Overview
In the UK, data analysis skills are in high demand, with job postings for data analysts having grown by 31% in recent years. Companies across all sectors are seeking professionals proficient in Python. By enrolling in the Python for Data Analysis course, you’ll gain the expertise needed to excel in this rapidly expanding field and enhance your CV with an accredited certification.
In this Python for Data Analysis course, you’ll start with the essentials of Python, learning to use powerful libraries like NumPy and Pandas for data manipulation. You’ll master data visualisation techniques with matplotlib, Seaborn, and Plotly, and dive into machine learning models using scikit-learn. The curriculum also covers advanced topics like natural language processing with NLTK and recommender systems, ensuring you gain a well-rounded understanding of data analysis.
This expertise not only makes you highly employable but also opens doors to lucrative career opportunities. Data analysts in the UK can earn between £30,000 and £60,000 per year, with further opportunities in specialised roles offering even higher salaries. Enrol in our Python for Data Analysis course today and acquire the skills needed to excel in the high-demand field of data analysis.
Learning Outcomes
After completing the Python for Data Analysis course, you will be able to:
- Understand and apply Python programming fundamentals.
- Use NumPy and Pandas for efficient data manipulation and analysis.
- Create insightful data visualisations with matplotlib, Seaborn, and Plotly.
- Build and evaluate machine learning models using scikit-learn.
- Perform data preprocessing and feature engineering.
- Implement natural language processing techniques using NLTK.
- Develop recommender systems to provide personalised recommendations.
- Automate data analysis tasks with Python scripting.
- Interpret and communicate data analysis results effectively.
How will I get my certificate?
You may have to take a quiz or a written test online during or after the course. After successfully completing the course, you will be eligible for the certificate.
Who is this course for?
There is no experience or previous qualifications required for enrolment on this Python for Data Analysis. It is available to all students, of all academic backgrounds.
Requirements
Our Python for Data Analysis is fully compatible with PC’s, Mac’s, Laptop, Tablet and Smartphone devices. This course has been designed to be fully compatible with tablets and smartphones so you can access your course on Wi-Fi, 3G or 4G.
There is no time limit for completing this course, it can be studied in your own time at your own pace.
Career path
Data Analyst:
- Analyse data to provide insights and support decision-making processes.
- Average UK salary: £30,000 – £50,000 per year.
Data Scientist:
- Develop advanced data models and algorithms to solve complex problems.
- Average UK salary: £40,000 – £70,000 per year.
Business Analyst:
- Use data analysis to understand business trends and recommend improvements.
- Average UK salary: £35,000 – £55,000 per year.
Machine Learning Engineer:
- Design and implement machine learning models and systems.
- Average UK salary: £45,000 – £80,000 per year.
Data Engineer:
- Build and maintain data pipelines and architectures.
- Average UK salary: £40,000 – £70,000 per year.
Research Analyst:
- Conduct detailed research and analysis to inform business strategies.
- Average UK salary: £30,000 – £50,000 per year.
Data Visualization Specialist:
- Create visual representations of data to help stakeholders understand complex information.
- Average UK salary: £35,000 – £60,000 per year.
AI Specialist:
- Develop and apply artificial intelligence solutions to various problems.
- Average UK salary: £50,000 – £90,000 per year.
FAQ
How long does it take to learn Python for data analysis?
The time it takes to learn Python for data analysis varies depending on your background and learning pace. With consistent effort, you can gain a good understanding of Python and its data analysis libraries within a few months.
Is Python better than R for data analysis?
Both Python and R have their strengths. Python is more versatile and can be used for a broader range of applications, including web development and machine learning. R is specialised for statistical analysis and visualisation. The choice depends on your specific needs and background.
How is the course assessed?
The Python for Data Analysis Course is assessed through an MCQ assessment. This MCQ assessment method is designed to evaluate your understanding of the course content and your ability to apply the knowledge and skills.
How do I purchase this Python for Data Analysis Course?
Click the “Take This Course” button. You can then adjust the course quantity if needed. Now, simply proceed to checkout. Enter your billing information and choose your preferred payment method. Once everything is filled out, click “Complete Purchase”. Welcome aboard! You’ll receive your login credentials via email shortly after purchase. Access the course platform using your login and start learning!
Course Curriculum
- 20 sections
- 100 lectures
- 00:00:00 total length
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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
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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
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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
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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
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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
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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
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Pandas Built-in Data Visualization
00:34:00 -
Pandas Data Visualization Exercises Overview
00:03:00 -
Panda Data Visualization Exercises Solutions
00:13:00
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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Resources- Python for Data Analysis
00:00:00
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Claim Your Certificate
00:00:00