Loughborough

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			<p>This course will enable you to bring value to the business by putting data science concepts into practice. Data is crucial for understanding where the business is and where it's headed. Not only can data reveal insights, but it can also inform - by guiding decisions and influencing day-to-day operations.</p>
			<div><i></i> 5 Days - 1 Exam</div>
			<div><i></i>  £595.00 incl. VAT</div>
			
			
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		<h3>Description</h3>
		<p></p><p>For a business to thrive in our data-driven world, it must treat data as one of its most important assets. Data is crucial for understanding where the business is and where it's headed. Not only can data reveal insights, but it can also inform - by guiding decisions and influencing day-to-day operations. This calls for a robust workforce of professionals who can analyze, understand, manipulate, and present data within an effective and repeatable process framework. In other words, the business world needs data science practitioners. This course will enable you to bring value to the business by putting data science concepts into practice. This course includes hands-on activities for each topic area.</p>
<h3>Course Objectives: </h3>
<p>In this course, you will implement data science techniques in order to address business issues. You will:</p>
<p>• Use data science principles to address business issues. <br />• Apply the extract, transform, and load (ETL) process to prepare datasets. <br />• Use multiple techniques to analyze data and extract valuable insights. <br />• Design a machine learning approach to address business issues. <br />• Train, tune, and evaluate classification models. <br />• Train, tune, and evaluate regression and forecasting models. <br />• Train, tune, and evaluate clustering models. <br />• Finalize a data science project by presenting models to an audience, putting models into production, and monitoring model performance.</p>
<p></p>
	
		<h3>Target Student</h3>
		<p></p><p>This course is designed for business professionals who leverage data to address business issues. The typical student in this course will have several years of experience with computing technology, including some aptitude in computer programming.  However, there is not necessarily a single organizational role that this course targets. A prospective student might be a programmer looking to expand their knowledge of how to guide business decisions by collecting, wrangling, analyzing, and manipulating data through code; or a data analyst with a background in applied math and statistics who wants to take their skills to the next level; or any number of other data-driven situations. </p>
<p>Ultimately, the target student is someone who wants to learn how to more effectively extract insights from their work and leverage that insight in addressing business issues, thereby bringing greater value to the business.  This course is also designed to assist students in preparing for the Certified Data Science Practitioner certification.</p>
<p></p>
	
		<h3>Prerequisites</h3>
		<p></p><p>To ensure your success in this course, you should have at least a high-level understanding of fundamental data science concepts, including, but not limited to: types of data, data science roles, the overall data science lifecycle, and the benefits and challenges of data science.</p>
<p>You should have also have experience with high-level programming languages like Python. Being comfortable using fundamental Python data science libraries like NumPy and pandas is highly recommended.</p>
<p>In addition to programming, you should also have experience working with databases, including querying languages like SQL. </p>
<p></p>
	
		<h3>Modules/Lessons</h3>
		<p></p><h4>Lesson 1: Addressing Business Issues with Data Science</h4>
<div>
<p>Topic A: Initiate a Data Science Project<br />Topic B: Formulate a Data Science Problem</p>
<h4>Lesson 2: Extracting, Transforming, and Loading Data</h4>
<p>Topic A: Extract Data <br />Topic B: Transform Data <br />Topic C: Load Data</p>
<h4>Lesson 3: Analyzing Data</h4>
<p>Topic A: Examine Data<br />Topic B: Explore the Underlying Distribution of Data <br />Topic C: Use Visualizations to Analyze Data<br />Topic D: Preprocess Data</p>
<h4>Lesson 4: Designing a Machine Learning Approach</h4>
<p>Topic A: Identify Machine Learning Concepts<br />Topic B: Test a Hypothesis</p>
</div>
<h4>Lesson 5: Developing Classification Models</h4>
<p>Topic A: Train and Tune Classification Models <br />Topic B: Evaluate Classification Models</p>
<h4>Lesson 6: Developing Regression Models</h4>
<p>Topic A: Train and Tune Regression Models <br />Topic B: Evaluate Regression Models</p>
<h4>Lesson 7: Developing Clustering Models</h4>
<p>Topic A: Train and Tune Clustering Models <br />Topic B: Evaluate Clustering Models</p>
<h4>Lesson 8: Finalizing a Data Science Project</h4>
<p>Topic A: Communicate Results to Stakeholders <br />Topic B: Demonstrate Models in a Web App <br />Topic C: Implement and Test Production Pipelines</p>
<p></p>

