Tech Career

When it comes to landing the data science job of your dreams, you only get one opportunity to make a first impression. You need to make it count! How can you do that? It’s simple: A concise and targeted data science resume. When written effectively, this document sets you apart from other candidates and conveys exactly what you can offer and why. These are the 7 quintessential factors one must include to devise the Perfect Data Science resume. 1. Contact information Be sure to include your full name, credentials, address, phone number, and email address. If you have a professional presence on LinkedIn, Twitter, GitHub, or another social media site, consider providing links to those as well. Make it as easy as possible for hiring managers to get in touch with you. 2. Professional summary Your professional summary should be no more than three or four sentences stating the following: Years of experience Primary areas of data science expertise (e.g., data mining, data

“A data scientist is someone who is better at statistics than any software engineer and better at software engineering than any statistician”. Found at the cross section of business and information technology, a data scientist is a professional with the capabilities to gather large amounts of data to analyze and synthesize the information into actionable plans for companies and other organizations. We’ve listed a breakdown of the data scientist skills that are essential for career aspirants in the realm of data and analytics management. Business skills: - Interpersonal skills and superior communication - Ability to meet deadlines and manage project delivery - Excellent report-writing and presentation skills - Critical thinking and problem-solving capabilities Technical skills: Proficiency with the programming languages of R, SAS, Python, MatLab, and Java:  You should know a statistical programming language, like R or Python (along with Numpy and Pandas Libraries),

Data Science has become one of the most demanded jobs of the 21st century. It has become a buzzword that almost everyone talks about these days. But what is Data Science? In this article, we will demystify the myths surrounding Data Science, the role of a Data Scientist and have a look at the tools required to master Data Science. You Need to have a Computer Science Background Most Data Scientists may be well versed at coding and might be having Experience in Computer Science, or Maths or Statistics. That does not mean that people from other backgrounds can’t pursue a career as a Data Scientist. One thing to keep in mind is that these people from these backgrounds may have an edge, but that’s only in the initial stages. You just need to keep up the dedication and hard work and soon it will be easy for you as well. Data Scientists would be replaced by AI soon. Data Scientists alone cannot solve everything and it’s not possible for AI to do that either. So, if you’re one of

Data analysts   are required to possess sharp technical skills complimented by strong industry knowledge. They require a complete understanding of relationships that exist among the organization’s various databases and data sources to be effective. As they acquire information using complex query statements and advanced database tools and techniques. I've listed the skills a Data analysts are required to possess and the way an individual can go about enhancing them. 1. Machine learning Machine learning combines data science, math, and software engineering, so it requires an extensive skill set to learn. Machine learning skills include computer science fundamentals, programming, probability and statistics, data modeling and evaluation, algorithms and libraries, and software engineering and system design. Resources to learn machine learning:  Kaggle   has a community of data scientists and machine learning engineers who work together to publish datasets, build mode

Big Data & its implications on business has often been compared to an iceberg which has more than 90% of the mass submerged under water (and only 10% visible). Hence, designing information & data systems so as to cater to Big Data ; and deriving meaningful insights from the same is not a short-term project or engagement. Rather, it’s a long-term strategy & governance wherein the business would reap benefits in the long run. In fact, there has been multiple studies carried out in the way businesses strategize Big Data implementations. As shown in the figure above; businesses leveraging the power of Big Data Analytics have realized 5 to 6% improvement in performance; while they foresee an improvement of 26% over the next three years. How does Big Data affect decision-making for enterprise organizations? There are multiple business-cases wherein enterprises leverage the power of Big Data Analytics in making more relevant and actionable decisions. Now, the key

Data driven decision making is one of the most trending optics of interest & focus in business organizations; and multi-national corporates are investing big-time in devising their information infrastructure so as to store & process transaction data; and massage them in a way so as to extract valuable business insights from the underlying data structure. Before coming to the application of Big Data in decision making ; it is imperative to understand the concept of Big Data, and how are organization leveraging this data to derive insightful inferences on business & market prospects. What is Big Data? How relevant is it for businesses? The term ‘Big Data’ refers to exceedingly large datasets which can be computationally analyzed to derive relevant trends, patterns & interactions – which may in turn enable valuable descriptive analytics (i.e. a picture of the past & present) as well as predictive analytics (i.e. a picture of the foreseeable future). Th