Manage Your Lipids

lipids nut avocado fish oil seed

What’s up ? This is THE stephane ANDRE !!! I watched a Jamcore DZ’s video  and I learned some good stuff.

Lipids help with brain function and hormones production like testosterone. Lipids are also the element that is the most energetic : 1gr of lipid is 9kcal while 1gr of protein or carbohydrate is 4kcal.

Lipids create the fat of living beings, which is why we most often use the word « fat ». There is 4 fat’s type :

  • Harmful fat – These are transformed fatty acids like partially hydrogenated oils. We find them everywhere, that’s why it’s important to have an ingredients label on foods.

  • Bad fat – These are saturated fatty acids

  • Neutral fat – These are monounsaturated fatty acids and they’re very beneficial for health.

  • Good fat – These are polyunsaturated fatty acids that are very beneficial. There are 2 families, omega 3 and omega 6. If your body has too much omega 6, it creats health problems.

Type

Harmful fat

It’s fatty acids that are partially hydrogenated oils that are, for example, in Nutella, candy, cookie, fast food and all pastries. It’s advisable to eat them moderately otherwise they create health problems. And unfortunately the majority of people consume this type of fat too much.

Bad fat

It’s fatty acids that are, for example, in butter, beef, cheese, coconut oil and cold cuts. Overeating this type of fat increases the insulin resistance that can cause diabetes. It also decrease the ability of your body to use this type of fat as energy.

But be careful because not all foods that contain this type of fat are equal. Take the example of coconut oil. Coconut oil is full of lauric acid which has excellent antibacterial and digestive properties for the body. This coconut oil is very rich in MCT (Medium Chain Triglyceride) whick is convenient to use to be shreddedd because it helps the fat’s oxydation and keep your muscle mass.

Fatty acids that are in bad fat have an important role in the testosterone’s production, that why it’s recommended to consume it in average quantitiy.

Neutral fat

It’s fatty acids that are, for example, in olive oil, almond oil, macadamia nut, brazilian nut, avocado and oleic sunflower oil. It’s important to diferentiate between oleic sunflower oil and classic sunflower oil. Classic sunflower oil has a lot of omega 6 and this create a lot of inflammation and other health problems. On the other hand, oleic sunflower oil contains oleic acid. Oleic acid has properties that control cholesterol levels by lowering your bad cholesterol and boosting your good cholesterol. Oleic acid is also good for the memory.

Good fat

It’s essential fatty acids, which means these are fats aren’t produced by your body. That’s why they’re important to consume. A deficiency of these good fats will create health problems. There are 2 families : Omega 3 and Omega 6.

Omega 3 : The source of omega 3 are fatty fish like sardines, mackerel, anchovies, salmon and tuna. There are also plant sources such as flaxseed, chia seeds, brussels sprouts, walnuts. It’s recommended to consume the animal source because they’re richer in omega 3 than vegetable sources (unless your eat huge amount of grain).

Omega 6 : The source of omega 6 are, for example, classic sunflower oil or soybean. All these foods with too much omega 6 are too present in the supermarkets. Omega 6 creates a lot of inflammation problem in your body, while Omega 3 decrease inflammation.

Consumption

lipids nut avocado fish oil seed

According to scientists, there is a ratio for having a healthy body. This ratio is :

  • -4/1 so 4 = omega 6 and 1 = omega 3

  • Or 1/1 so 1 = omega 6 and 1 = omega 3

The majority of people consume 20/1 so 20 = omega 6 and 1 = omega 3. That’s why a lot of people have health problems.

Lipids consumption is related to body weight. For exemple, take a person who weighs 85kg and who is beginner/intermediate :

  • To gain mass, it’s 0.9gr to 1gr per kilo of bodyweigth so 0.9 x 85 = 76.5 and 1 x 85 = 85. Which means between 76.5 and 85gr of lipids per day.

  • To be shredded, it’s 0.5 to 0.8gr per kilo of bodyweight so 0.5 x 85 = 42.5 and 0.8 x 85 = 68. Which means between 42.5 and 68 of lipids per day.

Now, take a person who weighs 100kg and who is a pro :

  • To gain mass, it’s 1gr to 1.5gr per kilo of bodyweight so 0.9 x 100 = 100 and 1.2 x 100 = 120. Which means between 100 and 120gr of lipids per day.

  • To be shredded, it’s 0.9 to 1.5gr per kilo of bodyweight so 0.9 x 100 = 90 and 1.5 x 100 = 150. Which means between 90 ans 150gr of lipids per day. A pro can take 150gr of lipid because he decreases carbohydrates.

These amount of lipids are to be divided between 4-5 meals a day for ease of digestion.

To be shredded, it’s really important to consider MCT

You have to pay attention to MCT (Medium Chain Triglyceride) because it increase your thermogenesis. Thermogenesis is what turns calories into heat and increases your metabolism to burn more fat. MCT helps you to be shredded by keeping your muscles mass as much possible.

