📊
Data
Data Science
Python, statistics, data cleaning, machine learning, visualization and model deployment.
Duration
12 weeks
Students
410+ enrolled
Rating
4.7
What You Will Learn
Analyze real datasets
Build ML models
Create dashboards
Explain insights for business use cases
Curriculum
1
Data science roadmap
RolesToolsProject lifecycleDataset types
Lab: Set up Python, Jupyter and data workspace
2
Python for data
ListsDictionariesFunctionsPackages
Lab: Analyze simple CSV files
3
NumPy and Pandas
ArraysDataFramesFilteringAggregation
Lab: Clean and summarize student performance data
4
Data cleaning
Missing valuesDuplicatesOutliersData types
Lab: Prepare raw sales dataset for analysis
5
Statistics basics
Mean/medianVarianceProbabilityDistributions
Lab: Create descriptive statistics report
6
Visualization
MatplotlibSeaborn conceptsChartsStorytelling
Lab: Build charts for business insights
7
Machine learning intro
Train/test splitRegressionClassificationMetrics
Lab: Train first prediction model
8
Feature engineering
EncodingScalingFeature selectionPipelines
Lab: Improve model accuracy using feature engineering
9
Advanced ML
Decision treesRandom forestClusteringModel tuning
Lab: Compare multiple algorithms
10
Model evaluation
Confusion matrixPrecision/recallRMSEOverfitting
Lab: Prepare model performance summary
11
Model deployment basics
Pickle/joblibFlask APIInput validationDeployment flow
Lab: Serve ML prediction API
12
Capstone and portfolio
Problem framingNotebook cleanupGitHub READMEInterview prep
Lab: Present end-to-end data science project