Intermediate
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Data Science
Comprehensive data science training covering statistical analysis, machine learning, and data visualization with real-world projects.
12 weeks
14 Modules
Certificate Included
Learning Outcomes
What You'll Learn
By the end of this course, you'll be able to:
Perform exploratory data analysis
Apply statistical methods
Build predictive models
Create compelling visualizations
Communicate data insights
Follow data science workflows
Curriculum
Course Modules
A comprehensive curriculum designed for practical application.
1
Data Science Overview
- The data science process
- Tools and environments
- Types of analysis
- Career paths
2
Data Collection
- APIs and web scraping
- Databases
- File formats
- Data quality
3
Data Cleaning
- Missing values
- Outliers
- Inconsistencies
- Python libraries
4
Exploratory Analysis
- Descriptive statistics
- Distributions
- Correlations
- Visual exploration
5
Statistical Inference
- Hypothesis testing
- Confidence intervals
- P-values
- Statistical power
6
Regression Analysis
- Linear regression
- Logistic regression
- Model diagnostics
- Interpretation
7
Classification
- Decision trees
- Logistic regression
- SVM
- Model evaluation
8
Clustering
- K-means
- Hierarchical
- DBSCAN
- Evaluation metrics
9
Dimensionality Reduction
- PCA
- t-SNE
- Feature selection
- Interpretation
10
Time Series
- Trends and seasonality
- Forecasting methods
- ARIMA
- Prophet
11
Data Visualization
- Matplotlib
- Seaborn
- Interactive plots
- Storytelling
12
Big Data
- Spark basics
- Distributed computing
- Data lakes
- Processing pipelines
13
Communication
- Presentations
- Reports
- Dashboards
- Stakeholder management
14
Capstone Project
- End-to-end project
- Real dataset
- Presentation
- Peer review
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