It’s recommended to take 2 teaspoons of MCT per day, otherwise you will poop everywhere.

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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.

Label And Format

 data science tableau label format

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

Our bar chart has colors by region but imagines that this bar chart is on a wall of an open space or in a report.

With labels, we can make this bar chart more clear, easier to understand.

In this bar chart, there are all necessary information: representative’s names, regions where representatives make sales and total sales for each representative in Swiss francs.

But, there is a problem. For example, if you ask for someone to say how many sales made Bill. This person must find Bill and see on the vertical axis to the left the value. Here we can see, it’s 1750.

But if we take the James case, we see that it’s between 1000 and 1500. James is far from the vertical axis and it’s difficult to say the true value.

That’s means, all people need to make effort to extract the bar chart’s information.

This it should not be the case because a Data Scientist searches always the best ways to communicate the information. This process is to help people to understand and extract the information in the easiest way.

Start with labels.

« Labels » button allows you to add text information in your bar chart.

data science tableau label format

You will add a label with the SUM(TotalSales) information

To do this, you click on SUM(TotalSales) and press and maintain the key Ctrl or Command on your keyboard and drag and drop SUM(TotalSales) on « Label ».

data science tableau label format

Now you can see the total sales value at the top of each bar.

data science tableau label format

The bar chart is easier to read because there is the total value of sales for each representative.

it’s time to add more information using the labels.

Use the « Rep » information. Click on « Rep », press and hold « Ctrl » key or Command key on your keyboard and drag and drop « Rep » to « Label ».

data science tableau label format

Now you can read the representatives names at the top of the bars.

data science tableau label format

You can also add the region. I’ll show you another way to add « Region » in « Labels ». Click « Region » in « Dimensions » and drag and drop « Region » on « Labels ».

data science tableau label format data science tableau label format

 

But it’s redundant because you can read the representatives names below and the regions at the top of the bar chart.

And each region has its own color. As it’s redundant, we remove « Rep » and « Region » from « Labels » by dragging and dropping out.

data science tableau label format data science tableau label format

 

It’s better, it remains only SUM(TotalSales).

data science tableau label format

Let’s go to the next level, we will publish our labels.

To do this, do a right-click on « Labels » and click on « … » button.

data science tableau label format

It allows you to have your own text. For example write « Sales : » and click on « OK » button.

data science tableau label format

Now you can see that your text appears at the top of the bars.

data science tableau label format

Well, click on « Labels » and click on « … » button.

data science tableau label format

Delete the text « Sale : »and click on « OK » button.

data science tableau label format

We will see now how to format your bar chart. This is the last step before your bar chart is in production.

You will change the labels size. Click on « Labels » and click on « Font »

data science tableau label format

Select « 12 » and bold.

data science tableau label format

Oh, you can do the same thing by clicking on « … » button

data science tableau label format

You have the possibility to change the color but we will keep the color black

data science tableau label format

Now you’re going to change the label type. Right-click on SUM(TotalSales) and click on « Format… ».

data science tableau label format

In fact the labels have their own format and you can change that by clicking on « Label » but all the other thing on Tableau give their format options make a right-click on it.

So when you click on « Format », you’ll see 2 tabs : « Axis » and « Pane ».

Select the tab « Pane » because that’s where the labels of our bar chart.

data science tableau label format

By clicking on « Alignement », you can change the text’s direction of the labels.

data science tableau label format

But what you can’t do with the « Labels » button is to change the digital type.

data science tableau label format

Return on the tab « Pane », we’ll change the numbers in currencies. Click « Numbers » and select « Currency(custom) ». You can also change the currency type in the « Prefix/suffix ».

data science tableau label format

To simplify, you delete 2 decimals in « Decimal Places ».

data science tableau label format

As you can see on my bar chart, the SUM(TotalSales) is vertical at the top of each bar. To change the direction of the label text, click « Alignement » in the « Pane » tab.

data science tableau label format

But there is a problem. Some bars don’t have SUM(TotalSales). To fix this, right-click on each bar and select « Mark Label » and « Alwlays Show ».

data science tableau label format

Now, the bar chart is more understandable.

Let’s put the units in thousands. Click on « Numbers » => « Currency(custom) » => « Units » => « Thousands (K) ».

data science tableau label format

Add a decimal in « Decimal Places ».

data science tableau label format

That’s better, we can see Swiss francs sales for each sales representative.

Look, there’s something you need to know You can’t change the size of the text in the tab « Pane ».

If you click on « Font » and change the size, it will not change anything on your bar chart.

data science tableau label format

This is because the font size in the « Label » button dominates the font that is in the tab « Pane ».

data science tableau label format

Ok, we changed the labels format. Now, let’s change the axes format.

