Python Data Science Machine Learning

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To put it minimally, if we: Programming Mathematics, Statistics, Machine Learning Personal Abilities Business Knowledge (the most important item, but of course this competence will not be at the initial level) If i open a little: Individual Skills: Excitement!, Curiosity, Ability to Ask the Right Questions, Analytical Perspective, Problem Solving Ability, Effective Communication, Storytelling and Presentation Ability Scientific Foundations: Mathematics, Statistics, Probability, Linear Algebra Programming: Algorithmic Approach, Programming Logic, SQL (databases), NoSQL, Bash Script, R, Python, Scala, SPSS, SAS, MATLAB etc Big Data Technologies: Understanding the Concept of Big Data, Hadoop, Spark, Hive, Impala, DB's, PySpark, SparkR, SparklyR and others. Cloud Technologies: AWS, Google Cloud, Microsoft Azure, IBM etc Statistical Learning(SL): Tidy Data Process and Data Preprocessing (missing data, outlier observation, inconsistency investigations, etc.) Exploratory Data Analysis (Descriptive Statistics, Data Visualization) Inferential statistics (sample theory, Probability Distributions, random variables, hypothesis testing, Bayesian inference, robust methods) Multivariate Statistical Methods (correlation, size reduction (PCA, LDA, Kernel PCA), variance analysis, clustering analysis, factor analysis, fit analysis, path analysis, separation analysis, etc.) Regression Models: Linear regression, logit-probit, m.logit-m.probit, quantile regression etc Resampling Methods (resampling methods: cross-validation, bootstrap) Linear Model Selection and Regularization Linearity and Causality Machine Learning(ML): Regression Models: Multiple Regression, Polynomial Regression, SVR, Regression Trees, Random Forest Regression… Classification: Logistic Regression, K-NN, SVM, Naive Bayes, Decision Trees, Community Learning Methods(bagging, boosting, RF,...,) Clustering: Hierarchical and Non-Hierarchical Clustering Methods (Hierarchical clustering, K-Means) Association Rule Learning (Association Rules: A Priori, Eclat) Text Mining, NLP Reinforcement Learning Deep Learning Model selection (validation test error methods, model performance evaluation, parameter tuning) and Learning Disorders Awareness (underfitting, overfitting, good fitting) Being aware that simple will always be better and the saying “All Models are Bad, Some are Useful” (George E.P. Box) Predictive proximity? Causality? A very good understanding of their situation. If you manage to do these things properly, I wish you success on your way..😉😉

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