This course will enable you to bring value to the business by putting data science concepts into practice. Data is crucial for understanding where the business is and where it's headed. Not only can data reveal insights, but it can also inform - by guiding decisions and influencing day-to-day operations.
5 Days - 1 Exam £595.00 incl. VAT
Want to run this event in-house? Enquire about running this event in-house 
Interested in attending? Have a suggestion about running this event near you?
Register your interest now
Description

For a business to thrive in our data-driven world, it must treat data as one of its most important assets. Data is crucial for understanding where the business is and where it's headed. Not only can data reveal insights, but it can also inform - by guiding decisions and influencing day-to-day operations. This calls for a robust workforce of professionals who can analyze, understand, manipulate, and present data within an effective and repeatable process framework. In other words, the business world needs data science practitioners. This course will enable you to bring value to the business by putting data science concepts into practice. This course includes hands-on activities for each topic area.
Course Objectives: 
In this course, you will implement data science techniques in order to address business issues. You will:
• Use data science principles to address business issues. 
• Apply the extract, transform, and load (ETL) process to prepare datasets. 
• Use multiple techniques to analyze data and extract valuable insights. 
• Design a machine learning approach to address business issues. 
• Train, tune, and evaluate classification models. 
• Train, tune, and evaluate regression and forecasting models. 
• Train, tune, and evaluate clustering models. 
• Finalize a data science project by presenting models to an audience, putting models into production, and monitoring model performance.

Target Student

This course is designed for business professionals who leverage data to address business issues. The typical student in this course will have several years of experience with computing technology, including some aptitude in computer programming. However, there is not necessarily a single organizational role that this course targets. A prospective student might be a programmer looking to expand their knowledge of how to guide business decisions by collecting, wrangling, analyzing, and manipulating data through code; or a data analyst with a background in applied math and statistics who wants to take their skills to the next level; or any number of other data-driven situations. 
Ultimately, the target student is someone who wants to learn how to more effectively extract insights from their work and leverage that insight in addressing business issues, thereby bringing greater value to the business. This course is also designed to assist students in preparing for the Certified Data Science Practitioner certification.

Prerequisites

To ensure your success in this course, you should have at least a high-level understanding of fundamental data science concepts, including, but not limited to: types of data, data science roles, the overall data science lifecycle, and the benefits and challenges of data science.
You should have also have experience with high-level programming languages like Python. Being comfortable using fundamental Python data science libraries like NumPy and pandas is highly recommended.
In addition to programming, you should also have experience working with databases, including querying languages like SQL. 

Modules/Lessons

Lesson 1: Addressing Business Issues with Data Science
Topic A: Initiate a Data Science Project
Topic B: Formulate a Data Science Problem
Lesson 2: Extracting, Transforming, and Loading Data
Topic A: Extract Data 
Topic B: Transform Data 
Topic C: Load Data
Lesson 3: Analyzing Data
Topic A: Examine Data
Topic B: Explore the Underlying Distribution of Data 
Topic C: Use Visualizations to Analyze Data
Topic D: Preprocess Data
Lesson 4: Designing a Machine Learning Approach
Topic A: Identify Machine Learning Concepts
Topic B: Test a Hypothesis
Lesson 5: Developing Classification Models
Topic A: Train and Tune Classification Models 
Topic B: Evaluate Classification Models
Lesson 6: Developing Regression Models
Topic A: Train and Tune Regression Models 
Topic B: Evaluate Regression Models
Lesson 7: Developing Clustering Models
Topic A: Train and Tune Clustering Models 
Topic B: Evaluate Clustering Models
Lesson 8: Finalizing a Data Science Project
Topic A: Communicate Results to Stakeholders 
Topic B: Demonstrate Models in a Web App 
Topic C: Implement and Test Production Pipelines


Mpi Learning - Professional Learning And Development Provider

This course will enable you to bring value to the business by putting data science concepts into practice. Data is crucial for understanding where the business is and where it's headed. Not only can data reveal insights, but it can also inform - by guiding decisions and influencing day-to-day operations.

