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Data Science Projects with Python
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Summary

Price
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Study method
Distance + live classes
Duration
Full-time
Qualification
No formal qualification

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Overview

The Data Science Projects with Python course equips learners with practical skills to implement data science and machine learning projects using Python. Through hands-on case studies and real-world datasets, you will learn how to explore, clean, and analyse data, apply predictive models, and deliver actionable insights in a business context.

You will work with industry-standard Python libraries including pandas, Matplotlib, scikit-learn, and Jupyter notebooks to simulate real-world data science workflows. The course focuses on the end-to-end process of a data science project: from understanding the problem, preparing and exploring the data, to building, evaluating, and tuning predictive models.

By the end of the course, you will be confident in applying logistic regression, random forests, and other machine learning techniques to solve practical problems, and in presenting your findings to stakeholders in a clear and actionable way.

Description

This course follows a case study approach, simulating realistic working conditions in data science projects. Each module combines theory with practical exercises, enabling you to apply your learning immediately.

Key topics include:

  • Data Exploration and Cleaning: Load and inspect datasets, check for quality issues, and prepare data for analysis.
  • Model Evaluation: Learn metrics for assessing predictive performance and explore methods like cross-validation.
  • Logistic Regression & Feature Engineering: Build models, select relevant features, and interpret coefficients.
  • Bias-Variance Trade-Off: Understand how to balance model complexity and predictive performance.
  • Decision Trees & Random Forests: Apply ensemble methods for robust predictions.
  • Handling Missing Data & Client Delivery: Impute missing values, derive insights, and present your analysis effectively.

Hands-on activities include working directly with financial datasets, creating predictive models, and presenting your findings. By the end of the course, you will have completed a full data science project from start to finish, ready to showcase on your CV or professional portfolio.

Who is this course for?

This course is ideal for:

  • Aspiring data scientists and analysts
  • Python developers looking to expand into data science and machine learning
  • Professionals working with datasets who want to derive actionable insights
  • Students and researchers aiming to apply predictive modelling in real-world projects

No prior experience in machine learning is required, though basic Python knowledge and familiarity with high school-level mathematics is recommended.

Requirements

  • Basic understanding of Python programming
  • Familiarity with basic mathematics (algebra, statistics)
  • Laptop with internet access and permission to install Python packages

Career path

Completing this course can support roles such as Data Scientist, Data Analyst, Business Intelligence Analyst, or Python Developer (data-focused).

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Legal information

This course is advertised on Reed.co.uk by the Course Provider, whose terms and conditions apply. Purchases are made directly from the Course Provider, and as such, content and materials are supplied by the Course Provider directly. Reed is acting as agent and not reseller in relation to this course. Reed's only responsibility is to facilitate your payment for the course. It is your responsibility to review and agree to the Course Provider's terms and conditions and satisfy yourself as to the suitability of the course you intend to purchase. Reed will not have any responsibility for the content of the course and/or associated materials.

FAQs

Study method describes the format in which the course will be delivered. At Reed Courses, courses are delivered in a number of ways, including online courses, where the course content can be accessed online remotely, and classroom courses, where courses are delivered in person at a classroom venue.

CPD stands for Continuing Professional Development. If you work in certain professions or for certain companies, your employer may require you to complete a number of CPD hours or points, per year. You can find a range of CPD courses on Reed Courses, many of which can be completed online.

A regulated qualification is delivered by a learning institution which is regulated by a government body. In England, the government body which regulates courses is Ofqual. Ofqual regulated qualifications sit on the Regulated Qualifications Framework (RQF), which can help students understand how different qualifications in different fields compare to each other. The framework also helps students to understand what qualifications they need to progress towards a higher learning goal, such as a university degree or equivalent higher education award.

An endorsed course is a skills based course which has been checked over and approved by an independent awarding body. Endorsed courses are not regulated so do not result in a qualification - however, the student can usually purchase a certificate showing the awarding body's logo if they wish. Certain awarding bodies - such as Quality Licence Scheme and TQUK - have developed endorsement schemes as a way to help students select the best skills based courses for them.
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