Data Science: Your Path to AI & Insights into Data

Go from data chaos to clear insights. Learn Python, Pandas, ML, & Deep Learning for real-world projects.

What you'll learn
- Understand the basics of data science and types of ML (supervised, unsupervised, reinforcement).
- Master descriptive statistics, probabilities, correlation, causality, hypothesis testing and A/B testing.
- Manipulate and transform data with Python: types, lists, tuples, sets, dictionaries, and comprehensions.
- Analyze data with Pandas: read, explore, clean, filter, group, aggregate, and create pivot tables.
- Manage time series efficiently with Pandas for chronological data analysis.
- Perform efficient numerical calculations with NumPy: arrays, indexing, slicing, mathematical operations.
- Create impactful data visualizations with Matplotlib: lines, bars, points, histograms, boxplots, subplots.
- Enhance visualizations with Seaborn: Pandas integration, pairplot, facetgrid, regressions, and heatmaps.
- Understand key Machine Learning concepts: workflow, feature selection, overfitting, and underfitting.
- Apply supervised ML with Scikit-Learn: linear regression, decision trees, random forests, SVM, ANN.
- Manage feature selection and dimensionality reduction to optimize ML models.
- Master imbalanced data and advanced classification techniques.
- Compare and evaluate ML models with relevant metrics and ensemble methods.
- Optimize models via hyperparameter tuning and cross-validation (Scikit-Learn).
- Apply unsupervised ML: K-Means, hierarchical clustering, dimensionality reduction.
- Detect anomalies (outliers) in datasets to improve analysis quality.
- Combine supervised and unsupervised learning for more robust solutions.
- Introduce Deep Learning with TensorFlow and Keras: sequential and functional models.
- Understand Forward and Backpropagation, and activation functions (ReLU, Sigmoid, Softmax).
- Perform hyperparameter tuning for Deep Learning with Optuna.Develop CNNs for image recognition and sequential models (RNN, LSTM, GRU).
- Discover GANs and concepts of generative models.
- Lead a Data Science project from A to Z: loading, cleaning, visualization, feature engineering, ML/DL modeling.
- Data Science Beginners: Anyone wishing to acquire a solid foundation in data science, even without prior programming or statistics experience.
- Data Analysts and BI Professionals: Those looking to enrich their data analysis skills with advanced techniques and machine learning.

Requirements
- No prior knowledge is required. I will guide you step by step through each concept.

Description
Master Data Science — And Go From Simple Code to Real Analysis.

Tired of not knowing where to start with your data, wasting time cleaning CSV files, or not understanding why your model isn't working? This course will guide you step by step — from your first lines of code to your own predictive models.

Whether you're a Python beginner or already have a programming foundation, this course will transform you into a Data Science practitioner. You'll learn to manipulate, analyze, visualize, and model data like a professional.

No more messy scripts, incomprehensible errors, and hours lost on Google. It's time for clear, structured, and efficient Data Science practice.

What you will learn

Understand the essential statistical foundations for data science

Effectively manipulate data with Pandas and NumPy

Visualize data with Matplotlib and Seaborn

Build Machine Learning models with Scikit-Learn

Create neural networks with TensorFlow and Keras

Implement concrete end-to-end projects

Course Structure – What to Expect

Introduction to Data ScienceWhat is data science, why is it essential today, and what are the key skills to master?

Statistical Fundamentals for Data ScienceLearn essential concepts: mean, standard deviation, distributions, correlation vs causality, p-values, A/B testing…

Python for Data ScienceMaster Python data structures (lists, dictionaries, sets, etc.) and learn modern techniques like comprehensions.

Data Manipulation with PandasImporting, cleaning, filtering, grouping, merging… You'll know everything about data preparation.

Numerical Calculations with NumPyCreate and manipulate high-performance numerical arrays, and perform vectorized calculations.

Data VisualizationLearn to create impactful graphs with Matplotlib and Seaborn: histograms, scatter plots, heatmaps, boxplots, and more.

Machine Learning with Scikit-LearnBuild supervised and unsupervised models, test them, optimize them, and understand their performance.

Deep Learning with TensorFlow & KerasCreate your first neural networks, CNNs, RNNs, and even explore GANs to generate your own data.

Final Project: Complete ApplicationA concrete project where you'll implement everything you've learned: cleaning, visualization, modeling, and prediction.

Who is this course for?

Python beginners curious to discover data science

Students or professionals wishing to add a sought-after skill to their profile

Self-taught developers wanting to structure their learning

Anyone wanting to go from simple Excel analyses to real data-driven predictions

PrerequisitesNone! You'll learn everything step by step. All you need is a computer, an internet connection, and your motivation.

Why take this course?Learning Data Science is opening the door to one of the most in-demand professions in the world. But this course doesn't just teach you concepts — it gives you concrete, practical skills, with professional tools, so you can use them today.

So, ready to transform your data into decisions? Join me on this exciting journey — and become a true Data Scientist. See you soon in the course! – Mika

Who this course is for:
- Python Developers: Developers seeking to expand their skills into the field of data science, analysis, and machine learning.
- Students and Researchers: Those who need to understand and apply data science methods for their academic or research projects.
- Professionals in Career Transition: Anyone wishing to embark on a career in data science and master the essential tools of the industry.
- AI and ML Enthusiasts: Anyone eager to understand machine learning and deep learning algorithms and their practical applications.
- Future Data Scientists: Those aiming for a Data Scientist career and wanting to master the technical skills demanded by companies.
- Developers wishing to optimize their solutions with predictive models and AI.
More Info