You feel two things: important because you are a PhD student and scared of the PhD journey ahead. In fact, the more important you feel because you are pursuing a PhD degree and nobody else in your circle of life is doing the same, the more scared you become. Vice versa, the more scared you are of the journey, the more you feel the need to pump up your self-confidence.

You are annoying, do you know that? You are only learning how to do your data analysis, and are completely lost in the jungle of all these methodologies, theoretical approaches, previous studies, and their endless intertwined little traps where you keep falling into. However, because you are not accepting of the fact that you do not know and are only learning, you cover this up and mask it into being overly self-certain. You question your supervisor when there is no need to, take advice personally and get upset of it. The frustration that should be directed at yourself, you place at your supervisors.

Dear younger me: your supervisors are only humans. Keep in mind that they are actually doing very well. You tend to see only what they are not doing, such as writing any of your thesis for you or interacting with you as if you are a true colleague. You suffer because you are slowly beginning to understand that you are not yet at their academic level of understanding. They know it -and they show it. Please be aware of the fact that they are not only reading your scribbles and commenting them both in person and in email, but they are also dealing with your attitude -ignorance combined with all possible attempts at hiding your slowly growing understanding that you are ignorant. Meaning, you, my dear, are a Besserwisser.

The sooner you learn to be humble, the better. Admit that you are only learning and do not yet know anything. Stay in that place of openness and curiosity and put your ego aside. That is the only way to ever truly learn. There will be many ways you will be humbled along the road, and you should take them as blessings, not as setbacks or punishments. When your article is not accepted in the journal you had wanted to publish it, when someone really challenges you at your conference presentation, and when your supervisors tell you to re-write it all (again!), take these as normal parts of the path and also as possibilities to get rid of your attempts to be perfect in a way that, in doing research, is far from perfect. Embrace all chances to admit you have taken the wrong approach, misunderstood something, tried a method that will just not work, or spent a lot of time reading something that is not useful (or that you have not even understood in the first place).

Dear younger me, when you stop acting like you already know everything, you start learning. The PhD journey becomes real -messy, fun, frustrating, adventurous. When you not only know that you don’t know but are also willing to show it instead of trying to defend your ground, your supervisors will respect you more, not less, and your work can proceed in unknown ways. The more you get rid of the need to know, the more space you open up for learning, taking unfamiliar perspectives, and doing things in a way that comes from the need to understand instead of the need to  know better.

So dear younger self, embrace a not-knowing -attitude and let it show. You will have the chance to be a know-it-all, if you still want to, later on, after you have graduated and other people (perhaps your own prospective PhD students) are willingly giving you the position of someone who indeed knows better.

About Heidi Toivonen: I am a Finnish clinical psychologist with a wide experience from a variety of different health and social care and medicine settings. Lately, I have been working with developing services that support employment, such as vocational and career coaching. My PhD research concerned the construction of agency and its lack, nonagency, in psychotherapeutic conversations. In the future, I aim to continue doing research and teach. My research interests include e.g. national stereotypes, stereotypes related to researchers in different fields of science, agency of patients with serious illness, and user’s sense of agency in human-computer -interaction.

More information about Heidi Toivonen:

• Link to PhD dissertation: https://jyx.jyu.fi/handle/123456789/65808
• Connect with Heidi in LinkedIn

If You Want to Be Brainy Start With Humility
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This letter ( If You Want to Be Brainy Start With Humility ) is part of the collection “letter to the younger self” and has been written for helping the “new generation of students” learning from who was there before. You can see all the letters at the following link:

https://francescolelli.info/category/letter-to-the-younger-self/

The post If You Want to Be Brainy Start With Humility appeared first on Francesco Lelli.

It is a collections of “digital messages in a bottle” written by students that successfully graduated. Some are friends and colleagues, have been my students.

Hopefully, they followed some of the advises that you are going to find in this blog. Independently of this fact they now are moving to the next chapter of their life.

If you had a successful defense, I would like to ask you to write a letter to the “younger self”. In particular, I encourage you in imagining to talk to yourself as you were when you started doing your thesis.

With this letters you will be able pass some of your experiences to the “new generation” of young bachelor and master students. In this way you will be able to motivate other students in continuing your work and at the same time the “new generation” will have a chance to not repeat the same mistakes that the you did. All that they have to do is to read your letter and the one of your fellow collthese letters.

