Python has become the most used programming language in the world of data analysis, and for good reason: it's readable, versatile, has a huge community, and makes it possible to go from zero to analyzing real datasets in weeks. If you want to enter the world of Data Analytics or Data Science in LATAM but don't know where to start, this guide takes you step by step from installation to your first complete analysis.

You don't need previous programming experience. You just need a computer, an internet connection, and the desire to learn.

Why Python is the language of choice for data analysis

According to the Stack Overflow Developer Survey, Python is the most popular language among developers and data scientists worldwide for more than five consecutive years. In LATAM, the demand for profiles with Python for Data Analytics grew more than 40% in the last year according to reports from LinkedIn and job portals like Computrabajo and GetOnBoard.

The concrete reasons why Python dominates data analysis:

• Clear and readable syntax: Python is designed to be read almost like English. Learning the basic concepts takes days, not months.

• Ecosystem of libraries: pandas, NumPy, matplotlib, seaborn, and scikit-learn are tools specifically designed to manipulate, analyze, and visualize data.

• Integration with AI: The most popular machine learning and artificial intelligence models are implemented in Python, which gives Data Analysts with Python a direct route toward more advanced roles.

• Community and resources: There are millions of tutorials, forums, practice datasets, and open source projects available in Spanish and English.

Step 1: Install Python and set up your environment

Before writing your first line of code, you need to install the right tools. The simplest way to do this is through Anaconda, a Python distribution that automatically includes Jupyter Notebook and the main data analysis libraries.

How to install Anaconda

1. Go to anaconda.com/download and download the installer for your operating system (Windows, Mac, or Linux).

2. Run the installer and follow the default steps. You don't need to change any advanced settings.

3. Once installed, open Anaconda Navigator and click Launch in the Jupyter Notebook section.

4. A browser window will open with Jupyter Notebook — your work environment for writing and running Python code.

Anaconda automatically installs pandas, NumPy, matplotlib, and hundreds of other libraries. You don't need to install anything additional to follow this guide.

Step 2: Understand Jupyter Notebook

Jupyter Notebook is data analysts' favorite work environment because it makes it possible to combine executable code with explanatory text, charts, and results in the same document. According to Project Jupyter, the platform has more than 10 million active users around the world.

The basic concepts you need to understand:

• Code cells: Blocks where you write and run Python code. They are run by pressing Shift + Enter.

• Text cells (Markdown): To add titles, descriptions, and explanatory notes.

• Kernel: The engine that runs your code. If something behaves strangely, you can restart it from the Kernel → Restart menu.

Step 3: The Python fundamentals you need for analysis

You don't need to learn all of Python before starting to analyze data. These are the essential concepts:

Variables and data types

In Python you can store values in variables without needing to declare their type. The most common types in data analysis are: integers (int), decimals (float), text (str), and lists (list). For example: age = 25 or countries = ["Argentina", "Mexico", "Colombia"].

Control structures

if/else for conditions and for for repeating operations are fundamental. In data analysis you'll use them to filter information and iterate over datasets.

Functions

Functions let you reuse code. They are defined with def function_name():. When you start working with pandas, you'll use the library's own functions constantly.

Step 4: Your first analysis with pandas

pandas is the central library for data analysis in Python. Its main structure is the DataFrame — basically a table like those in Excel but much more powerful.

To import pandas and load a dataset, you simply write:

import pandas as pd
df = pd.read_csv("my_dataset.csv")
df.head()  # Shows the first 5 rows

import pandas as pd
df = pd.read_csv("my_dataset.csv")
df.head()  # Shows the first 5 rows

import pandas as pd
df = pd.read_csv("my_dataset.csv")
df.head()  # Shows the first 5 rows

import pandas as pd
df = pd.read_csv("my_dataset.csv")
df.head()  # Shows the first 5 rows

The most important operations with pandas to get started:

• df.shape → How many rows and columns the dataset has

• df.describe() → Basic statistics (mean, maximum, minimum) of the numeric columns

• df.isnull().sum() → How many missing values there are in each column

• df.groupby("column").mean() → Group data and calculate averages

• df[df["column"] > 100] → Filter rows according to a condition

To practice with real datasets from day one, you can use the ones available for free on Kaggle.com — the most popular data science platform, with thousands of public datasets on every topic imaginable.

