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.
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WHY BUY THIS CERTIFICATE IN DATA SCIENCE AND VISUALISATION WITH MACHINE LEARNING
AT QLS LEVEL 3?
1. Unlimited access to the course for a lifetime.
2. Opportunity to earn a certificate accredited by the CPD Quality
Standards after completing this course.
3. Structured lesson planning in line with industry standards.
4. Immerse yourself in innovative and captivating course materials and
activities.
5. Assessments are designed to evaluate advanced cognitive abilities and skill
proficiency.
6. 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.
7. 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, Specificityetc. 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