Find the Flow

Flow- the mental state of being completely present and fully immersed in a task- is a strong contributor to creativity. When in flow, the creator and the universe become one, outside distractions recede from consciousness, and one’s mind is fully open and attuned to the act of creating. Since flow is so essential to creativity and well-being across many slices of life- from sports to music to physics to religion to spirituality to sex- it’s important that we learn more about the characteristics associated with flow so that we may all learn how to tap into this precious mental resource.

According to psychologist Mihaly Csikszentmihalyi, the following three conditions are required for flow (being in the zone):

  1. One must be involved in an activity with a clear set of goals and progress. This adds direction and structure to the task.
  2. The task at hand must have clear and immediate feedback. This helps the person negotiate any changing demands and allows them to adjust their performance to maintain the flow state.
  3. One must have a good balance between the perceived challenges of the task at hand and their own perceived skills. One must have confidence in one’s ability to complete the task at hand.

The purpose of Csikszentmihalyi studies are to discover what makes people happy in general but let’s have a look at how we can apply this in real life.

It can take practice, but you’ll get better at it. Here are the key steps to achieving and benefiting from Flow:

  1. Choose work you love.
  2. Choose an important task.
  3. Make sure it’s challenging, but not too hard.
  4. Find your quiet, peak time.
  5. Clear away distractions.
  6. Learn to focus on that task for as long as possible.
  7. Enjoy yourself.
  8. Keep practicing.
  9. Reap the rewards. .

“To be able to concentrate for a considerable time is essential to difficult achievement.” – Bertrand Russell

 Csikszentmihalyi’s flow model
Csikszentmihalyi’s flow model

Do you want to be a Data Scientist

The road to become a Data Scientist is paved with honing following skills:

  1. Coding Skills (Python or R)
  2. Logical Thinking
  3. Mathematics ( Statistics, Algebra and Calculus)
  4. Business Understanding

You need to work on top 3 skills until you finish your graduation. Business understanding comes with experience and reading books, case studies, research papers etc.

The best free resources to  learn above skills :

  1. Choose your language of interest ( R or Python) and learn them. You can followLearning paths on R and Learning path on Python. Just google them, and check the first link.
  2. Learn Statistics – You can follow Intro to Statistics and Intro to Descriptive Statistics and Intro to Inferential Statistics
  3. Learn Algebra, Calculus – You can follow Algebra I and Probability and statistics  and Differential calculus and Integral calculus
  4. There are lots of MOOCs available on Python and R for Data Science. Take any of them. For example – For R, you can do Analytics Edge by edX.
  5. Look for internships in data science. There are many startups in India who are hiring interns. For example – Analytics Vidhya
  6. Participate in Data Science Competition – Hands on learning is the best way to learn data science. Start participating on Data Hack, Kaggle and evaluate yourself.
  7. Practice, Practice and Practice

Learning Analytics and BIG data in Higher education


The use of Big data analytics in higher education is a relatively new area of practice and research. In context of educational institutions, Every time a student interacts with their university – be that going to the library, logging into their virtual learning environment or submitting assessments online – they leave behind a digital footprint. Learning analytics is the process of using this data to improve learning and teaching. Learning Analytics refers to the measurement, collection, analysis and reporting of data about the progress of learners and the contexts in which learning takes place. Using the increased availability of big datasets around learner activity and digital footprints left by student activity in learning environments, learning analytics take us further than data currently available can. Several reports documents the emerging uses of learning analytics in the United States, Australia and the United Kingdom. For example, in a recent report of January 2016, From Bricks to Clicks, the Higher Education Commission in U.K concluded that analytics had “enormous potential to improve the student experience at university” and recommended that all institutions consider introducing an appropriate learning analytics system.


The analysis of data that is generated via user interactions with digital world is enormously changing how organisations operate, process and compete in an international market. The impact of digital revolution and consequently associated analysis of user data have profound impact on the functioning and existence of corporations across the world. In the higher education (HE) sector this wave of data analytics has been evolving through the concept of learning analytics (LA). This research area has been proclaimed as a revolutionary change for education sector as the results of LA implementations will deal with core education challenges. These comprises of the concerns regarding student employability and academic performance, demonstration of learning and teaching quality, and developing models of personalised and adaptive learning. Although the importance of LA has been widely accepted across all sectors and stakeholders, still the challenge remains as how this venture can be effectively and efficiently rolled out across an educational Institution.






For HGSE Professor Chris Dede, the rise of data science in education research is a potentially transformative development in our understanding of how people learn — and how best to teach them.

