This 400 hours course is the perfect track for beginners with no coding background. Through an accelerated intensive learning experience, you will gain a solid knowledge of data science with Python.
Without prior experience, you will soon be able to work with multiple data sources, create compelling visualizations, perform valuable exploratory data analysis, and implement machine learning models. The course covers the most important tools used in the industry to help you build solid foundations, with an emphasis on learning by doing and our project driven approach will help you build a portfolio as you learn. Our instructors have deep practical and theoretical knowledge on the material covered.
We bring you the technical skills you need to join a growing tech startup or launch your own data-science based project. Last but not least, you will enjoy the strong family-like atmosphere in the space
Syllabus:
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01 – Python Foundations : data types and structures, control flow, functions and modules, OOP, Git and Github.
02 – Data collection : interactions with files, websites scraping, communication with APIs, database queries, regular expressions, big data with PySpark.
03 – Data analysis and visualization: Numpy, Pandas, Matplotlib, Seaborn, Tableau, exploratory data analysis (EDA), introduction to statistics.
04 – Machine Learning with Scikit-Learn: concepts and principles, classification, regression, train-test, Naive Bayes, cross-validation, feature selection, hyper parameters, PCA, unsupervised clustering, unsupervised dimensionality reduction, recommandation, deployment, regularization, anomaly detection, customer churn, random forest, decision trees, fraud detection, gradient boosting, times series.
05 – Natural Langage Processing (NLP) with NLTK/spaCy/Gensim/AutoML: preprocessing, tokenization, n-gram, lemmatization, latent semantic analysis, topic modeling, entity sentiment analysis.
06 – Deep Learning Introduction with Tensorflow: perceptron, multi-layer, convolutional neural networks, Docker and AWS deployment.