Certified Data Science Practitioner

General Admission

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			<p>The ability to identify and respond to changing trends is a hallmark of a successful business. Whether those trends are related to customers and sales, or to regulatory and industry standards, businesses are wise to keep track of the variables that can affect the bottom line. In today's business landscape, data comes from numerous sources and in diverse forms.</p>
			<div><i></i> 120 minutes</div>
			<div><i></i>  £50.00 incl. VAT</div>
			
			
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		<h3>Description</h3>
		<p></p><p>The ability to identify and respond to changing trends is a hallmark of a successful business. Whether those trends are related to customers and sales, or to regulatory and industry standards, businesses are wise to keep track of the variables that can affect the bottom line. In today's business landscape, data comes from numerous sources and in diverse forms. By leveraging data science concepts and technologies, businesses can mold all of that raw data into information that facilitates decisions to improve and expand the success of the business.</p>
<p></p>
	
		<h3>Modules/Lessons</h3>
		<p></p><h4><span><strong>Lesson 1:</strong> Data Science Fundamentals</span></h4>
<p>Topic A: What is Data Science?<br />Topic B: Types of Data<br />Topic C: The Data Science Lifecycle</p>
<h4><span><strong>Lesson 2:</strong> Data Science Implementation</span></h4>
<p>Topic A: Data Acquisition and Preparation<br />Topic B: Data Modeling and Visualization<br />Topic C: Data Science Roles</p>
<h4><span><strong>Lesson 3:</strong> The Impact of Data Science</span></h4>
<p>Topic A: Benefits of Data Science<br />Topic B: Challenges of Data Science<br />Topic C: Business Use Cases for Data Science</p>
<p></p>
	
		<h3>Target Student</h3>
		<p></p><p>This course is designed for business leaders and decision-makers, including C-level executives, project managers, HR leaders, Marketing and Sales leaders, and technical sales consultants, who want to increase their knowledge of and familiarity with concepts surrounding data science. Other individuals who want to know more about basic data science concepts are also candidates for this course.</p>
<p></p>

The ability to identify and respond to changing trends is a hallmark of a successful business. Whether those trends are related to customers and sales, or to regulatory and industry standards, businesses are wise to keep track of the variables that can affect the bottom line. In today's business landscape, data comes from numerous sources and in diverse forms.
120 minutes £50.00 incl. VAT
Want to run this event in-house? Enquire about running this event in-house 
Interested in attending? Have a suggestion about running this event near you?
Register your interest now
Description

The ability to identify and respond to changing trends is a hallmark of a successful business. Whether those trends are related to customers and sales, or to regulatory and industry standards, businesses are wise to keep track of the variables that can affect the bottom line. In today's business landscape, data comes from numerous sources and in diverse forms. By leveraging data science concepts and technologies, businesses can mold all of that raw data into information that facilitates decisions to improve and expand the success of the business.

Modules/Lessons

Lesson 1: Data Science Fundamentals
Topic A: What is Data Science?
Topic B: Types of Data
Topic C: The Data Science Lifecycle
Lesson 2: Data Science Implementation
Topic A: Data Acquisition and Preparation
Topic B: Data Modeling and Visualization
Topic C: Data Science Roles
Lesson 3: The Impact of Data Science
Topic A: Benefits of Data Science
Topic B: Challenges of Data Science
Topic C: Business Use Cases for Data Science

Target Student

This course is designed for business leaders and decision-makers, including C-level executives, project managers, HR leaders, Marketing and Sales leaders, and technical sales consultants, who want to increase their knowledge of and familiarity with concepts surrounding data science. Other individuals who want to know more about basic data science concepts are also candidates for this course.


The ability to identify and respond to changing trends is a hallmark of a successful business. Whether those trends are related to customers and sales, or to regulatory and industry standards, businesses are wise to keep track of the variables that can affect the bottom line. In today's business landscape, data comes from numerous sources and in diverse forms.

Data Science for Business Professionals

Medway

<p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Overview</strong></p><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">This is an 8-hour course aimed to help you learn what is meant by Data Science, what it involves and how it is often done, then to complete a small practical project to use some of the techniques and to get an idea of whether this is something that you might want to take further.</p><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">How you will learn</strong></p><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">You will be coached and mentored to complete the<em fr-original-style="" style="box-sizing: border-box;"> CISCO Skills for All </em>online course<em fr-original-style="" style="box-sizing: border-box;"> "Introduction to Data Science"</em> over a series of 3 two-hour, one per week. sessions with an opportunity to continue the learning in between sessions.</p><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">Each week there will be a half-hour workshop introducing a data tool (spreadsheet, visualisation, story-telling)</p><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">You will choose a data set that interests you and, in an final 2 hour session, you will complete a small practical project with your chosen dataset and one or more of the tools introduced in previous weeks.</p><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Topics</strong></p><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">You will find a list of topics covered in the online course here</p><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Achievements</strong></p><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">You will earn CISCO badges</p><ul fr-original-style="" style="box-sizing: border-box;"><li fr-original-style="" style="box-sizing: border-box;">Introduction to Data Science</li><li fr-original-style="" style="box-sizing: border-box;">Basics of Analytics</li><li fr-original-style="" style="box-sizing: border-box;">Data Collection</li><li fr-original-style="" style="box-sizing: border-box;">Artificial Intelligence and Machine Learning Concepts</li><li fr-original-style="" style="box-sizing: border-box;">Data Careers</li></ul><div fr-original-style="" style="box-sizing: border-box;"><span style="font-size: 16px; box-sizing: border-box;" fr-original-style="font-size: 16px;">and a statement of achievement from futureCodersSE CIC, with a shared link to your project to add to your CV or LinkedIn profile.</span></div><div fr-original-style="" style="box-sizing: border-box;"><br fr-original-style="" style="box-sizing: border-box;"></div>

