@xfer_torresx: Luis Sánchez ve la historia de venado98 y hell #luissanchez

Nando Torres
Nando Torres
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Region: MX
Sunday 30 November 2025 05:48:59 GMT
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dani_starxd
Dannxx-hpx :
quién es hell? tien como 2 semana que no veo a Luis
2025-11-30 06:16:17
59
laabel23
La Bel 23 :
Las verdaderas batallas las pierde el capi 😆
2025-11-30 13:44:32
9
willy_rv7
willy 🥤 :
gg venado te fuiste 🕊️🕊️🕊️
2025-11-30 06:09:55
30
patroncito212
El patrón :
El venado quiere todas las del capi 😂😂😂
2025-11-30 17:48:54
1
blakardo_2
𝕳𝖆𝖇𝖗𝖆𝖒 𝖘𝖊𝖑𝖆𝖓𝖎𝖙𝖔 :
denme contexto porfavor😣
2025-11-30 06:18:18
5
jairoxxc
Elihu :
e we eso iba a pasar mañana,
2025-11-30 06:21:44
4
axlguel1
Axel Guel :
venado44🕊🕊
2025-11-30 06:37:19
0
dl_rod1
RodrigoSF :
se la detonó ff
2025-11-30 07:00:46
6
tiktokbrian2.0official
🍀𝒯𝐼𝒦𝒯𝒪𝒦𝐵𝑅𝐼𝒜𝒩𝟤.𝟢 :
Todo le gana menos las batallas 😂😂😂
2025-11-30 10:01:42
7
juanledezma.10
Lettuceman10 :
La máquina de F0rnikar 98
2025-11-30 15:30:40
2
eppee4
roodolfot :
eso que le afecta al Capi
2025-11-30 15:23:26
1
jesus.david.ospin5
Chucho Ospino :
Como no si es jovencito
2025-11-30 12:33:20
1
alanom47
AlanOM :
😓
2025-11-30 16:01:23
0
z.2119
z :
😔😔😔
2025-11-30 09:06:39
0
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## Roadmap to Becoming a Data Analyst Here are the key skills required to become a successful data analyst: ### Soft Skills: - **Critical Thinking:** Ability to analyze and evaluate information to make informed decisions. - **Problem-Solving:** Effectively and creatively identifying and solving problems. - **Communication Skills:** Effectively communicating with the team and stakeholders. - **Collaboration and Teamwork:** Working harmoniously with the team to achieve common goals. - **Data Storytelling:** Turning data into understandable and meaningful stories. - **Presentation Skills:** Presenting information and results in a clear and engaging manner. - **Adaptability:** Ability to adapt to changes and new challenges. ### Machine Learning: - **Supervised Learning:** Training a model using known data for accurate predictions. - **Linear Regression:** A predictive model that shows the relationship between variables. - **Logistic Regression:** Used to predict binary outcomes (yes/no). - **Decision Trees:** A model that uses a decision tree to classify data. - **Unsupervised Learning:** Learning from data without specific labels. - **K-Means Clustering:** Dividing data into groups based on shared characteristics. - **Hierarchical Clustering:** Building a hierarchy of clusters. - **Model Evaluation:** Measuring model performance using various metrics. - **Confusion Matrix:** A tool for evaluating classification model performance. - **ROC Curve:** A graph used to evaluate the performance of a classification model. ### Data Visualization: - **Plotly:** A library for creating interactive charts. - **Seaborn:** A data visualization library based on Matplotlib. - **Bokeh:** A tool for creating interactive data visualizations. - **Taipy:** A library for simplifying data analysis and visualization. - **Looker:** A platform for analyzing and visualizing data. - **Matplotlib:** A powerful library for creating static charts. - **Tableau:** Software for data visualization and creating business dashboards. - **Power BI:** A Microsoft tool for visualizing and presenting data. ### Data Wrangling: - **Handling Missing Values:** Dealing with missing data scientifically. - **Data Transformation:** Changing the data format to make it analyzable. - **Data Cleaning:** Preparing data for analysis. ### SQL: - **Basics:** Basic commands for database management like retrieving, inserting, updating, and deleting data. - **Subqueries:** Combining tables and complex data queries. - **Window Functions:** Analyzing data within a specific range of records. ### Python: - **Pandas:** A library for data manipulation and analysis. - **NumPy:** A library for handling arrays and mathematical operations. - **Matplotlib:** A library for creating charts. - **Seaborn:** A data visualization library built on Matplotlib. - **Scikit-learn:** A machine learning library. - **Plotly:** A library for creating interactive charts. - **TensorFlow:** A framework for building deep learning models. - **PyTorch:** A deep learning framework. ### Mathematics & Statistics: - **Probability:** Study of probabilities and related statistics. - **Hypothesis Testing:** Testing scientific hypotheses using data. - **Linear Algebra:** Dealing with vectors and mathematical matrices. - **Calculus:** Study of rates of change. - **Descriptive Statistics:** Describing and summarizing data. - **Inferential Statistics:** Drawing conclusions from data. - **Statistical Analysis:** Applying statistical methods to analyze data.

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