Held in June 2015, the second workshop, “Advancing Data-Intensive Research in Education,” focused on discussing current data-intensive research initiatives in education and applying heuristics from the sciences and engineering to articulate the conditions for success in education research and in models for effective partnerships that use big data. The event focused on emergent data-intensive research in education on these six general topics:

Predictive Models based on Behavioral Patterns in Higher Education

Massively Open Online Courses (MOOCs)

Games and Simulations

Collaborating on Tools, Infrastructures, and Repositories

Some Possible Implications of Data-intensive Research for Education

◗ Privacy, Security, and Ethics Breakout sessions focused on cross-cutting issues of infrastructure, building human capacity, relationships and partnerships between producers and consumers, and new models of teaching and learning based on data-rich environments, visualization, and analytics. A detailed analysis of each of these topics is presented in the body of this report. Overall, seven themes surfaced as significant next steps for stakeholders such as scholars, funders, policymakers, and practitioners; these are illustrative, not inclusive of all promising strategies. The seven themes are:


Mobilize Communities Around Opportunities Based on New Forms of Evidence: For each type of data discussed in the report, workshop participants identified important educational issues for which richer evidence would lead to improved decision-making. The field of data-intensive research in education may be new enough that a well-planned common trajectory could be set before individual efforts diverge in incompatible ways. This could begin with establishing common definitions; taking time to establish standards and ontologies may immensely slow progress in the short-term, but would pay off once established. In addition, if specific sets of consumers can be identified, targeted products can be made, motivated by what’s most valuable and most needed, rather than letting the market drive itself.

Infuse Evidence-Based Decision-Making Throughout a System: Each type of big data is part of a complex system in the education sector, for which pervasive evidence-based decision-making is crucial to realize improvements. As an illustration of this theme, data analytics about instruction can be used on a small scale, providing real-time feedback within one classroom, or on a large scale, involving multiple courses within an organization or across different institutions. In order to determine and thus further increase the level of uptake of evidence-based education, a common set of assessments is necessary for straightforward aggregation and comparison across experiments in order to reach stronger conclusions from data-intensive research in education.

Develop New Forms of Educational Assessment: Novel ways of measuring learning can dramatically change both learning and assessment by providing new forms of evidence for decision-making to students, teachers, and other stakeholders. For example, Shute’s briefing paper describes “continually collecting data as students interact with digital environments both inside and, importantly, outside of school. When the various data streams coalesce, the accumulated information can potentially provide increasingly reliable and valid evidence about what students know and can do across multiple contexts. It involves high-quality, ongoing, unobtrusive assessments embedded in various technology-rich environments (TREs) that can be aggregated to inform a student’s evolving competency levels (at various grain sizes) and also aggregated across students to inform higher-level decisions (e.g., from student to class to school to district to state, to country).”

Reconceptualize Data Generation, Collection, Storage, and Representation Processes: Many briefing papers and workshop discussions illustrated the crucial need to change how educational data is generated, collected, stored, and framed for various types of users. Micro-level data (e.g., each student’s second-by-second behaviors as they learn), meso-level data (e.g., teachers’ patterns in instruction) and macro-level data (e.g., aggregated student outcomes for accountability purposes) are all important inputs to an infrastructure of tools and repositories for open data sharing and analysis. Ho’s briefing paper argues that an important aspect of this is, “‘data creation,’ because it focuses analysts on the process that generates the data. From this perspective, the rise of big data is the result of new contexts that create data, not new methods that extract data from existing contexts.”

Develop New Types of Analytic Methods: An overarching theme in all aspects of the workshops was the need to develop new types of analytic methods to enable rich findings from complex forms of educational data. For example, appropriate measurement models for simulations and games—particularly those that are open ended—include Bayes nets, artificial neural networks, and model tracing. In his briefing paper, Mitros writes, “Integrating different forms of data—from peer grading, to mastery-based assessments, to ungraded formative assessments, to participation in social forums—gives an unprecedented level of diversity to the data. This suggests a move from traditional statistics increasingly into machine learning, and calls for very different techniques from those developed in traditional psychometrics.” Breakthroughs in analytic methods are clearly a necessary advance for data science in education.

Build Human Capacity to Do Data Science and to Use Its Products: More people with expertise in data science and data engineering are needed to realize its potential in education, and all stakeholders must become sophisticated consumers of dataintensive research in education. Few data science education programs currently exist, and most educational research programs 5 do not require data literacy beyond a graduate statistics course. Infusing educational research with data science training or providing an education “track” for data scientists could provide these cross-disciplinary opportunities. Ethics should be included in every step of data science training to reduce the unintentional emotional harm that could result from various analyses.

