DATA-SCIENCE.AW1
Learn Data Science from Scratch
Data science made simple. Simple to grasp. And grasp the future with both hands.
- 19 Lecciones interactivas y 172 topics mapped to the official exam objectives
Beginner A tu propio ritmo · 1 año de acceso
19Lecciones interactivas
172Topics
01 / Habilidades que obtendrás
What you will be able to do
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No se requiere tarjeta de crédito
Our Data Science for Beginners course guides you through a hands-on journey from Python basics to machine learning.
Through interactive lessons, you’ll tackle data scraping, visualization, and cleaning. As you progress, you can test your knowledge of ML algorithms, NLP, and real-world project deployment in virtual labs.
So, gear up to become a certified data scientist.
- Python Programming for data science, including NumPy, Pandas, and Matplotlib/Seaborn.
- Data collection techniques like web scraping and API integration.
- Exploratory Data Analysis (EDA) and data visualization to uncover patterns.
- Data cleaning and preprocessing to handle missing values, normalization, and feature engineering.
- Statistics and probability fundamentals, including distributions, hypothesis testing, and Bayes’ theorem.
- ML algorithms, including regression (linear, logistic), k-NN, Naive Bayes, decision trees, and SVMs.
- Dimensionality reduction with PCA, t-SNE, and UMAP
- Unsupervised learning via clustering (k-means, DBSCAN)
- Deep learning basics such as neural networks, CNNs, RNNs, LSTMs
- Natural Language Processing (NLP): text processing, sentiment analysis, topic modeling
- Building recommender systems (collaborative/content-based filtering, hybrid models)
- Database management (SQL, NoSQL) and data storage (warehouses, lakes)
- End-to-end project deployment, from data pipelines to model monitoring
Course Highlights
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19 Lecciones estructuradas Cobertura completa de los objetivos principales del curso
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1 año de acceso completo Aprendizaje a tu propio ritmo, accesible en cualquier momento y en todos los dispositivos
02 / Lecciones y laboratorios
See exactly what you will learn and practice
Plan de estudios
19 Lecciones interactivas · 172 topics01 Introduction +
02 Unraveling the Data Science Universe: An Introduction 11 topics +
- Introduction
- What is data science
- Data science: A fusion of fields
- History and evolution of data science as a field
- The data science process
- A day in the life of a data scientist
- How data science is shaping our world
- Differences between Artificial Intelligence, big data, and data science
- Conclusion
- Points to remember
- Questions
03 Essential Python Libraries and Tools for Data Science 10 topics +
- Introduction
- Setting up your developer environment
- Basics of NumPy
- Pandas for data manipulation
- Matplotlib, seaborn, and Plotly for data visualization
- Jupyter Notebook essentials
- Scikit-learn: Key to streamlined Machine Learning
- Conclusion
- Points to remember
- Questions
04 Statistics and Probability Essentials for Data Science 13 topics +
- Introduction
- Probability theory
- Basic probability concepts
- Conditional probability and Bayes’ theorem
- Discrete and continuous random variables
- Expectation, variance, and covariance of random variables
- Distributions and sampling
- Central limit theorem
- Sampling techniques
- Hypothesis testing
- Conclusion
- Points to remember
- Questions
05 Data Mining Expedition: Web Scraping and Data Collection Techniques 8 topics +
- Introduction
- Sources of data
- Web scraping with Beautiful Soup and Requests
- APIs and Python libraries for data collection
- Ethical considerations during data collection
- Conclusion
- Points to remember
- Questions
06 Painting with Data: Exploration and Visualization 8 topics +
- Introduction
- Exploratory data analysis
- Descriptive statistics
- Data visualization with Matplotlib, seaborn, and Plotly
- Discovering trends and relationships
- Conclusion
- Points to remember
- Questions
07 Data Alchemy: Cleaning and Preprocessing Raw Data 9 topics +
- Introduction
- Handling missing data
- Data transformation and normalization
- Addressing duplication and data inconsistencies
- Feature engineering and selection
- Encoding categorical features
- Conclusion
- Points to remember
- Questions
08 Machine Learning Magic: An Introduction to Predictive Modeling 8 topics +
- Introduction
- Supervised and unsupervised learning
- Essential algorithms and model selection
- Training, testing, and evaluation'
- Overfitting and underfitting
- Conclusion
- Points to remember
- Questions
09 Exploring Regression: Linear, Logistic, and Advanced Methods 7 topics +
- Introduction
- Linear regression
- Logistic regression
- Harnessing regularization: Techniques to rein in your model