If you plan to read or write one of these messages in a bottle you also may want to listen to following song. It will set the mood for the task:

Hope you will appreciate the opinions and practical suggestions that you will find in the letters to the younger self. Think about the struggle of the student that wrote the letter, read it carefully as is talking about the journey that you are currently doing or that you did. I am sure that it will help you in writing a better thesis.

Browse around and if you see something that you like send them a message. I am sure that they will appreciate!

Where can you find the letters? Here:

Letters to the Younger Self

Do a better #thesis by reading these letters to the #younger self!
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I hereby want to share an interesting way to get access to an extensive data set in the field of Altmetrics. Several well developed Altmetric data providers collect a variety of metrics, thereby covering a large part of social attention that papers receive. One of these platforms is Altmetric.com.

Info@altmetric.com

In order to get access to the data send an e-mail to the address mentioned above. Tell them you are interested in using the data and they will send you the ‘Altmetric Data Researcher Access Application Form’. After filling this out, you will get access to the data within a couple days. You could also look on their page and download the request form, I think this is the correct one, see below:

https://www.altmetric.com/audience/researchers/

The founder of the platform is Stacy Konkiel, who is always willing to help and a very pleasant woman. For any questions related to the data I think you should contact her.

Good luck on your thesis!

Mark Stoevelaar

This letter is part of the collection “letter to the younger self” and has been written for helping the “new generation of students” learning from who was there before. You can see all the letters at the following link:

https://francescolelli.info/category/letter-to-the-younger-self/

Other post related to altermetrics include the following:

• https://francescolelli.info/generic/scientometrics-and-scientific-citations-in-patent-a-database/
• https://francescolelli.info/thesis/promotion-of-scientific-work-thesis-proposal/

The post Accessing Altermetric Data: a quick how to appeared first on Francesco Lelli.

Firstly, make sure you understand what it takes when starting to program in a programming language. If you do not have any experience yet, using python probably is the best way to go. Before deciding whether to use a certain language, it is advised to find similar codes to the one you intend to make and to replicate these codes. This can be a great way of practicing and determining whether you are able to actually work with the programming language. There is plenty of environments in which this can be done, for example using TensorFlow. For my thesis, I used TensorFlow given the simplicity of using it and the great amount of documentation available. A great source for finding examples of codes is GitHub.

Secondly, make sure that you verify whether it is possible to come to your desired outputs with the machine learning algorithm you intend to use. For example, determine whether you want a categorical or continuous variable as output and choose the appropriate machine learning technique for getting to this output.

Thirdly, and probably most importantly, make sure that the data you want to use is available. And, make sure there is enough data. When using data from financial statements, using the Wharton Research Database may be a good way to go. You can get free access to this database through Tilburg University. Moreover, carefully consider how much time you need to prepare the data. For example, using data from companies from many different industries or using data from many different years may be a pain. Therefore, try to be as consistent as possible in collecting your data. Moreover, start with making a framework on what (meta)data you need. For example, data such as company information, dates of collecting data, etc. In case you find later in your research that you lack some data, this framework makes it relatively easy to repair your mistakes.

Fourthly, developing machine learning algorithms and being able to explain what actually happens in these algorithms requires some statistical and mathematical knowledge. Make sure you read into what is happening in the algorithm, or make sure you have access to the right people to explain this to you. There are quite a few standardized packages for e.g. neural networks (e.g. through Scikit). However, you may still be asked to explain what happens in the neural network.

Lastly, when you compare your algorithm to other algorithms, it is important that you measure the performance of your model in a similar way as done in the measurement of the other models. Otherwise, your comparison may be inaccurate. For example, F1 is generally considered to better measure the ability of the model to discriminate than e.g. hit ratio. Reading into the meaning of these measurements and choosing the appropriate one may be fundamental to your research.

Good luck on your thesis!

Tobias Descamps            
https://www.linkedin.com/in/tobiasdescamps/

This letter is part of the collection “letter to the younger self” and has been written for helping the “new generation of students” learning from who was there before. You can see all the letters at the following link:

https://francescolelli.info/category/letter-to-the-younger-self/

The post Machine Learning for Financial Applications appeared first on Francesco Lelli.