Data Science for Beginners

Data Science for Beginners is a Breni learning path that covers the full data science workflow, from asking a question to building a model and explaining the result. Its 8 courses combine statistics, Python with pandas, data cleaning, charts and simple predictive models. Learners finish able to take a public dataset, prepare it, test an idea and present what they found to people who are not technical. Statistics comes before modelling on purpose, because each course stays locked until the one before it is complete.

  • 8 courses
  • 12 learning objectives
  • Level: beginner
  • Language: English
  • Price: Subscription, free trial

What you will be able to do

  • Load, filter and summarise a dataset with Python and pandas
  • Clean missing values, duplicates and badly formatted dates in a raw dataset
  • Test whether a difference between two groups is likely to be real or chance
  • Train a regression model and measure its error on data it has not seen
  • Present a data science finding and its limits to a non-technical audience

Full curriculum

Part 1 · Foundations

1. Data Science Basics: What Data Scientists Do

Describe the data science workflow and the kinds of questions it can answer. Units, in order: Turning a business question into a data question; The workflow: collect, clean, explore, model, share; Data science, data analysis and machine learning compared; Kinds of data: numbers, categories, text and time; Privacy and ethics when working with personal data.

  • — Business Questions to Data
    Turn vague business problems into clear data questions.
    • Business Problem Framing
    • Asking Data Questions
  • — The Data Workflow
    Follow steps from collection and cleaning to modeling and sharing.
    • Collection and Cleaning
    • Exploration and Modeling
    • Sharing Results
  • — Data Roles Compared
    Understand data science, data analysis, and machine learning differences.
    • Data Analysis Basics
    • What is Data Science
    • Machine Learning Role
  • — Kinds of Data
    Explore numbers, categories, text, and time values in datasets.
    • Numbers and Categories
    • Text and Time Data
  • — Privacy and Ethics
    Learn how to handle personal data safely and ethically.
    • Protecting Personal Data
    • Ethical Data Use

2. Statistics for Data Science

Summarise data and reason about uncertainty before trusting a pattern. Units, in order: Mean, median and spread; Distributions and outliers; Samples and why sample size matters; Correlation versus causation; Confidence intervals and p-values in plain terms.

3. Python and pandas for Data

Load a dataset in Python and reshape it with pandas. Units, in order: The Python you need for data work; DataFrames: rows, columns and data types; Selecting and filtering rows; Grouping and aggregating; Merging two tables on a shared column.

Part 2 · Working with data

4. Cleaning Messy Data

Turn a raw export into a dataset you can trust. Units, in order: Finding and handling missing values; Duplicates and inconsistent labels; Fixing dates, currencies and number formats; Spotting values that cannot be true; Keeping a record of every cleaning step.

5. Exploratory Data Analysis

Find patterns and good questions in a dataset you have never seen before. Units, in order: First look: shape, types and summary tables; Histograms and box plots; Comparing groups side by side; Relationships between two variables; Writing down what to test next.

6. Visualising Data

Choose and build charts that answer a question honestly. Units, in order: Matching a chart type to the question; Bar, line and scatter charts with matplotlib; Labels, scales and colour that help the reader; Charts that mislead and how to avoid them.

Part 3 · Models and results

7. First Predictive Models

Build and evaluate simple models that predict a number or a category. Units, in order: Features and targets; Splitting data into training and test sets; Linear regression for predicting numbers; Logistic regression for yes or no outcomes; Measuring error and accuracy; Overfitting and how to notice it.

8. Data Science Project

Carry one project from a question to a written recommendation. Units, in order: Scoping a question that data can answer; Finding and checking a public dataset; Writing an analysis plan; Presenting findings to non-technical people; Stating the limits of what the data shows.

Sample: Data Science Basics: What Data Scientists Do

The opening tier, (Business Questions to Data). Turn vague business problems into clear data questions.

Business Problem Framing

Identify real business problems that data can help solve.

Asking Data Questions

Convert a broad goal into a specific data question.

Questions about Data Science for Beginners

Can I learn data science with no experience?

Yes. Data Science for Beginners on Breni starts with what data scientists do and teaches the Python and statistics you need along the way.

Do I need to know Python before this data science course?

No. The third course covers the Python and pandas used in the rest of the path, though some earlier coding practice will make it feel quicker.

What is the difference between data science and data analysis?

Data analysis answers questions about what has already happened, while data science also builds models that predict what is likely to happen. This path covers both, and the Data Analysis for Beginners path on Breni goes deeper on spreadsheets, SQL and dashboards.

How much maths does data science need?

At this level, mostly averages, percentages, spread and basic probability. The path teaches each idea in plain terms before using it in a model.

Do I get a certificate at the end?

Yes. Completing all 8 courses earns a Breni certificate of completion. It records that you finished the path and is not a degree.

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