The Data Science and Data Mining Certified Course is designed to equip learners with in-demand skills to analyze, interpret, and derive actionable insights from large volumes of structured and unstructured data. This comprehensive program combines foundational concepts in data science with advanced techniques in data mining, providing hands-on experience with real-world datasets and industry-relevant tools.
Participants will explore key areas such as data preprocessing, pattern recognition, clustering, classification, predictive modeling, and association rule mining. The course emphasizes practical implementation using programming languages like Python and R, along with tools like SQL, Excel, and popular data visualization platforms.
Key Features of Course Divine:
Module 1: Introduction to Data Science and Data Mining What is Data Science? Importance and Applications Overview of Data Mining CRISP-DM methodology Difference between Data Science, AI, ML, and Data Mining.
Module 2: Data Collection and Preprocessing Data sources: structured and unstructured Data cleaning (missing values, outliers) Data integration and transformation Feature scaling and encoding Data sampling techniques.
Module 3: Exploratory Data Analysis (EDA) Descriptive statistics Data visualization (histograms, boxplots, scatter plots) Correlation analysis Handling imbalanced data Tools: Python (Pandas, Matplotlib, Seaborn).
Module 4: Statistical Foundations for Data Science Probability theory Hypothesis testing Confidence intervals Linear and logistic regression Statistical significance and p-values.
Module 5: Supervised Learning Techniques Classification algorithms (Decision Tree, KNN, SVM, Naïve Bayes) Regression models (Linear, Ridge, Lasso) Model evaluation: accuracy, precision, recall, F1-score, ROC-AUC Cross-validation techniques.
Module 6: Unsupervised Learning and Clustering K-Means Clustering Hierarchical Clustering DBSCAN Dimensionality reduction (PCA, t-SNE) Association Rule Mining (Apriorism, FP-Growth).
Module 7: Data Mining Techniques and Algorithms Rule-based and Pattern-based mining Market Basket Analysis Anomaly and Outlier Detection Time Series Data Mining Text Mining basics.
Module 8: Machine Learning in Data Science ML pipeline overview Ensemble methods (Random Forest, Boost) Model selection and tuning (Grid Search, Hyperparameter Tuning) Overfitting and Underfitting Introduction to deep learning
Module 9: Tools and Technologies Python (Numbly, Pandas, Sickie-learn) R Basics for Data Mining SQL for Data Extraction Jupiter Notebooks Big Data platforms (Hadoop, Spark – basic overview).
Module 10: Capstone Project & Industry Use Cases Real-world project from domains like Healthcare, Finance, or E-commerce Data Cleaning, Modeling, and Evaluation Project documentation and presentation Review of industry applications of Data Science and Mining.
Industry Projects:
Mobile: 9100348679
Email: coursedivine@gmail.com
You cannot copy content of this page
