EduCentro

- Python basics: variables, data types, loops, functions, conditionals
- Data science libraries: NumPy, Pandas, Matplotlib, Seaborn
- Working efficiently in Jupyter notebooks

- Importing data (CSV, Excel, databases, APIs)
- Handling missing data and type conversions
- Data normalization, scaling, and transformation techniques

- Descriptive statistics and key insights
- Visualizing trends and patterns (histograms, scatter plots, box plots)
- Correlation and covariance analysis

- Principles of impactful data visualization
- Creating engaging charts using Matplotlib and Seaborn
- Interactive visualizations with Plotly

- Supervised vs. unsupervised learning
- Key algorithms: Linear & Logistic regression, Decision Trees, KNN, K-Means
- Model evaluation metrics: accuracy, precision, recall, F1-score.

- Basics of relational databases and SQL
- Writing efficient queries for data retrieval and manipulation
- Introduction to database management systems (DBMS)

- Overview of big data concepts and Hadoop
- Cloud platforms (AWS, Google Cloud, Azure)
- Introduction to NoSQL databases like MongoDB

- Data collection, cleaning, analysis, and model building - Applying machine learning and visualization techniques - Presenting insights and results

- Mini Project
- Main Project

Data Science

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Data Science

What We Cover

Module 1: Foundations of Python for Data Science +

Module 2: Mastering Data Wrangling and Cleaning +

Module 3: Exploratory Data Analysis Unleashed +

Module 4: Data Storytelling with Visualizations +

Module 5: Introduction to Machine Learning Magic +

Module 6: SQL Essentials for Data Science +

Module 7: Diving into Big Data and Cloud Computing +