Mistake To Avoid During A Presentation

mistake presentation the family oussama ammar

What’s up ? This is THE stephane ANDRE !!! I watched an Olivier Roland’s video  and I learned some good stuff.

2-3 years ago, I participated in Robert Kiyosake’s « Rich Dad Workshop » , it’s the author of « Rich Dad, Poor Dad ». I returned to this workshop a few months ago and the speakers were less dynamic than the last time. Besides, there is one person who made a classic mistake that we make during a presentation. This error is to display a text on a big screen and read it. It’s really boring.

People read fast

speed reading

Reading a text that is displayed on a large screen in front of everyone is a bad idea because it breaks the presentation’s dynamism. This creates a moment of flutter because you’re disconnected from your audience. But, there is an even more scandalous reason that this one. Typically, people read between 250 and 500 words per minute while the average speech rate is approximately 150 words per minute. This means that people have already understood the text before you have read the half aloud. The bottom line is that people have to wait for you to finish reading the text aloud when they have already read everything and that is awful, pffffffff.

Most difficult thing in a presentation is to keep the audience’s attention and reading aloud a text that is displayed doesn’t create any surprise’s effects. If you absolutely have to read a text during a presentation, read this text without displaying it on the big screen. It will not be the most excellent thing in the world but people will discover the content as you speak. In this way, the audience will be more likely to be attentive to what you say.

I don’t know if you know but there is a little trick when we make a presentation with PowerPoint. In PowerPoint, it’s possible to see the text before it appear on the big screen, it’s Presenter View . With this configuration, there is a private monitor (your laptop) and a public monitor (big screen). This allows you to see in advance what the audience will see.

To improve the quality of my presentation, there is a very interesting book by Garr Reynolds « Presentation Zen » . With this book, you’ll make cool presentation.

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-Steph

P.S. If you’re in Miami and you like Caribbean food, go to my cousin’s bistro to eat Haitian food, click here.

Data Science Domains

matrix

I have just enrolled in a Data Science course on Udemy and I learned good stuff.

In Science, there are several domains. In Data Science, it’s the same.

data science domain

Data Science is composed of 3 fields : computer science, math and statistics and domain knowledge. But for some years, this changed a bit. Data Scientists need to have other skills than programming and statistics.

Look at this new diagram :

data science domain

Let’s look at these skills in detail.

Statistics

Data is the basis of the Data Scientists so they must be able to filter the data to have relevant data that will provide them with insights. This allow Data Scientist to build models to classify the population and make reliable forecasts of future events.

Visualization

Do you know the computers langage ? Do you know bytecode like « 00100010100101010110 » ? No and it’s the same for me. It’s for this reason that Data Scientists must have the ability to see through the data and especially show them to others. This is why visualization is an important skill to show the data.

Data Mining

This is the part of the work where the Data Scientist has to make the detective like Sherlock Holmes. It’s in this phase that we must look in the data for insights and abnormalities.

Database and Data process

It’s simple, the Data Scientist cleans the data, stores and processes the data in the database.

Pattern recognition, Machine learning et Neurocomputing

These 3 disciplines help explain to computers how to learn do to a specific task on its own. There are not things I’ll learn but these are interesting disciplines for some business problems.

In our world where competition is increasingly aggressive, technical skills are no longer enough. Here are other skills that Data Scientist need to have.

Communication

communication

Data Scientists need to interact with people everyday. They have to do that because the insights are not just in the data. There are insights that we can only find by talking to people. That’s why it’s important to not afraid to talk to people to ask them questions on a daily basis.

Presentation

This is another type of communication . In this case the Data Scientist doesn’t try to extract information but to explain what he/she found to the people. This is a very important skill because the Data Scientist is the intermediary between insights and people. It’s a bit the data translator, it’s simply explain the content of data.

Domain knowledge

Data Science can be used in any industry. One day you can do research to find fraudulent transactions and another day you can build a compensation model for employees of a medical establishment.

That is why, in what industry you work, you must do research and know quickly the necessary part of the industry. The rest will come naturally. Quickly learn the basics of the industry where you work.

Practice in real situations

Proverb : « It’s by forging that one you become a blacksmith » says everything. This concept is extremely applicable in Data Science.

Programmation

The 2nd basic domain of Data Scientists. The better you talk to your computer and the more efficient you are, the more successful you will be. If you don’t know how to program, learn this from today. Programming has to become a hobby, something you like to do.

Creativity

This is what make the difference between Data Scientist and Data Analysts. To become an excellent Data Scientist, you need to work your creativity. Be curious and you will find insights that nobody would never have found.

Now you know the skills needed to become an excellent Data Scientist. As you see I have a lot to do.

Share this article if you think it can help someone you know. Thank you.

-Steph