Full Stack Data Science

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...