Develop Advances in Privacy, Security, and Ethics: Recent events have highlighted the importance of reassuring stakeholders in education about issues of privacy, security, and ethical usage of any educational data collected. More attention is being paid to explicit and implicit bias embedded in big data and algorithms and the subsequent harms that arise. Hammer’s briefing paper indicates that “[e]ach new technology a researcher may want to use will present a unique combination of risks, most of which can be guarded against using available technologies and proper information policies. Speaking generally, privacy can be adequately protected through encrypted servers and data, anonymized data, having controlled access to data, and by implementing and enforcing in-office privacy policies to guard against unauthorized and exceeded data access.” A risk-based approach, similar to the approach taken by the National Institute of Standards and Technologies in guidelines for federal agencies, would allow for confidentiality, consent, and security concerns to be addressed commensurate with the consequences of a breach.

Goals For Research/PhD

#1 Goal Is To Publish Good quality Articles

The measurement of good quality article is by their publication house or a well known Index. for example in computer science community good articles usually come from ACM, IEEE, Springer, Elsevier , Taylor and Francis publications and the index used is SCI Index.

#2 Goal Is To Stay Motivated

Since a PhD involves diving very deep into a topic, one might expect that learning very complicated stuff would be the hardest part. If you don’t learn fast enough and well enough, you will not finish your PhD. Right? Not true.

That period is called the Valley Of Shit or the Phase 3 of PhD Motivation, the “Crisis of Meaning” . Almost every graduate student goes through this existential crisis.

#3 Goal is to Complete the Plan on time

One, you realise a bit, just a bit, too late that this is not going to work. Seriously, do you really need 5 years to decide you won’t have enough results and papers to defend your thesis?

Two, after 4 years you are almost there, you have enough data to write those two last articles and the introduction of your thesis. It feels so close and obvious you are going to get your PhD title that you decide to start a postdoc or a new job. Excellent Wrong choice, Sir!!!!

#4 Goal is to consider yourself the last authority to judge

One common source of frustration is to ask your PhD supervisors for help and realise they know as much as aunt Martha does. If these brilliant guys can’t answer your problems, how are you expected to answer them?

#5 Goal is not to try doing something BIG

Most of scientists make big contributions after a lifetime of research, not in a couple years.Then why do it a PhD in the first place? Well, you need to start somewhere and a PhD can give you the tools and skills necessary for achieving higher scientific goals.

#6 Goal is embark on the process of Reading, Writing, Networking

#7 Goal is Time mangement

If you haven’t read The 4 Hour Workweek yet, buy it, devour it and apply it to your PhD. It is stuffed with great ideas that you can turn into graduate school advice, it will revolutionise the way you see the world.

  • You want to be effective, not just efficient: being efficient at something unimportant is useless. Being effective at finishing important things makes a big difference.7
  • Pareto’s Law 80/20: focus your efforts in that 20% of tasks that bring 80% of the benefits (like writing papers). Remove the 80% of tasks that only contribute to 20% of the results (like revising constantly your time management system).
  • Parkinson’s Law: set tight deadlines, the last minute rush will activate your creativity. If you decide you can do a task in  2 days, guess what? It will take your 2 days to accomplish it. If you would assign 3 hours to it, you would still finish it.4
  • You are scared, so is everybody else: when talking to other people, giving presentations, applying for that position, it is scary, but everybody else would be scared.
  • Have near-impossible goals: these are the goals that motivate you and that are worth working hard and walking the extra mile. When would you work harder? When you have to prepare a poster for a regional meeting or when you have to give a talk at an international conference in New York? I thought so.


#8 Goal is to Deliver Fast And Often, Get Feedback

Bear with me: done is better than perfect.

Don’t wait till you have the perfect figures or till you are not ashamed of the quality of your work. You need to make progress and you need the feedback of your supervisors to do so.

#9 Enjoy The Ride

Graduate school has many perks that make it a great experience. You will meet interesting people and you will have the chance to explore your own ideas and to be creative.

You have the chance to travel. Get results and present them in conferences. Ask your boss to pay for the trip or apply for a travelling stipend for students. Find collaborators and get them to invite you to visit their lab.


#10 Most important  Goal is to Pimp Your Online Reputation And Grow Your Academic Footprint





How to start your PhD