Step 5: Visualize your data with matplotlib and seaborn

Numbers alone don't tell stories. Data visualization is what transforms a technical analysis into information that any stakeholder can understand.

To create your first bar chart:

import matplotlib.pyplot as plt

df.groupby("category")["sales"].sum().plot(kind="bar")
plt.title("Sales by category")
plt.ylabel("Total sales")
plt.show()

import matplotlib.pyplot as plt

df.groupby("category")["sales"].sum().plot(kind="bar")
plt.title("Sales by category")
plt.ylabel("Total sales")
plt.show()

import matplotlib.pyplot as plt

df.groupby("category")["sales"].sum().plot(kind="bar")
plt.title("Sales by category")
plt.ylabel("Total sales")
plt.show()

import matplotlib.pyplot as plt

df.groupby("category")["sales"].sum().plot(kind="bar")
plt.title("Sales by category")
plt.ylabel("Total sales")
plt.show()

seaborn is built on top of matplotlib and produces more elegant statistical charts with less code. For a heatmap of correlations between variables:

import seaborn as sns

sns.heatmap(df.corr(), annot=True, cmap="coolwarm")
plt.show()

import seaborn as sns

sns.heatmap(df.corr(), annot=True, cmap="coolwarm")
plt.show()

import seaborn as sns

sns.heatmap(df.corr(), annot=True, cmap="coolwarm")
plt.show()

import seaborn as sns

sns.heatmap(df.corr(), annot=True, cmap="coolwarm")
plt.show()

With these two charts you can effectively communicate patterns, trends, and relationships between variables in presentations or reports.

Your first complete project: analyzing a real dataset

The best learning is what's done with real data. Here we propose a complete practice project that you can include in your portfolio:

1. Download a dataset of sales, salaries, or reviews from Kaggle or Google Dataset Search.

2. Initial exploration: Use df.head(), df.describe(), and df.isnull() to understand what you have.

3. Data cleaning: Remove or impute missing values, correct data types, remove duplicates.

4. Descriptive analysis: Calculate averages, medians, distributions. Identify outliers.

5. Visualizations: Create at least 3 charts that tell a clear story about the data.

6. Conclusions: Write in the notebook what you found and what business decisions could be made with that information.

Save the notebook on GitHub and you'll have your first data analysis project ready to show. If you want references on how to build a solid portfolio, you can also read how to build your Data Science portfolio to get a job in Argentina.

Recommended Coderhouse training

If you want to learn data analysis with structure, hands-on projects, and real-time mentorship, Coderhouse has programs designed to take you from zero to the job:

• Introduction to Artificial Intelligence Course: Ideal for understanding how Python and data connect with the world of AI. Beginner level, no previous knowledge required.

• AI Engineering Course: For those who already have a base in Python and want to make the leap to building solutions with language models and machine learning in production.

• Full Stack Development Career: If you want to combine data analysis with the development of applications that consume that information, this career gives you the most complete profile in the market.

Frequently asked questions

Do I need to know advanced mathematics to learn data analysis with Python?

Not to start. Basic descriptive statistics (averages, medians, percentages) is enough to begin. As you advance and get into machine learning, you will need linear algebra and probability, but it's something you can learn in parallel with practice.

How long does it take to learn Python for data analysis from scratch?

With a dedication of 1-2 hours daily, in 3-4 months you can reach a functional level to do basic analyses and build a portfolio of projects. For junior-level positions in the industry, most professionals estimate between 6 and 12 months of constant practice.

What is the difference between Data Analyst and Data Scientist in terms of Python use?

The Data Analyst uses Python mainly to clean data, explore datasets, and create visualizations. The Data Scientist goes further: builds predictive models, works with machine learning, and takes models to production. Python serves both roles, but the level of technical depth is greater in Data Science.

Can I get a job as a Data Analyst just knowing Python and pandas?

In junior positions, yes. The most common requirements in LATAM for entry positions include Python with pandas, basic SQL, Excel handling, and at least one visualization tool like Power BI or Tableau. Python + pandas positions you well for those searches.

Is Jupyter Notebook the only tool for working with Python in data?

No, but it's the most popular to start because it combines code, text, and charts in the same document. Popular alternatives are Google Colab (free, no installation), VS Code with Jupyter extensions, and PyCharm. Large companies also use Databricks or cloud notebook platforms.