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Python for Data Science & Artificial Intelligence
Xcel Learning

Complimentary Assessment | Digital Certificate | 24/7 Support | Lifetime Access | Transparent Pricing

Summary

Price
£22 inc VAT
Study method
Online, On Demand 
Duration
1.3 hours · Self-paced
Qualification
No formal qualification
Certificates
  • Reed Courses Certificate of Completion - Free
Assessment details
  • Review Questions and Assessments (included in price)
Additional info
  • Tutor is available to students

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Overview

Python for Data Science & Artificial Intelligence course provides a comprehensive introduction to Python programming within the context of Data Science and Artificial Intelligence. Students begin by mastering Python fundamentals, including data structures, object-oriented programming, and numerical computing. The course then progresses into data analysis using Pandas, visualization techniques, and essential statistical foundations. Learners explore supervised and unsupervised machine learning algorithms, followed by deep learning concepts and practical implementation using modern frameworks. Emphasis is placed on hands-on application, real world problem solving, and responsible AI development. By the end of the course, participants will be able to preprocess data, build predictive models, evaluate performance, and deploy AI-driven solutions. This program equips learners with the technical skills, analytical mindset, and ethical awareness required to succeed in today’s data-driven industries.

Certificates

Assessment details

Review Questions and Assessments

Included in course price

Curriculum

13
sections
13
lectures
1h 16m
total

Description

Exciting Adventures Await: Discover the Fascinating Topics This Course Will Explore!

Chapter 1: Introduction to Python Programming

  1. Installing Python, Anaconda, and Jupyter Notebook
  2. Python Syntax, Variables, and Data Types
  3. Operators and Expressions
  4. Control Flow (if statements, loops)
  5. Functions and Basic Input/Output

Chapter 2: Data Structures in Python

  1. Lists and List Comprehensions
  2. Tuples and Sets
  3. Dictionaries and Dictionary Methods
  4. Strings and String Manipulation
  5. Working with Built-in Functions

Chapter 3: Object-Oriented Programming (OOP)

  1. Classes and Objects
  2. Attributes and Methods
  3. Inheritance and Polymorphism
  4. Encapsulation and Abstraction
  5. Magic (Dunder) Methods

Chapter 4: Numerical Computing with NumPy

  1. Introduction to NumPy Arrays
  2. Array Indexing and Slicing
  3. Vectorized Operations
  4. Broadcasting
  5. Linear Algebra with NumPy

Chapter 5: Data Analysis with Pandas

  1. Introduction to Series and DataFrames
  2. Data Loading and Saving (CSV, Excel, JSON)
  3. Data Cleaning and Preprocessing
  4. Data Aggregation and GroupBy Operations
  5. Merging, Joining, and Reshaping Data

Chapter 6: Data Visualization

  1. Introduction to Matplotlib
  2. Statistical Visualization with Seaborn
  3. Customizing Plots and Styles
  4. Interactive Visualization with Plotly
  5. Best Practices for Data Visualization

Chapter 7: Statistics and Probability for Data Science

  1. Descriptive Statistics
  2. Probability Theory Basics
  3. Random Variables and Distributions
  4. Sampling and Central Limit Theorem
  5. Hypothesis Testing

Chapter 8: Introduction to Machine Learning

  1. Types of Machine Learning (Supervised, Unsupervised, Reinforcement)
  2. Machine Learning Workflow
  3. Feature Engineering
  4. Model Evaluation Metrics
  5. Overfitting and Underfitting

Chapter 9: Supervised Learning Algorithms

  1. Linear Regression
  2. Logistic Regression
  3. k-Nearest Neighbors (k-NN)
  4. Decision Trees and Random Forests
  5. Support Vector Machines (SVM)

Chapter 10: Unsupervised Learning Algorithms

  1. Clustering with k-Means
  2. Hierarchical Clustering
  3. DBSCAN
  4. Principal Component Analysis (PCA)
  5. Anomaly Detection

Chapter 11: Deep Learning with Python

  1. Introduction to Neural Networks
  2. Activation Functions and Loss Functions
  3. Backpropagation and Gradient Descent
  4. Building Models with TensorFlow and Keras
  5. Introduction to PyTorch

Chapter 12: Artificial Intelligence Applications

  1. Natural Language Processing (NLP)
  2. Computer Vision Fundamentals
  3. Time Series Forecasting
  4. Model Deployment and APIs
  5. Ethics in AI and Responsible AI Development

Don't miss out on the chance to discover your full potential. Enroll today and open the door to a world of opportunities. Receive an exclusive digital certificate upon completing the course!

Who is this course for?

This course is designed for beginners and aspiring professionals who want to build a strong foundation in Python for data science and artificial intelligence. It suits students, analysts, and developers seeking practical skills in data analysis, machine learning, and problem-solving, even if they have little or no prior programming experience.

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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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