*Welcome to All Data Science.*

It is a **long and hard way** to become a data scientist. If you do some research and try to put together everything you need to learn data science you will end up with a list similar to the following:

**Mathematics**(Linear Algebra, Calculus, Probability, Inference…).**Computing**(Algorithms, Programming, Databases, Web…).**Machine Learning**(Supervised Learning, Unsupervised Learning, Reinforcement Learning…).**Big Data.**(MapReduce, Unstructured data…).**Visualization.**(Networks, Charts, Maps…).

And we could keep expanding each of these topics in more subtopics.

Even if you have knowledge of some of these fields, it’s normal to feel a bit **overwhelmed**:

*Where do I start?**Do I have to start learning statistics or programming?**Is it better Python or R?*- Or even,
**What is really data science?**

Here you will find a guide to help you answering to these questions. It will still be long and hard, but **you won’t feel lost anymore**.

*Please note that this page will be updated with new posts and resources. Subscribe to our email newsletter to receive the latest updates directly to your inbox.*

### Start index:

#### 1. Introduction – Resources

2. Mathematics – Statistics

3. Computing – Tools

4. Machine Learning – Data Mining

5. Big Data – Cloud

6. Visualization – Business

## 1. Introduction – Resources

Congratulations on taking the first step to become a data scientist. In this section you will find the **introductory posts and resources** you need to check to start off on the right foot.

The **simplest definition** of Data Science is the following one:

Data Science is the extraction of knowledge from data.

Another one by Simply Statistics:

Data science is the process of formulating a quantitative question that can be answered with data, collecting and cleaning the data, analyzing the data, and communicating the answer to the question to a relevant audience.

It is important to note the difference between **knowledge** and **data**. We can find a **more detailed description** in the post **What is Data Science?**

Once you are sure you know what is the meaning of data science, we recommend you to read the following selection of introductory articles:

Read **all the posts** in the **Introduction – Resources** category.

## 2. Mathematics – Statistics

Mathematics is one of the basis of Data Science. Most of the machine learning techniques require a good knowledge of **linear algebra** and **calculus**, but even more important is the field of **statistics**, as it is the basis of Data Analysis and Data Science.

In this section you will find the posts related to these fields.

### Selected posts

- Basic statistics for Data Science.
- Basic linear algebra for Data Science.
- Basic calculus for Data Science.

Read **all the posts** in the **Mathematics – Statistics** category.

## 3. Computing – Tools

The use of computing is what separates the traditional statistician and business analyst from the data scientist. Every data scientist needs to know **at least one programming language** to develop its analysis.

Here you will find the posts that will help you developing your toolbox.

### Selected posts

- The Data Scientist’s toolbox.

Read **all the posts** in the **Computing – Tools** category.

## 4. Machine Learning – Data Mining

Machine Learning and Data Mining is what separates basic descriptive analysis from more complex and useful data studies. **Prediction** and **classification** are just an example of what can be achieved with machine learning and data mining techniques.

### Selected posts

- What is Machine Learning?
- What is Data Mining?

Read **all the posts** in the **Machine Learning – Data Mining** category.

## 5. Big Data – Cloud

When it comes to data, sometimes **more is better**. But you have to be aware, actual big data problems need to be approach with **different techniques**.

### Selected posts

- What is Big Data?

Read **all the posts** in the **Big Data – Cloud** category.

## 6. Visualization – Business

A Data Scientist should be able to **effectively communicate** the findings of an analysis to **non-technical** people, and knowing how to **visually** present the data and the results is a must to achieve this.

In this section you will find the posts related to visualization and communication in the business environment.

### Selected posts

- What is Visualization?

Read **all the posts** in the **Visualization – Business** category.

In our resources page you can find **a selection of the best resources classified by type of resource**.

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