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Python Basics (Variables, Data Structures, Functions),
Libraries: NumPy, Pandas, Matplotlib, Seaborn,
Data Manipulation with Pandas, Data Visualization
with Matplotlib and Seaborn
Handling Missing Data, Data Cleaning and Transformation,
Data Normalization and Standardization
Feature Engineering and Selection
Descriptive Statistics (Mean, Median, Mode, Variance, etc.)
Data Visualization Techniques
Histograms, Boxplots, Scatter Plots
Identifying Patterns and Outliers in Data
Probability Theory Basics, Random Variables and Distributions,
Hypothesis Testing (t-tests, Chi-square), Statistical Inference
4. Machine Learning
Introduction to Machine Learning, Supervised Learning
(Linear Regression,
Logistic Regression) , Unsupervised Learning (K-Means Clustering, PCA)
Model Evaluation (Accuracy, Precision, Recall, F1-Score)
7. Advanced Machine Learning Algorithms
Decision Trees and Random Forests, Support Vector Machines (SVM)
K-Nearest Neighbors (KNN), Neural Networks and Deep
Model Deployment ConceptsIntroduction to Cloud Platforms (AWS, Azure, GCP) Creating and Deploying Machine Learning ModelsVersion Control for Models (Git, Docker)
Introduction to Big DataHadoop Ecosystem Spark and its ApplicationsData Storage Systems (SQL, NoSQL)
Ethical Issues in Data ScienceBias in Data and ModelsData Privacy Regulations (GDPR, CCPA)
Industry Use Cases of Data Science, Hands-on Projects (E.g., Predictive Analytics, Recommendation Systems)