This course focuses on purpose, usage, advantages, processes, concepts of Data Science. It explains how Data Science (DS) works from data elements through relationships and predictions. This course is focused on practice (R programming, tools and libraries) and aimed to become a full-fledged Data Science practioner who can contribute reallife DS projects.
Uderstanding the domain, the data(set), the concepts and life cycle of Data Science, the 6 major steps (from problem to prediction),
Learn statistics that are required for performing data engineering and machine learning operations, data types & operations, central tendency (mean, median, mode), variation (Variance & Standard deviation), skew (quartiles, outliers), distributions (shapes), correlation
Develop profiency to use R for all stages of analytics, R (studio) installation, R langage, vectors, lists, data frames, matrices, factors, data transformations, I/O, packages, libraries (statistics, graphics)
Learn data engineering tools and techniques, data sources, acquisition, formatting, make data valid and reliable, cleansing, transforming, putting them into repository
Types of analytics, Machine Learning, Unsupervised Learning (UL), Supervised Learning (SL), training Data, reinforcement learning, confusion matrix, predictions (bias, variances, errors), linear regression, decision trees, Naïves Bayes, random forest, K Means clustering, Association Rules Mining, Artifical Neural Networks, Support Vector Machines, Bagging, Boosting, Dimensionality Reduction, Classification And REgression Training package ...