L.E.A.P (Learn. Enrich. Accelerate. Professionalize) – A Teradata Graduate Program, 2019

If you are ambitious and have a passion for business, you want to work for a consulting company where your skills and capabilities are constantly challenged, where you have the flexibility to experiment and innovate and are given exposure to multinational clients world over then Teradata Global delivery center is the right place.

Teradata says that it will train you and make you put to practice what you have learned in the universities in real world. At Teradata GDC, the company gives you an opportunity to transform into skilled professionals in your chosen field of interest. It will help you enable to be able to explore your potential and interest in an environment that promotes diversity of thoughts and ideas.

Teradata is looking for diverse, energetic, flexible and culturally aware undergraduate and postgraduate students with excellent problem solving skills and are willing to be part of our 2019 Teradata Graduate program. It is a 1 year program where you will be trained on our tools and technologies in different functions and inducted into our mainstream departments at the end of the program. List below

  • Business Intelligence
  • Data Integration (ETL)
  • QA & Testing
  • Managed Services
  • Application Development
  • Teradata Analytics Applications
  • Advanced Analytics

General Requirements
The candidate should ideally hold a Bachelors/Master’s degree in computer sciences/IT/software engineering/mathematics/statistics/Business administration and the following skills

  • Strong analytical skills
  • Rigorous application of structured thinking
  • Strong communication and presentation skills (English)
  • Discipline to pace own work
  • Effective team working habits
  • Eye for detail
  • Willingness to travel

Specific Requirements
Help build the young talented graduates into professionals who can exhibit the following characteristics

  • Passion– Do great things because we believe in them
  • Future focused– Innovative
  • Grounded Expert– Become the Gurus in our domains
  • Practical– Diligent and hands-on

Teradata takes keen interest in the grooming and development of its graduates. Shortlisted candidates will be called for written aptitude and technical tests followed by interviews.

Courtesy: Teradata

Fully funded summer school at York University Canada



The multi-institutional, interdisciplinary CREATE Program in Data Analytics and Visualization (DAV) at York University in Toronto offers an *all-expenses-paid undergraduate summer school on big data science.

The event includes talks by CREATE DAV faculty and industry experts on current research topics in big data science, as well as hands-on experience in York and OCAD U laboratories. The curriculum reflects the wide range of research areas at CREATE DAV, which includes research on machine learning, data mining, signal processing, computer vision, image processing, computer graphics, virtual human modeling, serious games, natural language processing, human perception & cognition, visualization & design.

The program accepts undergraduate students who are interested in pursuing a career a career in the big data science. It is intended mainly for students who are planning to apply to graduate school in late 2019, and are interested in investigating interdisciplinary research aspects of the big data science. Citizens of all countries are eligible.

Summer School dates are from June 25 until 28, inclusive

The application deadline is March 15, 2019.

The program covers transportation costs (*up to $1,300 CAD), and provides on-campus accommodations and meals.

For details: https://www.createdav.com/apply/



10 top data science and analytics education programs of 2018

By Alison DeNisco Rayome

These universities and institutes offer the best programs for professionals interested in a career in big data and analytics.datata

There’s never been a better time to begin a career in data science: By 2020 the number of annual job openings for all data-savvy professionals in the US will increase to 2.7 million, IBM predicted. And those with data science skills can command an average salary of $139,840 in the US, according to Glassdoor. The massive growth in data has changed the way enterprises operate, and data science has become crucial for making smart business decisions.

But how do you gain this coveted skillset? Analytics Insight Magazine has named the 10 best analytics and data science institutes of 2018, naming the top programs with experienced global faculties that offer real-world experience to their students, according to a press release.

“The featured institutions offer a comprehensive curriculum in Big Data and Data Science, delivered by top-class faculties along with extraordinary industry exposure,” Ashish Sukhadeve, founder and editor-in-chief of Analytics Insight, said in the release. “We congratulate all the ten institutes for providing world-class analytical education and building impactful data professionals.”

Here are 10 analytics and data science institutes that are at the forefront of education in the field.

  1. Carnegie Mellon University’s Heinz College of Information Systems and Public Policy

Carnegie Mellon offers a Master’s program in Business Intelligence and Data Analytics (BIDA), and deploys the latest in analytical education, according to the release.

  1. Cornell University

Cornell offers a unique flagship program in Master of Professional Studies in Applied Statistics (M.P.S.). It also offers undergraduate degrees in statistics and data science, as well as M.S and Ph.D. degrees in these fields.

  1. Great Lakes Institute of Management

Great Lakes Institute of Management is a leading business school in India, which offers a one-year Post Graduate Program in Management (PGPM), a regular two-year Post Graduate Diploma in Management (PGDM), along with weekend and executive programs in analytics, for professionals who want to gain these skills while working.

  1. International School of Engineering

The International School of Engineering (INSOFE) in India is an applied engineering school with a focus on data science and big data analytics education, according to the release. The school offers courses in big data analytics, teaching the latest machine learning and deep learning techniques to solve real-world problems.

  1. New York University Stern School of Business

The NYU Stern School offers a Masters of Science in Business Analytics degree that aims to help executives understand the role of data in decision-making. The program focuses on domain-specific areas such as analytics strategy, marketing, and optimization, according to the release.

