In the UK, demand for data analysis skills has surged, with job listings for data analysts increasing by 31% in recent years. Employers across various industries are actively seeking professionals with Python expertise. By joining the Data Analysis with Python course, you’ll acquire the essential skills to succeed in this growing field and boost your CV with an accredited certification.
In the Data Analysis with Python course, you’ll begin with the fundamentals of Python, mastering powerful libraries like NumPy and Pandas for data manipulation. You’ll also learn data visualisation using tools such as Matplotlib, Seaborn, and Plotly, and explore machine learning with Scikit-learn. The course also covers advanced topics, including natural language processing with NLTK and building recommender systems, providing a comprehensive understanding of data analysis.
This in-demand expertise will enhance your employability and open doors to well-paying career paths. Data analysts in the UK earn between £30,000 and £60,000 annually, with specialised roles offering even higher salaries. Enrol in the Data Analysis with Python course today to gain the skills needed to thrive in the competitive field of data analysis.
Who is this course for?
By taking this Data Analysis with Python Course, you’re able to open yourself to new opportunities within the data and analytics career path. Such careers may include:
- Data Analyst (£22,000 to £45,000)
- Business Intelligence Analyst (£30,000 to £55,000)
- Data Engineer (£40,000 to £65,000)
Course Curriculum
| Welcome, Course Introduction & overview, and Environment set-up | |||
| Welcome & Course Overview | 00:07:00 | ||
| Set-up the Environment for the Course (lecture 1) | 00:09:00 | ||
| Set-up the Environment for the Course (lecture 2) | 00:25:00 | ||
| Two other options to setup environment | 00:04:00 | ||
| Python Essentials | |||
| Python data types Part 1 | 00:21:00 | ||
| Python Data Types Part 2 | 00:15:00 | ||
| Loops, List Comprehension, Functions, Lambda Expression, Map and Filter (Part 1) | 00:16:00 | ||
| Loops, List Comprehension, Functions, Lambda Expression, Map and Filter (Part 2) | 00:20:00 | ||
| Python Essentials Exercises Overview | 00:02:00 | ||
| Python Essentials Exercises Solutions | 00:22:00 | ||
| Python for Data Analysis using NumPy | |||
| What is Numpy? A brief introduction and installation instructions. | 00:03:00 | ||
| NumPy Essentials – NumPy arrays, built-in methods, array methods and attributes. | 00:28:00 | ||
| NumPy Essentials – Indexing, slicing, broadcasting & boolean masking | 00:26:00 | ||
| NumPy Essentials – Arithmetic Operations & Universal Functions | 00:07:00 | ||
| NumPy Essentials Exercises Overview | 00:02:00 | ||
| NumPy Essentials Exercises Solutions | 00:25:00 | ||
| Python for Data Analysis using Pandas | |||
| What is pandas? A brief introduction and installation instructions. | 00:02:00 | ||
| Pandas Introduction | 00:02:00 | ||
| Pandas Essentials – Pandas Data Structures – Series | 00:20:00 | ||
| Pandas Essentials – Pandas Data Structures – DataFrame | 00:30:00 | ||
| Pandas Essentials – Handling Missing Data | 00:12:00 | ||
| Pandas Essentials – Data Wrangling – Combining, merging, joining | 00:20:00 | ||
| Pandas Essentials – Groupby | 00:10:00 | ||
| Pandas Essentials – Useful Methods and Operations | 00:26:00 | ||