Overview
This is an 8-hour course aimed to help you learn what is meant by Data Science, what it involves and how it is often done, then to complete a small practical project to use some of the techniques and to get an idea of whether this is something that you might want to take further.
How you will learn
You will be mentored to complete the CISCO Skills for All online course "Introduction to Data Science" over a series of 4 two-hour, one per week. sessions with an opportunity to continue the learning in between sessions.
Each week there will be a half-hour workshop introducing a data tool (spreadsheet, visualisation, story-telling)
You will choose a data set that interests you and, in an final 2 hour session, you will complete a small practical project with your chosen dataset and one or more of the tools introduced in previous weeks.
Topics
You will find a list of topics covered in the online course here
Achievements
You will earn CISCO badges
Introduction to Data Science
Basics of Analytics
Data Collection
Artificial Intelligence and Machine Learning Concepts
Data Careers
and a statement of achievement from futureCodersSE CIC, with a shared link to your project to add to your CV or LinkedIn profile.

futureCoders SE

Data Storytelling

Learn the basics of Data Science, combining a supported #CISCO Skills for All online course with practical learning and a project to help consolidate the learning.

Introduction to Data Science

General admission

<div id="course-pills-1">Duration<p>2 Days</p><p>12 CPD hours</p>This course is intended for<p>Audience: Data Scientists, Software Developers, IT Architects, and Technical Managers. Participants should have the general knowledge of statistics and programming<br />Also familiar with Python</p>Overview<p>? NumPy, pandas, Matplotlib,  scikit-learn<br />? Python REPLs<br />? Jupyter Notebooks<br />? Data analytics life-cycle phases<br />? Data repairing and normalizing<br />? Data aggregation and grouping<br />? Data visualization<br />? Data science algorithms for supervised and unsupervised machine learning</p><p>Covers theoretical and technical aspects of using Python in Applied Data Science projects and Data Logistics use cases.</p></div><div id="course-pills-2"><h4>Python for Data Science</h4><ul><li>? Using Modules</li><li>? Listing Methods in a Module</li><li>? Creating Your Own Modules</li><li>? List Comprehension</li><li>? Dictionary Comprehension</li><li>? String Comprehension</li><li>? Python 2 vs Python 3</li><li>? Sets (Python 3+)</li><li>? Python Idioms</li><li>? Python Data Science ?Ecosystem?</li><li>? NumPy</li><li>? NumPy Arrays</li><li>? NumPy Idioms</li><li>? pandas</li><li>? Data Wrangling with pandas' DataFrame</li><li>? SciPy</li><li>? Scikit-learn</li><li>? SciPy or scikit-learn?</li><li>? Matplotlib</li><li>? Python vs R</li><li>? Python on Apache Spark</li><li>? Python Dev Tools and REPLs</li><li>? Anaconda</li><li>? IPython</li><li>? Visual Studio Code</li><li>? Jupyter</li><li>? Jupyter Basic Commands</li><li>? Summary</li></ul><h4>Applied Data Science</h4><ul><li>? What is Data Science?</li><li>? Data Science Ecosystem</li><li>? Data Mining vs. Data Science</li><li>? Business Analytics vs. Data Science</li><li>? Data Science, Machine Learning, AI?</li><li>? Who is a Data Scientist?</li><li>? Data Science Skill Sets Venn Diagram</li><li>? Data Scientists at Work</li><li>? Examples of Data Science Projects</li><li>? An Example of a Data Product</li><li>? Applied Data Science at Google</li><li>? Data Science Gotchas</li><li>? Summary</li></ul><h4>Data Analytics Life-cycle Phases</h4><ul><li>? Big Data Analytics Pipeline</li><li>? Data Discovery Phase</li><li>? Data Harvesting Phase</li><li>? Data Priming Phase</li><li>? Data Logistics and Data Governance</li><li>? Exploratory Data Analysis</li><li>? Model Planning Phase</li><li>? Model Building Phase</li><li>? Communicating the Results</li><li>? Production Roll-out</li><li>? Summary</li></ul><h4>Repairing and