  1. Penn State University

Penn State University offers Master in Data Analytics, preparing students to work in positions that require the design and maintenance of big data and data analytics systems with exposure to real-world datasets.

  1. Praxis Business School

India’s Praxis Business School offers a one-year post-graduate program in data science with machine learning and artificial intelligence (AI) capabilities aimed at equipping students with the tools, techniques, and skills to enter the analytics field, the release said.

  1. Saint Mary’s College, Notre Dame

Notre Dame offers a Master of Science in Data Science program provides students with a deep dive into the mathematical and computational skills needed to take on complex data challenges.

  1. University of Chicago Graham School

The University of Chicago offers a Master of Science in Analytics (MScA) program to help students analyze complex datasets and and solve real-world problems, the release noted.

  1. University of San Francisco

The University of San Francisco offers a Master of Science in Data Science (MSDS). This full-time, one-year program involves a rigorous curriculum focused on mathematical and computational techniques in emerging fields


4 Analytics Concepts Every Manager Should Understand

Like many professionals, my job doesn’t require expertise in data or analytics. I’m a writer and editor, so I deal with words, not numbers. Still, nearly every knowledge worker today needs to be a regular consumer of data analysis. For example, I need to understand whether and why articles on having a mid-career crisis outperformed ones on receiving feedback or why pieces with particular headlines get more traffic than others.

I also need to be able to read research on the topics I cover and understand whether the findings in those studies are valid and generalizable, and be able to articulate the findings — and their limitations — to you, our readers.datata

To do all of this, I need a more-than-basic understanding of data analytics. And while the statistics course I took in graduate school was helpful, it didn’t fully equip me to grasp the important concepts and have the conversations I need to around data analysis.

Fortunately, I had the opportunity to talk with some of the best experts in the field — Tom Redman, author of Data Driven: Profiting from Your Most Important Business Asset, and Kaiser Fung, who founded the applied analytics program at Columbia University — about several critical topics when it comes to data analysis. Here are four refreshers from our archives on data analytics concepts that every manager should understand.

Randomized controlled experiments

One of the first steps in any analysis is data gathering. This often happens via a spectrum of experiments that companies do — from quick, informal surveys, to pilot studies, field experiments, and lab research. One of the more structured types is the randomized controlled experiment. Many people, when they hear this term, immediately think of costly clinical trials but randomized controlled experiments don’t have to be costly or time consuming and they can be used to gather data on things like whether a particular customer service intervention improved customer retention or whether a new, more expensive piece of equipment is more effective than a less costly one. In this refresher, Tom Redman helps me understand what it means for a test to be “controlled” and how you make sure it includes an element of “randomization.” The article also addresses questions like: What are dependent and independent variables? And what are the steps to designing and conducting one of these experiments?

A/B testing

One of the more common experiments companies use these days is the A/B test (which is a type of randomized controlled experiment). At their most basic, these tests are a way to compare two versions of something to figure out which performs better. Companies use it to answer questions like, “What is most likely to make people click? Or buy our product? Or register with our site?” A/B testing is used to evaluate everything from website design to online offers to headlines to product descriptions. It’s critical to understand how to interpret the results and to avoid common mistakes, like ending the experiment too soon before you have valid results or trying to look at a dashboard of metrics when you really should be focusing on a few. You can learn more about A/B tests here.

Regression analysis

Once you have the data, regression analysis helps you make sense of it. Of course, there are many ways to analyze the data, but linear regression is one of the most important. It’s a way of mathematically sorting out whether there’s a relationship between two or more variables. For example, if you are in the business of selling umbrellas, you might want to know how many more items you sell on rainy days. Regression analysis can help you determine whether and how inches of rain impacts sales. It answers the questions: Which factors matter most? Which can we ignore? How do those factors interact with each other? And, perhaps most importantly, how certain are we about all of these factors?

Fortunately, regression is not something you typically do on your own. There are statistics programs for that! But it’s still important to understand the math behind it and the types of mistakes to avoid. In this refresher, I explain how regression works and share a common — but often misunderstood — warning against confusing correlation with causation.

Statistical significance

Once you’ve done the analysis, you need to figure out what your results mean, if anything. This is where statistical significance comes in. This is a concept that is also often misunderstood and misused. And yet because more and more companies are relying on data to make critical business decisions, it’s an essential concept to understand. Statistical significance helps you quantify whether a result from an experiment is likely due to chance or from the factors you were measuring.

This is a concept I sometimes struggled to fully understand myself but, fortunately, the average professional doesn’t need to understand it too deeply. According to Tom Redman, who helped out with this refresher, it’s more important to understand how to not misuse it.

While you’re boning up on these four concepts, it would also be helpful to read this overview on quantitative analysis from my colleague, Walt Frick. It is a nice primer on why data matters, picking the right metrics, and asking the right questions from data. There’s also a great chart on correlation vs. causation to help you make decisions about when to act on analysis and when not to.

Lastly, if you’re interested in analytics because you need to consume social science research, I highly recommend this piece from Eva Vivalt, a research fellow and lecturer at the Australian National University. She gives several tips for determining whether the evidence from a study should be trusted.

Data analytics is ultimately about making good decisions. It doesn’t matter what business you are in or what your role is at your company, we all want to — need to, really — make smart, informed, evidence-based decisions.


Amy Gallo, Harvard Business Review