-Environment set-up and installation
-Jupyter Notebooks
-Optional virtual environment
-Python crash course
-Numpy(arrays, indexing, operations)
-Pandas(series, dataframes, missing data, groupby, merging joining & concatenating, operations, data input & output)
-Data Visualization(Matplotlib)
-Matplotlib exercises
-Seaborn(distribution plots, categorical plots, matrix plots, regression plots, grids, style & color)
-Pandas built-in data visuallization
-Plotly & Cufflinks
-Geographical plotting(choroplth maps)
-projects(calls, finance)
-Introduction to Machine Learning(regression, bias variance trade-off, logistic regression theory, KNN, Decision tree & random forest, SVM, K-means algorithm, PCA, recommender system, NLP)
-Big data overview(spark, AWS account set-up, EC2 instance set-up, PySpark set-up, lambda expression, RDD transformations & actions, Neural network theory)
-Deep Learning overview(TensorFlow basics installations & basics, MNIST with multi-layer perceptron, contriblearn)
-TensorFlow project