Correlation is the degree and direction in which two variables are related or associated. Therefore, correlation coefficients are used to measure the strength of the relationship between two variables.

Let me tell you about them and their usefulness in #DataAnalytics 🧵

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A correlation can be positive, meaning that the 2 variables move in the same direction, or negative, meaning that they move in opposite directions. When the correlation is neutral (close to 0) it means that the 2 variables are unrelated.

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Two of the most important correlation coefficients are:
▪️ Pearson
▪️ Spearman

and what is the difference between them?

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Pearson's correlation coefficient is a statistical measure of the strength of the linear relationship between two numerical variables.

It is a very useful coefficient , but it is also very sensitive to outliers.

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Spearman correlation coefficient is a statistical measure of the strength of a monotonic relationship between two variables.

Spearman correlation is less sensitive than the Pearson correlation to outliers.

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Processing images digitally with Python is not only a fun task, but it's also very easy to do.

In the following thread🧵, let me explain how you can do it from scratch using simple operations on a matrix.

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First of all, as a context, PNG (Portable Network Graphics) images can naturally be represented by a 2D matrix of pixels.

At the same time, the pixels are made up of 3 RGB components, representing the colors Red, Green Blue.

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Therefore, after converting a PNG image into a matrix, 2 operations that can be performed very easily are:

▪️Flip vertically: invert the order of the columns of the matrix

▪️Rotate 90 degrees: invert the rows by columns, traversing the matrix inversely

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In addition, the illumination of the image can be modified:

▪️Apply brightness: add a constant value to all pixels of the image (e.g. alpha 0.1)

▪️Apply contrast: subtract a constant value to all pixels of the image

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You can also calculate the negative of the image. To do this, subtract 1 from each color (maximum value for each component) and save the absolute value of this subtraction.

The image will look like this (very cool, right?):

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I have been working on 3 free, hands-on Python courses on the following fields:

1⃣ Algorithms
2⃣ Machine Learning
3⃣ Natural Language Procesing

Below are the content and links for each of them. I really hope they will be useful for you to learn more about these topics.

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Content of the Algorithms course:

1. Algorithm's Basics
2. Divide and Conquer
3. Graphs
4. Dynamic Programming
5. Probabilistic Algorithms
6. Similarity Functions
7. Chaotic Systems
8. Cellular

Content of the ML course (work in progress):

1. Data science: basic techniques
2. Machine Learning: fundamentals, unsupervised learning and supervised learning
3. Deep learning: practical examples
4. Natural language processing:

Content of the NLP course:

1. NLP with spaCy
2. Semantic Enrichment of Entities: SPARQL and DBpedia
3. Spell Checker: from scratch and with PySpellChecker
4. Word Embedding with Gensim
5. Relationship between Words: d3.js and force

Any comments, questions or suggestions are welcome.

If you want to contribute to any of these free courses (add new content or translate it to another language), feel free to send a PR.

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Time series: types and their nature

▪️Stochastic: the property of being well described by a random probability distribution.

▪️Deterministic: system in which no randomness is involved in the development of future states.

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Therefore, stochastic time series can only be forecasted in a probabilistic sense.

Whereas a deterministic model will always produce the same result from a given starting condition or initial state.

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A time series (TS) is a series of data points indexed (or listed or graphed) in time order, with equally spaced time intervals.

Let me tell you a little about them, in the following thread 🧵

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Currently, time series analysis is applied in many fields, such as:

🔹Economic forecasting
🔹Inventory analysis
🔹Stock price forecasting
🔹Medical supplies forecasting
🔹Census analysis

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The 4 main components of a time series are:

▪️Level: the average value in the series if it were a straight line.
▪️Trend: the linear increasing or decreasing behavior of the series over time.

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and,

▪️Seasonality: the repeating patterns or cycles of behavior over time.
▪️Noise: the random variation in the series that cannot be explained by the model.

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A time series can be decomposed into its components using the following 2 methods:

▪️Additive decomposition: Level + Trend + Seasonality + Noise

▪️Multiplicative decomposition: Level * Trend * Seasonality * Noise

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One of the most common tasks performed in NLP is the identification of parts of speech (POS).

Therefore, it is useful to know the definition of the basic classes of POS:
1⃣ Noun
2⃣ Pronoun
3⃣ Verb
4⃣ Adjective
5⃣ Adverb
6⃣ Adposition
7⃣ Conjunction
8⃣ Interjection

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1⃣ Noun (NOUN): is the name of a person, place, thing, or idea.

2⃣ Pronoun (PRON): is a word used in place of a noun.

3⃣ Verb (VERB): expresses action or being.

4⃣ Adjective (ADJ): modifies or describes a noun or pronoun.

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5⃣ Adverb (ADV): modifies or describes a verb, an adjective, or another adverb.

6⃣ Adposition (ADP): is a word placed before (prepositions) or after (postpositions) a noun or pronoun to form a phrase modifying another word in the sentence.

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7⃣ Conjunction (CCONJ or SCONJ): joins words, phrases, or clauses.

8⃣ Interjection (INTJ): is a word used to express emotion.

Finally, you can find the list of universal POS tags in the following link: