Professional Certificate in Predictive Data Analytics Applications
-- ViewingNowThe Professional Certificate in Predictive Data Analytics Applications is a comprehensive course designed to equip learners with essential skills in predictive data analytics. This certification program emphasizes the importance of data-driven decision making and how to apply predictive analytics techniques to solve real-world business problems.
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โข Introduction to Predictive Data Analytics: Fundamentals of predictive analytics, data mining, and machine learning. Understanding data sets, predictive modeling, and data-driven decision making.
โข Data Wrangling and Preprocessing: Data cleaning, transformation, and preparation. Handling missing data, outliers, and noisy data. Data normalization, aggregation, and selection.
โข Exploratory Data Analysis: Descriptive statistics, data visualization, and hypothesis testing. Univariate and multivariate analysis. Identifying patterns, trends, and correlations.
โข Regression Analysis: Simple and multiple linear regression, logistic regression, and polynomial regression. Model selection, validation, and evaluation.
โข Time Series Analysis: Autoregressive, moving average, and ARIMA models. Seasonality, trend, and forecasting. Time series data preprocessing and model evaluation.
โข Classification and Clustering: Decision trees, random forests, k-nearest neighbors, and support vector machines. Unsupervised learning and hierarchical clustering. Model validation and evaluation.
โข Neural Networks and Deep Learning: Artificial neural networks, backpropagation, and deep learning. Convolutional neural networks, recurrent neural networks, and long short-term memory networks. Model training, validation, and evaluation.
โข Natural Language Processing: Text preprocessing, tokenization, stemming, and lemmatization. Sentiment analysis, topic modeling, and named entity recognition. Model training, validation, and evaluation.
โข Ethics and Privacy in Predictive Analytics: Data privacy, security, and confidentiality. Bias, fairness, and transparency. Ethical considerations and best practices.
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