Data Science Professional Certification

Module 1: Data Science Fundamentals

• Data science concepts
• Data science lifecycle
• Types of data
• Analytical approaches
• Business applications
• Data-driven decision-making

Module 2: Python for Data Science

• Python syntax
• Variables and data types
• Data structures
• Functions
• Control flow
• Data processing

Module 3: NumPy for Numerical Computing

• NumPy arrays
• Array operations
• Indexing and slicing
• Broadcasting
• Mathematical functions
• Aggregations

Module 4: Data Analysis with Pandas

• Series and DataFrames
• Data selection
• Filtering
• Grouping
• Merging
• Pivoting

Module 5: Data Cleaning & Preparation

• Missing values
• Duplicate records
• Outlier handling
• Data type conversion
• Data transformation
• Data validation

Module 6: Exploratory Data Analysis

• Data distributions
• Descriptive analysis
• Pattern identification
• Correlation analysis
• Outlier analysis
• Insight generation

Module 7: Statistics for Data Science

• Descriptive statistics
• Probability concepts
• Probability distributions
• Sampling
• Confidence intervals
• Statistical interpretation

Module 8: Hypothesis Testing

• Statistical hypotheses
• P-values
• Significance levels
• T-tests
• Chi-square concepts
• Statistical decision-making

Module 9: Data Visualization

• Visualization principles
• Matplotlib
• Charts and plots
• Distribution visualization
• Relationship visualization
• Data storytelling

Module 10: SQL for Data Science

• Data querying
• Filtering and sorting
• Aggregations
• Joins
• Subqueries
• Window functions

Module 11: Machine Learning Fundamentals

• Machine learning lifecycle
• Supervised learning
• Unsupervised learning
• Features and targets
• Training and testing
• Model selection

Module 12: Regression Techniques

• Linear regression
• Multiple regression
• Regression assumptions
• Feature relationships
• Prediction
• Regression evaluation

Module 13: Classification Techniques

• Logistic regression
• Decision trees
• Random forests
• K-nearest neighbors
• Classification workflows
• Model comparison

Module 14: Feature Engineering

• Feature creation
• Encoding categorical variables
• Scaling
• Normalization
• Feature selection
• Feature transformation

Module 15: Model Evaluation & Optimization

• Confusion matrix
• Accuracy
• Precision and recall
• F1 score
• ROC-AUC
• Hyperparameter tuning

Module 16: Unsupervised Learning

• Clustering concepts
• K-Means
• Hierarchical clustering
• DBSCAN concepts
• PCA
• Customer segmentation

Module 17: Time Series Analysis

• Time series components
• Trends
• Seasonality
• Moving averages
• Forecasting concepts
• Forecast evaluation

Module 18: Natural Language Processing

• NLP fundamentals
• Text preprocessing
• Tokenization
• Text representation
• Sentiment analysis
• Text classification

Module 19: Generative AI for Data Science

• Generative AI fundamentals
• Large language models
• Prompt engineering
• Embeddings
• Retrieval concepts
• Data science applications

Module 20: Data Science Deployment Fundamentals

• Model serialization
• Prediction workflows
• API concepts
• Deployment architecture
• Model monitoring
• Production considerations

Module 21: Responsible Data Science & Best Practices

• Data quality
• Model interpretability
• Bias awareness
• Reproducibility
• Data governance
• Responsible AI

Module 22: Practical Data Science Project

• Business problem definition
• Data preparation
• Exploratory analysis
• Feature engineering
• Model development
• Results presentation