Dietary Fats

dietary, fat, oil, fish, avocado, nut, olive

What’s up ? This is THE stephane ANDRE. With my training, I’m interested in nutrition to stay in shape. I read some advice from Arnold Schwarzenegger and I learned some good stuff.

Of the 3 macronutriments, fats are the nutrients that are the most dense in energy. Fat is composed of the same thing as carbohydrates (carbon, hydrogen and oxygen) but the difference is that the atoms are not linked together in the same way. Fat is in plants and animals. Oils are liquid fats. Fats are insoluble in water. Fats are organized in 3 categories:

  • Simple fats (triglycerides)
  • Compound fats (phospholipid, glucolipid, lipoprotein)
  • Derived fats (cholesterol)

Here are the 3 fat’s functions in your body:

  1. Fats are the main source of stored energy (body fat)
  2. Fats help to protect and cushion the major organs
  3. Fats have an insulator effect, preserving body heat and protecting against excessive cold.

Fat is the most dense nutrient in calories 1 pound (453gr) of fat contains 4000 calories while 1 pound (453gr) of protein or carbohydrate contains about 1800 calories.

When you do exercise and stay within your aerobic capacity, it means you don’t run out of breath, your body uses fats and carbohydrates as a source of energy at around 50/50. But if you continue, your body will use more fat than carbohydrates as a source of energy. If you train for 3 hours, your body can use fat to create 80% energy for your body.

As you could read, there is different type of fat: saturated, unsaturated and polyunsaturated. These terms mean the number of hydrogen atoms attached to the molecule. Here is an analogy with a string’s ball so that it’s easier to understand. Saturated fat is like a length of string in a messy clutter. Unsaturated is like a rope with some entanglements. And polyunsaturated is like a rope carefully wrapped without the sign of a tangle. The more fat is saturated (tangled), the more likely it’s to remain in the body and clog the arteries, which increase the risk of heart disease.

There are also other factors. Diets rich in saturated fat tend to increase cholesterol levels in the blood. Health experts advise that 2/3 of your fat intake is polyunsaturated fat.

Saturated fats are found in:

  • Beef
  • Lamb
  • Pork
  • Chicken
  • Shellfish
  • Egg yolks
  • Cream
  • Milk
  • Cheese
  • Butter
  • Vegetable shortening
  • Chocolate
  • Lard

Unsaturated fats are found in:

  • Avocados
  • Cashews
  • Olives and olive oil
  • Peanuts, peanuts oil, peanut butter

Polyunsaturated fats are found in:

  • Almonds
  • Cottonseed oil
  • Margarine (usually)
  • Pecans
  • Sunflower oil
  • Corn oil
  • Fish
  • Mayonnaise
  • Safflower oil
  • Soybean oil
  • Walnuts

Essential Fatty Acids

fatty, acid, saturated, unsaturated, monounsaturated, polyunsaturated, omega,3,6,9,linolenic, linoleic, cla, gla, arachidonic, epa, dha, oleic, lauric, myristic,palmitic, stearic

Essential fatty acids are inevitable in a healthy diet because your body can’t create it itself- That’s why it’s essential that you eat foods containing essential fatty acids. It’s a shame because many bodybuilders have low fat diets and they develop deficiencies in dietary fat. Fortunately there are foods and supplements that provide “good fats” to avoid this extreme. Here are some examples:

Fish oil

Instead of eating low-fat fish, test salmon, trout or mackerel. Fish oil is needed by organs, especially the brain. You can also take fish oils as supplements.

Polyunsaturated vegetable oil

In vegetable oils, there are 2 acids that are valuable: linoleic acid and linolenic acid. Supermarket oils such as corn oil, sunflower oil and safflower oil don’t contain linoleic acid. Soybean oil is the only supermarket oil containing linoleic acid but you need ot pay attention to GMO. For linolenic acid, you can find that in linseed oil, walnut oil, pumpkin seed oil.

Monounsaturated fatty acids

There are the most harmless fatty acids compared to some polyunsaturated fatty acids because they don’t affect your cholesterol or your prostaglandins (regulators of the hormones action). Monounsaturated fatty acids are found in olive oil and macadamia nuts.

Supplements of fatty acid

These supplements contain essential fatty acids from fish oils and other sources.

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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 .

A Pratical Tip To Validate Your Approach

data science tableau check

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

How was the A/B test « Number Of Product » ? Easy or difficult ?

Here is the result I found.

data science tableau check bar chart

I think you noticed there was something bizarre. There is an anomaly. We imagine that the more the client has products, the more the client is satisfied with the bank so this type of clients should stay in the bank.

In the first 2 bars we can see that a client who has 1 product is more likely to leave the bank than a client who has 2 products. But when a client has 3 or 4 products, we see a huge rate of clients leaving the bank.

Look, there is a little bizarre detail. In the 2nd bar, we can’t see the « Exited » label. This is because there is no place in the orange part to put the text. To make it simpler, we’ll remove the label « Exited ». Drag and drop on the « Exited » text label to the outside.

data science tableau check bar chart

data science tableau check bar chart

Perfect, we can read the percentages. On the 1st bar, we can see that among the client that have 1 products, 28% left the bank. On the 2nd bar, we can see that among clients who have 2 products, 8% left the bank. This show us that clients who have 1 products are more likely to leave the bank than clients with 2 products.

And for the next bars, we observe an anomaly. On the 3rd bar, we can see that among the clients who have 3 products, 83% left the bank. On the 4th bar, we can see that among clients who have 4 products, 100% left the bank. We clearly see that there is a problem and we need to do a deeper analysis to understand what is going on .

