Certificate in Data Science and Visualisation with Machine Learning at QLS Level 3

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Certificate in Data Science and Visualisation with Machine Learning at QLS Level 3

Certificate in Data Science and Visualisation with Machine Learning at QLS Level 3

Level: Level 3 Certificate
Modules: 100 Modules
Duration: 1 day
Students: 715

Are you ready to be at the helm, steering the ship into a realm where data is the new gold? In the infinite world of data, where information spirals at breakneck speed, lies a universe rich in potential and discovery: the domain of Data Science and Visualisation. This “Certificate in Data Science and Visualisation with Machine Learning at QLS Level 3” course unravels the wonders of extracting meaningful insights using Python, the worldwide leading language of data experts. Harnessing the strength of Python, you’ll delve deep into data analysis, experience the finesse of visualisation tools, and master the art of Machine Learning.

The need to understand, interpret, and act on this data has become paramount, with vast amounts of data increasing the digital sphere. Envision a canvas where raw numbers are transformed into visually compelling stories, and machine learning models foretell future trends. This course provides a meticulous pathway for anyone eager to learn the data representation paradigms backed by Python’s robust libraries.

Dive into a curriculum rich with analytical explorations, visual artistry, and machine learning predictions.

Learning Outcomes

Understanding the foundations and functionalities of Python, focusing on its application in data science.
Applying various Python libraries like NumPy and Pandas for effective data analysis.
Demonstrating proficiency in creating detailed visual narratives using tools like matplotlib, Seaborn, and Plotly.
Implementing Machine Learning algorithms in Python using scikit-learn, ranging from regression models to clustering techniques.
Designing and executing a holistic data analysis and visualisation project, encapsulating all learned techniques.
Exploring advanced topics, encompassing recommender systems and natural language processing with Python.
Attaining the confidence to independently analyse complex data sets and translate them into actionable insights.

Why buy this Certificate in Data Science and Visualisation with Machine Learning at QLS Level 3?

Unlimited access to the course for a lifetime.
Opportunity to earn a certificate accredited by the CPD Quality Standards after completing this course.
Structured lesson planning in line with industry standards.
Immerse yourself in innovative and captivating course materials and activities.
Assessments are designed to evaluate advanced cognitive abilities and skill proficiency.
Flexibility to complete the Certificate in Data Science and Visualisation with Machine Learning at QLS Level 3 Course at your own pace, on your own schedule.
Receive full tutor support throughout the week, from Monday to Friday, to enhance your learning experience.

Who is this Certificate in Data Science and Visualisation with Machine Learning at QLS Level 3 course for?

Aspiring data scientists aiming to harness the power of Python.
Researchers keen to enrich their analytical and visualisation skills.
Analysts aiming to add machine learning to their toolkit.
Developers striving to integrate data analytics into applications.
Business professionals desiring data-driven decision-making capabilities.

Career path

Data Scientist: £55,000 – £85,000 Per Annum
Machine Learning Engineer: £60,000 – £90,000 Per Annum
Data Analyst: £30,000 – £50,000 Per Annum
Data Visualisation Specialist: £45,000 – £70,000 Per Annum
Natural Language Processing Specialist: £65,000 – £95,000 Per Annum
Business Intelligence Developer: £40,000 – £65,000 Per Annum

Prerequisites

This Certificate in Data Science and Visualisation with Machine Learning at QLS Level 3 does not require you to have any prior qualifications or experience. You can just enrol and start learning. This Certificate in Data Science and Visualisation with Machine Learning at QLS Level 3 was made by professionals and it is compatible with all PC’s, Mac’s, tablets and smartphones. You will be able to access the course from anywhere at any time as long as you have a good enough internet connection.

Certification

After studying the course materials, there will be a written assignment test which you can take at the end of the course. After successfully passing the test you will be able to claim the pdf certificate for £4.99 Original Hard Copy certificates need to be ordered at an additional cost of £8.

Endorsed Certificate of Achievement from the Quality Licence Scheme

Learners will be able to achieve an endorsed certificate after completing the course as proof of their achievement. You can order the endorsed certificate for only £85 to be delivered to your home by post. For international students, there is an additional postage charge of £10.

Endorsement

The Quality Licence Scheme (QLS) has endorsed this course for its high-quality, non-regulated provision and training programmes. The QLS is a UK-based organisation that sets standards for non-regulated training and learning. This endorsement means that the course has been reviewed and approved by the QLS and meets the highest quality standards.

Please Note: Studyhub is a Compliance Central approved resale partner for Quality Licence Scheme Endorsed courses.

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
Resources
	Resources – Data Science and Visualisation with Machine Learning	00:00:00
Order your QLS Endorsed Certificate
	Order your QLS Endorsed Certificate	00:00:00