- Conclusion
- Points to remember
- Questions
10 Unveiling Patterns with k-Nearest Neighbors and Naïve Bayes 7 topics +
- Introduction
- Understanding the k-Nearest Neighbors algorithm
- Naïve Bayes classifier
- Hyperparameter tuning
- Conclusion
- Points to remember
- Questions
11 Exploring Tree-Based Models: Decision Trees to Gradient Boosting 8 topics +
- Introduction
- Decision trees
- Entropy and information gain
- Tree pruning and optimization
- The power of ensemble methods in machine learning
- Conclusion
- Points to remember
- Questions
12 Support Vector Machines: Simplifying Complexity 9 topics +
- Introduction
- Introduction to support vector machines
- Understanding kernel methods
- SVM for classification and regression roles
- Real-world SVM: From preprocessing to evaluation
- Balancing the bias-variance trade-off in SVM
- Conclusion
- Points to remember
- Questions
13 Dimensionality Reduction: From PCA to Advanced Methods 8 topics +
- Introduction
- Understanding the problem of high dimensionality
- Principal component analysis
- Visualizing high-dimensional data
- Exploring beyond PCA: t-SNE and UMAP
- Conclusion
- Points to remember
- Questions
14 Unlocking Unsupervised Learning 9 topics +
- Introduction
- K-means clustering
- Hierarchical clustering
- Understanding DBSCAN: A comprehensive guide
- DBSCAN and other density-based methods
- Cluster evaluation and validation
- Conclusion
- Points to remember
- Questions
15 The Essence of Neural Networks and Deep Learning 15 topics +
- Introduction
- Deep learning: Beyond conventional machine learning
- Deep learning as artificial intelligence’s game changer
- Data and processing power
- Introduction to deep learning libraries
- The intricate web of artificial neural networks
- Importance of data and feature engineering in deep learning
- Feature crafting versus self-learning
- Overfitting: A deep learning perspective
- Convolutional neural networks
- Recurrent neural networks
- Long short-term memory networks
- Conclusion
- Points to remember
- Questions
16 Word Play: Text Analytics and Natural Language Processing 8 topics +
- Introduction
- Text processing and tokenization
- The transformation journey: From text to features
- Decoding emotions: Sentiment analysis and text classification
- Topic modeling and entity recognition
- Conclusion
- Points to remember
- Questions
17 Crafting Recommender Systems 15 topics +
- Introduction
- Introduction to collaborative filtering
- User-based collaborative filtering
- Decoding item-based collaborative filtering
- Measuring similarities in recommender systems
- Sparsity and scalability in collaborative filtering
- Building your first collaborative filtering systems in Python
- Personalized proposals: Understanding content-based filtering
- Building content based recommendations in Python
- Matrix factorization and SVD in recommender system
- Synergy in recommendation: Hybrid systems
- Crafting a hybrid recommender with Python: Step-by-step guide
- Conclusion
- Points to remember
- Questions
18 Data Storage Mastery: Databases and Efficient Data Management 11 topics +
- Introduction
- Exploring database types: Relational and NoSQL databases
- Diversifying your data storage: NoSQL databases
- Python meets SQL: Mastering database interaction
- Navigating databses in Python: SQLAlchemy, SQLite3, PyMango
- Python data format handling: CSV, JSON, XML, Parquet, Excel
- Unpacking serialization: Moving and storing data efficiently
- Data warehouses and data lakes: A comprehensive guide
- Conclusion
- Points to remember
- Questions
19 Data Science in Action: A Comprehensive End-to-end Project 8 topics +
- Introduction
- Defining a data science problem
- Data collection and preparation
- From selection to evaluation: Charting the model’s journey
- Communication of results
- Deployment, monitoring and maintenance of a model
- Conclusion
- Points to remember
03 / Preguntas frecuentes
Preguntas antes de empezar
Will I learn enough to get a job as a data scientist?+
This Data Science Training for Beginners covers foundational skills (Python, ML, stats), but entry-level jobs may require portfolio projects and additional practice.
Can I take this course if I’m not a programmer?+
Yes, but expect a steeper learning curve. Prioritize the Python basics modules first.
Is there a certificate? Can I add it to LinkedIn?+
Yes! You will receive a shareable certificate of completion, which is great for resumes and LinkedIn.
Will I learn practical applications of data science?+
Yes, The course includes hands-on labs and simulations to help you practice building projects like:
- Predictive models
- NLP applications
- Recommender systems
Keep going
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- 1 año de acceso completo
- Certificado de finalización
No se requiere tarjeta de crédito