precise requirements. The fact is that various businesses, partic...

Big Data as the name suggests involve examining and interpreting huge amount of data to drive meaningful insights and patterns which in turn could help organizations make intelligent business decisions in real quick time. The concept of Big Data is not new; it has been there for many years. Before the advent of complex software systems that could help organizations uncover hidden trends, insights, patterns and correlations, business organizations applied analytics on the data they had gathered to ease their decision making processes. With technological advancements and advent of advanced computing, organizations discovered new ways of analysing data that significantly reduced the analytics time and offered more agility at a reduced cost.  In this article we shed some light on a few examples of how big organizations are leveraging the power of Big Data analytics to gain competitive advantage in the marketplace. Big Data in acquisition and retention of customers No b...

There is no dearth of AI related news making headings. While some is about the surge of evil robots, others visualize a post-work world where AI makes human workers useless. These futuristic stories are amusing;though they can make it difficult to streamline the current state of AI and the manner it can add actual value to our daily lives. The truth is that, today, all AI systems dependgreatly on data. In contrast to what the media would lead us to trust, AI systems don’t work in a void. How these systems are designed, made and maintained controls their success. “Garbage in, garbage out” applies to AI systems as well, especially those supporting business users. Futuristicbusiness and IT leaders are looking for practical ways to use analytics and AI to let externally and internally facing teams to personalize interactions and manage workflow and resource distribution. All AI is not made equal Amongst the attack of tech industry jargon seen over the last few years, eve...

Data science is a rapidly emerging field. Though, there is a substantial gap between the available jobs and the professionals available to fill those jobs. Joining a data science bootcamp is a great way to build your career in the field of data science. These bootcamps usually are of the duration from six weeks to six months. These are intensive programs, which are carefully designed to prepare you for a career as a data scientist. What will You Learn to do in a Data Science Bootcamp Describe the problem It is impossible to solve a problem until you don’t know what it is. This is quite true in data science as if you don’t have any clear idea about what your question is, you are more possibly to choose inappropriate data to solve it. Put more precisely, the basis of a data science process is recognizing accurately, the question you want to answer. Data collection and processing You require the data which is suitable to solve your query. Mostly, when you f...

Artificial intelligence (AI) has now become an integral part of our lives. It’s how Amazon suggests products, Google answers our searches, and Pandora plays another song. AI allows scalable growth, and personalizes customer experiences for marketers. AI is a powerful marketing strategy as it allows marketers to outdo themselves in their roles by interacting with their customers through targeted messaging — all at scale. Here are the five ways AI can assist sales and marketing in finding leads. 1. Support Sales with Appropriate Customer Experiences Customers now expect personalized experiences and interactions through their favorite channel. AI-driven predictive content tools are allowing marketers to be more tactical, while reducing the workload.   These AI-driven marketing programs can examine your website for case studies, white papers, blogs, articles, videos, eBooks, etc. Once the content gathered, AI foresees what will appeal and ultimately convert each audi...