As a Data Scientist, we need to explain what happens in bars 3 and 4. Usually when a client has 3 or 4 banking products, that means he/she is satisfied and is loyal to the bank. But in our case, it’s the opposite because there is a high rate of client who left the bank. This is the time to do deeper analysis.

The first thing to analyze is the quality of the data. There is a very big anomaly and it may be because there is something insignificant in our data that disturbs the statistics. For example, it’s possible that when the bank selected these clients in this sample, there were very few clients with 4 products and all those clients with 4 products left the bank. Sometimes chance can create anomalies and you have to play attention to these effects of chance because they don’t seem important but they can create false interpretations.

To start, we will check the number of clients with 4 products.

In « Measure », move « Number Of Records » (which gives the number of observations) on « Label ».

data science tableau check bar chart

data science tableau check bar chart

We observe on the first 2 bars than many clients with 1 or 2 products selected for our sample. For clients with 3 or 4 products, we can see that there were fewer clients selected for our sample.

There are 220 clients with 3 products and 60 clients with 4 products. These small number of clients probably explain why we observe these anomalies.

In this sample of randomly selected clients, there are very few clients with 4 products and they all left the bank. In this situation, we can confirm that it’s a chance. When thing like that happen, you have to be very careful not to make conclusion too fast and make misinterpretations.

The conclusion is that a lot of clients have been selected for category 1 and 2. For category 3 and 4, there have been few clients selected so we can’t do accurate statistics. We need to do deeper analyze for these categories of clients with 3 and 4 products.

Now, let’s put the percentage back on the bar chart. Click on the « Back » button.

.

data science tableau check bar chart

Or do a click and drag of « SUM(Number of Record) » to outside.

data science tableau check bar chart

data science tableau check bar chart

We saw that there is an anomaly and what is interesting to do is to have a comment to remember to do a more in-depth analysis of columns 3 and 4.

Right-click between the bar chart’s title and the bars. Select « Annotate » then « Areas… ».

data science tableau check bar chart

A window appears. In this window, you write « Low observation in last 2 categories » and click on the « OK » button.

data science tableau check bar chart

data science tableau check bar chart

Click on the comment and move it on bars 3 and 4.

data science tableau check bar chart

data science tableau check bar chart

The next time you work on this bar chart, you will see this comment that will remind you to seriously analyze client who have 3 and 4 products.

Validate our approach

It’s time to show you how to validate an approach and how to validate the data. For this we will create a new A/B test.

Duplicate this worksheet with a right-click on the « NumberOfProducts » tab and select « Duplicate ».

data science tableau check bar chart

And rename the tab « Validation ».

data science tableau check bar chart

For this tab, we will erase the comment. Select the comment and press the « Delete » button on your keyboard.

data science tableau check bar chart

data science tableau check bar chart

Everything is ready, the idea is to find a variable that doesn’t affect our results. That is a variable that has no impact on a client’s decision to leave or stay in the bank.

Take for example, the variable « Customer Id ». Client’s identification number has no influence on the client’s decision to stay or leave the bank.

We’ll do an A/B test with the last digit of the « Customer Id » and we’ill check that there is the same clients proportion who leave the bank in the 10 categories of the last digit of the « Customer Id ». The 10 categories are the numbers 0,1,2,3,4,5,6,7,8,9.

Let’s g.To start, we will create the variable that contains the last digit of the « Customer Id ». To have this variable, we will create a « Calculated Field ».

Right-click on « Customer Id », select « Create » and click on « Calculated Field ».

data science tableau check bar chart

data science tableau check bar chart

Name the calculated field « LastDigitOfCustID ». In the text field, we use the « RIGHT » function with « Customer Id » in parenthesis to select the last character of the « Customer Id ». In our case, the last character of the « Customer Id » is the last digit.

Here is the code to write in the text field : Right ({Customer Id},1)

data science tableau check bar chart

data science tableau check bar chart

Oooops, you see there is a small mistake => The calculation contains errors.

There is an error in the formula because « Customer Id » is a number variable and the « RIGHT » function applies to a variable of type « STRING ».

To use the « RIGHT » function, we will convert « Customer Id » into a string. We will use the « STR » function with « Customer Id » in parenthesis.

Here is the code to write in the text field

And click on the « OK » button : Right (STR({Customer Id}),1).

data science tableau check bar chart

Now, you can see that our calculated field « LastDigitOfCustID » is in « Dimensions ».

Click on « LastDigitOfCustID » and move it on top of « NumOfProducts » in « Columns ».

data science tableau check bar chart

data science tableau check bar chart

Now we have a new bar chart and we see that for every last digit of the « Customer Id » there is about the same proportion of clients leaving the bank. All these proportions don’t correspond exactly to the average of 20% but these slight variations aren’t important.

Seeing this uniform distribution allows us to validate our data because these data are homogenous.

Conculsion

Here’s how you can check the homogeneity of your data. You take a variable that has no impact on the fact that a client leaves or stays in the bank. The example we did with the last digit of the « Customer Id » is excellent. We were able to verify that in each of the categories taken by this variable, if there was the same proportion of clients leaving the bank. As is the case, we can validate our data.

Imagine another result. When we do the test with the last digit of the « Customer Id », we observe that for one of the numbers, the rate of clients who left is really higher than the average. This shows us that there is a problem in our data because it indicates an anomaly.

You can find other ways to verify your data by using other « insignificant variables » to see if the distribution is homogeneous. But be careful when you select an « insignificant variable » because there may be traps.

Here is an example. If you create a variable that takes the first letter of the first name, the distribution will not be homogeneous. The reason is simple, there are many more people who have a name that starts with the letter « M » than with the letter « Y ».

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

-Steph