| Pandas Essentials – Project 1 (Overview) Customer Purchases Data | 00:08:00 | ||
| Pandas Essentials – Project 1 (Solutions) Customer Purchases Data | 00:31:00 | ||
| Pandas Essentials – Project 2 (Overview) Chicago Payroll Data | 00:04:00 | ||
| Pandas Essentials – Project 2 (Solutions Part 1) Chicago Payroll Data | 00:18:00 | ||
| Python for Data Visualization using matplotlib | |||
| Matplotlib Essentials (Part 1) – Basic Plotting & Object Oriented Approach | 00:13:00 | ||
| Matplotlib Essentials (Part 2) – Basic Plotting & Object Oriented Approach | 00:22:00 | ||
| Matplotlib Essentials (Part 3) – Basic Plotting & Object Oriented Approach | 00:22:00 | ||
| Matplotlib Essentials – Exercises Overview | 00:06:00 | ||
| Matplotlib Essentials – Exercises Solutions | 00:21:00 | ||
| Python for Data Visualization using Seaborn | |||
| Seaborn – Introduction & Installation | 00:04:00 | ||
| Seaborn – Distribution Plots | 00:25:00 | ||
| Seaborn – Categorical Plots (Part 1) | 00:21:00 | ||
| Seaborn – Categorical Plots (Part 2) | 00:16:00 | ||
| Seborn-Axis Grids | 00:25:00 | ||
| Seaborn – Matrix Plots | 00:13:00 | ||
| Seaborn – Regression Plots | 00:11:00 | ||
| Seaborn – Controlling Figure Aesthetics | 00:10:00 | ||
| Seaborn – Exercises Overview | 00:04:00 | ||
| Seaborn – Exercise Solutions | 00:19:00 | ||
| Python for Data Visualization using pandas | |||
| Pandas Built-in Data Visualization | 00:34:00 | ||
| Pandas Data Visualization Exercises Overview | 00:03:00 | ||
| Panda Data Visualization Exercises Solutions | 00:13:00 | ||
| Python for interactive & geographical plotting using Plotly and Cufflinks | |||
| Plotly & Cufflinks – Interactive & Geographical Plotting (Part 1) | 00:19:00 | ||
| Plotly & Cufflinks – Interactive & Geographical Plotting (Part 2) | 00:14:00 | ||
| Plotly & Cufflinks – Interactive & Geographical Plotting Exercises (Overview) | 00:11:00 | ||
| Plotly & Cufflinks – Interactive & Geographical Plotting Exercises (Solutions) | 00:37:00 | ||
| Capstone Project - Python for Data Analysis & Visualization | |||
| Project 1 – Oil vs Banks Stock Price during recession (Overview) | 00:15:00 | ||
| Project 1 – Oil vs Banks Stock Price during recession (Solutions Part 1) | 00:18:00 | ||
| Project 1 – Oil vs Banks Stock Price during recession (Solutions Part 2) | 00:18:00 | ||
| Project 1 – Oil vs Banks Stock Price during recession (Solutions Part 3) | 00:17:00 | ||
| Project 2 (Optional) – Emergency Calls from Montgomery County, PA (Overview) | 00:03:00 | ||
| Python for Machine Learning (ML) - scikit-learn - Linear Regression Model | |||
| Introduction to ML – What, Why and Types….. | 00:15:00 | ||
| Theory Lecture on Linear Regression Model, No Free Lunch, Bias Variance Tradeoff | 00:15:00 | ||
| scikit-learn – Linear Regression Model – Hands-on (Part 1) | 00:17:00 | ||
| scikit-learn – Linear Regression Model Hands-on (Part 2) | 00:19:00 | ||
| Good to know! How to save and load your trained Machine Learning Model! | 00:01:00 | ||
| scikit-learn – Linear Regression Model (Insurance Data Project Overview) | 00:08:00 | ||
| scikit-learn – Linear Regression Model (Insurance Data Project Solutions) | 00:30:00 | ||
| Python for Machine Learning - scikit-learn - Logistic Regression Model | |||
| Theory: Logistic Regression, conf. mat., TP, TN, Accuracy, Specificity…etc. | 00:10:00 | ||
