Data Science
Turn data into insight with analysis, visualisation and statistical thinking.
- Fees
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About this course
Data science uses statistics, programming and machine learning to answer questions from data and to predict what is likely to happen next. This course takes you through the whole path: collecting and cleaning data, exploring it, testing ideas with statistics, building predictive models and explaining the results to the people who make decisions.
You work in Python with real, public datasets. The emphasis is on sound method, such as checking assumptions and measuring a model honestly, because a model that looks good on paper but fails on new data is of no use to a business.
What you will be able to do
- Frame a business question as a data problem and plan the steps to answer it.
- Clean, combine and explore data with pandas and SQL.
- Choose and read charts that show a result honestly.
- Apply statistical tests and confidence intervals, and avoid common mistakes such as confusing correlation with cause.
- Train, compare and tune regression, classification and clustering models with scikit-learn.
- Measure a model on unseen data with the right metric and explain what drives its predictions.
- Present findings in a short report or notebook that a non-technical reader can follow.
Syllabus
10 modules. Select a module to see its topics.
Module 1: Data science foundations
- The project lifecycle
- Kinds of data
- Turning a business question into a data question
- Roles on a data team
- Jupyter notebooks and Git
Module 2: Python for data work
- Core syntax
- Lists, dictionaries and functions
- NumPy arrays
- pandas DataFrames
- Reading CSV, Excel, JSON and databases
Module 3: Cleaning and exploring data
- Missing values
- Duplicates
- Outliers
- Data types
- Grouping, merging and reshaping
- Exploratory data analysis
Module 4: Visualisation and storytelling
- Matplotlib, Seaborn and Plotly
- Choosing the right chart
- Avoiding misleading visuals
- Building a clear data story
Module 5: Statistics and probability
- Descriptive statistics
- Distributions
- Sampling
- Confidence intervals
- Hypothesis tests
- Correlation and causation
- A/B test basics
Module 6: SQL for data scientists
- SELECT, joins and aggregates
- Subqueries and CTEs
- Window functions
- Pulling query results into pandas
Module 7: Machine learning with scikit-learn
- Regression and classification
- Train and test splits
- Pipelines
- Cross-validation
- Metrics such as RMSE, precision, recall and ROC-AUC
Module 8: Feature engineering and model improvement
- Encoding and scaling
- Feature selection
- Tree-based models and ensembles
- Hyperparameter tuning
- Imbalanced data
Module 9: Unsupervised learning and time series
- k-means and hierarchical clustering
- PCA
- Anomaly detection
- Trend and seasonality
- Simple forecasting models
Module 10: Explaining and delivering results
- Feature importance and model explanation
- Reporting
- Saving a model
- A small prediction app
- Capstone project and presentation
Tools and technologies
- Python
- Jupyter
- NumPy
- pandas
- Matplotlib
- Seaborn
- Plotly
- scikit-learn
- statsmodels
- SQL (PostgreSQL or SQLite)
- Git and GitHub
- Streamlit (for a simple demo app)
Projects you will build
Customer churn prediction with a classification model and a clear explanation of the main drivers.
Sales or demand forecasting using trend and seasonality.
Customer segmentation with clustering.
House or used-car price prediction with regression and error analysis.
A/B test analysis report with a recommendation.
Capstone: an end-to-end project on a dataset of the learner's choice, from question to presentation.
Before you start
- This outline starts from the basics: Python is taught from the first modules, so no earlier coding is assumed.
- Helpful but not required: school-level maths (percentages, averages, basic algebra) and some comfort with Excel.
- Entry requirements and the lab set-up are confirmed by Pathway Career when you enquire.
Career paths
Examples of roles that use these skills. They are not a promise of a job.
- Junior data scientist
- Data analyst
- Machine learning analyst
- Business insights analyst
- Research analyst
Questions about this course
Do I need programming or advanced maths?
This outline assumes neither. Python is taught from the basics, and the maths is taught as it is needed, with the focus on understanding rather than proofs. Entry requirements are confirmed by Pathway Career when you enquire.
What is the difference between Data Science and Data Analytics?
Data Analytics describes and explains what happened, using SQL, Excel and BI tools. Data Science adds prediction with statistics and machine learning. The two overlap, and many learners take both.
Does the course include deep learning?
It covers machine learning with scikit-learn and gives only an overview of neural networks. The AI & Machine Learning course goes deeper.
Is R included?
The course is built around Python. Pathway Career confirms whether R is offered when you enquire.
What data will we use?
Public datasets from business, health and e-commerce settings. Pathway Career confirms any datasets it supplies when you enquire.
Can working professionals join?
Batch timings and formats are confirmed by Pathway Career when you enquire.
Next step
Interested in Data Science? Send an enquiry to ask about the syllabus, schedule and fees.
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