Data Science & Machine Learning Course
Learn a practical data science workflow with Python, statistics, visualization, and machine learning projects.
ABOUT THE COURSE
About Data Science & Machine Learning Course
Data science combines programming, statistics, domain questions, and communication to turn data into useful evidence. Machine learning is one part of that process: models can identify patterns in examples, but their outputs need appropriate data, evaluation, and human interpretation.
Learn the full data workflow
Practice framing a question, inspecting data, documenting assumptions, cleaning records, exploring patterns, and communicating limitations. Use Python with NumPy and Pandas for data work, visualization tools to inspect distributions, and SQL fundamentals to understand how structured data is queried.
Build machine learning foundations
Study supervised learning for regression and classification, create simple baselines, split data for evaluation, and compare metrics that match the task. Learn why data leakage, unrepresentative samples, and changing conditions can make a model appear more reliable than it is.
Apply responsible judgment
Work with practice datasets and explain the limits of each conclusion. A model score does not prove a result is fair, causal, or suitable for a consequential decision. The course emphasizes reproducibility, privacy-aware data handling, transparent communication, and human review.
Course projects support learning and portfolio practice; they do not guarantee a job, salary, or business outcome.
UNDERSTAND THE FIELD
What is Data Science & Machine Learning Course?
COURSE CURRICULUM
What you will study
6 modules · Explore the complete learning path below
Frame a question, understand the data source, work in notebooks, and use Python fundamentals to build reproducible analyses.
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Data questions and evidence
- Define a question
- Identify assumptions and limitations
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Python and notebooks
- Core Python
- Jupyter workflow
- Readable code
Create a notebook that loads a small public or synthetic dataset, describes its fields, and records data-quality questions.
Inspect tabular data, handle missing and duplicate records deliberately, transform fields, and document each preparation choice.
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Tabular data
- DataFrames and arrays
- Types and summaries
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Cleaning decisions
- Missing values
- Duplicates
- Inconsistent categories
Prepare a synthetic service-record dataset and compare row counts and summary statistics before and after cleaning.
Summarize distributions, inspect relationships, visualize uncertainty, and distinguish description from causal explanation.
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Descriptive statistics
- Center and spread
- Sampling and uncertainty
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Visualization
- Distribution plots
- Comparisons
- Clear chart annotations
Explore a synthetic dataset with multiple charts and write a short explanation that separates observations from hypotheses.
Build baseline regression and classification models, prepare features, and understand how a model learns from labeled examples.
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Problem types
- Regression
- Classification
- Baseline models
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Training workflow
- Features and target
- Train and validation data
Train a baseline model on a permitted educational dataset and explain what the target and features represent.
Choose metrics that fit the problem, use validation appropriately, and recognize overfitting, leakage, imbalance, and distribution changes.
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Evaluation metrics
- MAE and RMSE
- Precision and recall
- Confusion matrix
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Validation risks
- Overfitting
- Data leakage
- Class imbalance
Compare two models using a suitable metric and describe a validation mistake that could make either score misleading.
Deliver a reproducible analysis or model project with source notes, privacy checks, limitations, and an understandable presentation.
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Reproducibility
- Project structure
- Version control
- Re-run instructions
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Responsible communication
- Limitations
- Fairness questions
- Human review
Present a data question, data preparation, baseline, evaluation, limitations, and next steps using approved public or synthetic data.
The learning sequence covers data science workflow, Python, data preparation, exploratory analysis, statistics, visualization, supervised machine learning, model evaluation, and a reproducible capstone. Confirm the course duration, class schedule, format, and payment terms with admissions.
TOOLS & TECHNOLOGIES
Tools you will work with
- Python
- Jupyter
- NumPy
- Pandas
- Matplotlib
- Seaborn
- scikit-learn
- SQL
- Git
WHAT YOU WILL LEARN
Learning outcomes
- Frame a data question and document assumptions, inputs, and limitations.
- Use Python, NumPy, and Pandas to inspect, clean, and transform tabular data.
- Use descriptive statistics and visualizations to explore patterns and outliers.
- Build baseline regression and classification models with scikit-learn.
- Evaluate models with suitable metrics and avoid common validation and leakage mistakes.
- Present a reproducible project with clear evidence, limitations, and responsible-use notes.
WHO SHOULD JOIN
Is this course right for you?
Students, graduates, analysts, and professionals who want structured practice with Python-based data analysis and machine learning fundamentals. The program focuses on learning workflows and project communication, not a guarantee of a specific role.
Prerequisites / eligibility
Basic computer use and comfort with school-level mathematics are helpful. Prior Python experience is useful but not required for the introductory modules. Confirm the current cohort prerequisites, duration, schedule, delivery format, and payment terms with admissions.
FAQ
Frequently asked questions
COURSE TOPICS
Explore Data Science & Machine Learning Course Topics
The Data Science Workflow: From Question to Evidence
Follow a reproducible data science process from a clear question and data checks to analysis, evaluation, and communication.
View full topicPython, NumPy, and Pandas for Data Science
Use Python arrays and Pandas tables to inspect, clean, transform, and summarize data with documented choices.
View full topicStatistics and Visualization for Machine Learning
Use descriptive statistics and charts to understand distributions, sampling, uncertainty, and relationships before modeling.
View full topicSupervised Machine Learning: Regression and Classification
Understand labeled data, features and targets, simple baselines, and the difference between regression and classification.
View full topicEvaluate Machine Learning Models Without Data Leakage
Choose metrics that match the task and avoid leakage, overfitting, and misleading evaluation splits.
View full topicData Science Capstone: A Reproducible Project from Start to Finish
Plan a compact data science project with documented sources, repeatable preparation, a baseline, evaluation, and clear limitations.
View full topicLocal course pages
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