To do this, right-click on the vertical axis and select « Format ».

data science tableau label format

Click on the « Axis » tab and change the text size with « Font » to 12.

data science tableau label format

Then, right-click on the horizontal axis. Selects « Format ».

data science tableau label format

And in the « Header » tab, you change the text size with « Font » to 12.

data science tableau label format

Oooh, do you see ? Mathiew is cut off. To arrange this, enlarge the bar chart by clicking and dragging on the right.

data science tableau label format

Right-click on « Central » in the top axis and select « Format ».

data science tableau label format

And changes the text’s size with « Font » to 12 and bold.

data science tableau label format

Now, look at the top of the bar chart. The « Region/Rep » line is useless because we know that Central, East and West are the regions and the representatives names are at the bottom of the bar chart.

data science tableau label format

To change it, right-click on « Region/Rep » and select « Hide Field Label for Columns ».

data science tableau label format

if you want to improve the title « TotalSales » by adding a space, right-click on the vertical axis and select « Edit axis ».

data science tableau label format

In the « General » tab, add a space in the title and click « OK ».

data science tableau label format

Let’s do one more thing. We’re going to put all the « Total Sales » in Swiss francs. Make a right-click on the vertical axis and select « Format ».

data science tableau label format

Click on tab « Axis » => « Numbers » => « Currency(custom) ».

In « Decimal Places », you put « 0 ». In « Units », you put « Thousand(K) ». In « Prefix/Suffix », you put « CHF ».

data science tableau label format

Well, you did a good job. Now you know how to change the format of the charts in Tableau.

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

-Steph

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

Data Science Underrated Job

data science

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

I know you’ve heard many times « Look this one, this is the job of the future ! ». The simplest thing I can do is explain why it’s interesting to learn about Data Science. This is extremely useful skills for the future.

The principle is that the more data there is, the more work there is for Data Scientists. Let’s look at the amount of data created in the world in the past, present, and estimate for the future.

130 Exabytes have been created by humans since the beginning of humanity until 2005. Ok, you didn’t understand. Don’t worry, it was the same for me. Let’s go back to the source.

Measuring data

measuring data

The source, it’s 1 byte (1B) and 1 byte is the necessary place for a hard drive to hold a letter. For example, the letter « S » = 1 byte (1B).

You go to the next level and you multiply 1 byte (1B) by 1000 which gives you 1 Kilobyte (1Kb). A book’s page contains between 2000 and 5000 letters so we can say that a half of page of text is about 1 Kilobyte (1Kb).

You go to the next level and you multiply 1 Kilobyte (1Kb) by 1000 which gives you 1 Megabyte (1Mb). A 500 pages book is about 1 Megabyte (1Mb).

You go to the next level and you multiply 1 Megabyte (1Mb) by 1000 which gives you 1 Gigabyte (Gb). A human genome (coded) can be contained in 1 Gigabyte (1Gb).

You go to the next level and you multiply 1 Gigabyte (1Gb) by 1000 which gives you 1 Terabyte (1Tb). If you take an HD camera and take a picture every day, every hour for 80 years. All videos can be contained in 1 Terabyte (1Tb).

You go to the next level and you multiply 1 Terabyte (1Tb) by 1000 which gives you 1 Petabyte (1Pb). If you take all trees of Amazonian forest to make paper and you write text on both sides each paper, all this paper represents between 1 and 2 Petabyte (1-2 Pb).

You go to the next level and you multiply 1 Petabyte (1Pb) per 1000 which gives you 1 Exabyte (1Eb). All existing data on planet Earth is contained in 1 Exabyte (1Eb).

More more more data

more data more problems

I think now you understand better how we measure the amount of data in a hard drive. At first, I told you that 130 Exabytes (130 Eb) created by humans from the beginning of humanity until 2005.

In 2010, this increased to 1200 Exabytes (1200 Eb). In 2015, this increased to 7900 Exabyte (7900 Eb). The forecast for 2020 is that this will increase up to 40 900 Exabyte (40 900 Eb).You see how data creation is growing in the world, it goes very very fast.

With a graphic, it’s easier to visualize all that.

data science forecast graph

The blue line on the graph corresponds to the quatitiy that machines (computers) can sore. You see, there is much more data than what computers can store.

The red line corresponds to what Data Scientists can process as data. You see, there is much more data than Data Scientists can process.

Another important point is that the gap between the machines and the Data Scientists will increase over time.

There are very few Data Scientits in the world and because they’re rare, they’re expensive or their salaries are high.

As companies increasingly seek ou Data Scientists, universities and engineering schools are beginning to offer this type of trainining.

The fact that the number of data increase, the companies demand to have Data Scientist to proccess data also increase. This demand is so enormous that it’s expected that in dozen years, everyone will know the Data Science’s basics as the programming now.

I advise you to do research on Data Science, you’ll see, it can be used in any industry, it’s really interesting.

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

-Steph