| scikit-learn – Logistic Regression Model – Hands-on (Part 1) | 00:17:00 | ||
| scikit-learn – Logistic Regression Model – Hands-on (Part 2) | 00:20:00 | ||
| scikit-learn – Logistic Regression Model – Hands-on (Part 3) | 00:11:00 | ||
| scikit-learn – Logistic Regression Model – Hands-on (Project Overview) | 00:05:00 | ||
| scikit-learn – Logistic Regression Model – Hands-on (Project Solutions) | 00:15:00 | ||
| Python for Machine Learning - scikit-learn - K Nearest Neighbors | |||
| Theory: K Nearest Neighbors, Curse of dimensionality …. | 00:08:00 | ||
| scikit-learn – K Nearest Neighbors – Hands-on | 00:25:00 | ||
| scikt-learn – K Nearest Neighbors (Project Overview) | 00:04:00 | ||
| scikit-learn – K Nearest Neighbors (Project Solutions) | 00:14:00 | ||
| Python for Machine Learning - scikit-learn - Decision Tree and Random Forests | |||
| Theory: D-Tree & Random Forests, splitting, Entropy, IG, Bootstrap, Bagging…. | 00:18:00 | ||
| scikit-learn – Decision Tree and Random Forests – Hands-on (Part 1) | 00:19:00 | ||
| scikit-learn – Decision Tree and Random Forests (Project Overview) | 00:05:00 | ||
| scikit-learn – Decision Tree and Random Forests (Project Solutions) | 00:15:00 | ||
| Python for Machine Learning - scikit-learn -Support Vector Machines (SVMs) | |||
| Support Vector Machines (SVMs) – (Theory Lecture) | 00:07:00 | ||
| scikit-learn – Support Vector Machines – Hands-on (SVMs) | 00:30:00 | ||
| scikit-learn – Support Vector Machines (Project 1 Overview) | 00:07:00 | ||
| scikit-learn – Support Vector Machines (Project 1 Solutions) | 00:20:00 | ||
| scikit-learn – Support Vector Machines (Optional Project 2 – Overview) | 00:02:00 | ||
| Python for Machine Learning - scikit-learn - K Means Clustering | |||
| Theory: K Means Clustering, Elbow method ….. | 00:11:00 | ||
| scikit-learn – K Means Clustering – Hands-on | 00:23:00 | ||
| scikit-learn – K Means Clustering (Project Overview) | 00:07:00 | ||
| scikit-learn – K Means Clustering (Project Solutions) | 00:22:00 | ||
| Python for Machine Learning - scikit-learn - Principal Component Analysis (PCA) | |||
| Theory: Principal Component Analysis (PCA) | 00:09:00 | ||
| scikit-learn – Principal Component Analysis (PCA) – Hands-on | 00:22:00 | ||
| scikit-learn – Principal Component Analysis (PCA) – (Project Overview) | 00:02:00 | ||
| scikit-learn – Principal Component Analysis (PCA) – (Project Solutions) | 00:17:00 | ||
| Recommender Systems with Python - (Additional Topic) | |||
| Theory: Recommender Systems their Types and Importance | 00:06:00 | ||
| Python for Recommender Systems – Hands-on (Part 1) | 00:18:00 | ||
| Python for Recommender Systems – – Hands-on (Part 2) | 00:19:00 | ||
| Python for Natural Language Processing (NLP) - NLTK - (Additional Topic) | |||
| Natural Language Processing (NLP) – (Theory Lecture) | 00:13:00 | ||
| NLTK – NLP-Challenges, Data Sources, Data Processing ….. | 00:13:00 | ||
| NLTK – Feature Engineering and Text Preprocessing in Natural Language Processing | 00:19:00 | ||
| NLTK – NLP – Tokenization, Text Normalization, Vectorization, BoW…. | 00:19:00 | ||
| NLTK – BoW, TF-IDF, Machine Learning, Training & Evaluation, Naive Bayes … | 00:13:00 | ||
| NLTK – NLP – Pipeline feature to assemble several steps for cross-validation… | 00:09:00 | ||
| Final Exam | |||
| Final Exam – Data Analysis with Python | 00:25:00 | ||
10 STUDENTS ENROLLED