Normalizing Data</h4><ul><li>? Repairing and Normalizing Data</li><li>? Dealing with the Missing Data</li><li>? Sample Data Set</li><li>? Getting Info on Null Data</li><li>? Dropping a Column</li><li>? Interpolating Missing Data in pandas</li><li>? Replacing the Missing Values with the Mean Value</li><li>? Scaling (Normalizing) the Data</li><li>? Data Preprocessing with scikit-learn</li><li>? Scaling with the scale() Function</li><li>? The MinMaxScaler Object</li><li>? Summary</li></ul><h4>Descriptive Statistics Computing Features in Python</h4><ul><li>? Descriptive Statistics</li><li>? Non-uniformity of a Probability Distribution</li><li>? Using NumPy for Calculating Descriptive Statistics Measures</li><li>? Finding Min and Max in NumPy</li><li>? Using pandas for Calculating Descriptive Statistics Measures</li><li>? Correlation</li><li>? Regression and Correlation</li><li>? Covariance</li><li>? Getting Pairwise Correlation and Covariance Measures</li><li>? Finding Min and Max in pandas DataFrame</li><li>? Summary</li></ul><h4>Data Aggregation and Grouping</h4><ul><li>? Data Aggregation and Grouping</li><li>? Sample Data Set</li><li>? The pandas.core.groupby.SeriesGroupBy Object</li><li>? Grouping by Two or More Columns</li><li>? Emulating the SQL's WHERE Clause</li><li>? The Pivot Tables</li><li>? Cross-Tabulation</li><li>? Summary</li></ul><h4>Data Visualization with matplotlib</h4><ul><li>? Data Visualization<br />? What is matplotlib?<br />? Getting Started with matplotlib<br />? The Plotting Window<br />? The Figure Options<br />? The matplotlib.pyplot.plot() Function<br />? The matplotlib.pyplot.bar() Function<br />? The matplotlib.pyplot.pie () Function<br />? Subplots<br />? Using the matplotlib.gridspec.GridSpec Object<br />? The matplotlib.pyplot.subplot() Function<br />? Hands-on Exercise<br />? Figures<br />? Saving Figures to File<br />? Visualization with pandas<br />? Working with matplotlib in Jupyter Notebooks<br />? Summary</li></ul><h4>Data Science and ML Algorithms in scikit-learn</h4><ul><li>? Data Science, Machine Learning, AI?</li><li>? Types of Machine Learning</li><li>? Terminology: Features and Observations</li><li>? Continuous and Categorical Features (Variables)</li><li>? Terminology: Axis</li><li>? The scikit-learn Package</li><li>? scikit-learn Estimators</li><li>? Models, Estimators, and Predictors</li><li>? Common Distance Metrics</li><li>? The Euclidean Metric</li><li>? The LIBSVM format</li><li>?  Scaling of the Features</li><li>? The Curse of Dimensionality</li><li>? Supervised vs Unsupervised Machine Learning</li><li>? Supervised Machine Learning Algorithms</li><li>? Unsupervised Machine Learning Algorithms</li><li>? Choose the Right Algorithm</li><li>? Life-cycles of Machine Learning Development</li><li>? Data Split for Training and Test Data Sets</li><li>? Data Splitting in scikit-learn</li><li>? Hands-on Exercise</li><li>? Classification Examples</li><li>? Classifying with k-Nearest Neighbors (SL)</li><li>? k-Nearest Neighbors Algorithm</li><li>? k-Nearest Neighbors Algorithm</li><li>? The Error Rate</li><li>? Hands-on Exercise</li><li>? Dimensionality Reduction</li><li>? The Advantages of Dimensionality Reduction</li><li>? Principal component analysis (PCA)</li><li>? Hands-on Exercise</li><li>? Data Blending</li><li>? Decision Trees (SL)</li><li>? Decision Tree Terminology</li><li>? Decision Tree Classification in Context of Information Theory</li><li>? Information Entropy Defined</li><li>? The Shannon Entropy Formula</li><li>? The Simplified Decision Tree Algorithm</li><li>? Using Decision Trees</li><li>? Random Forests</li><li>? SVM</li><li>? Naive Bayes Classifier (SL)</li><li>? Naive Bayesian Probabilistic Model in a Nutshell</li><li>? Bayes Formula</li><li>? Classification of Documents with Naive Bayes</li><li>? Unsupervised Learning Type: Clustering</li><li>? Clustering Examples</li><li>? k-Means Clustering (UL)</li><li>? k-Means Clustering in a Nutshell</li><li>? k-Means Characteristics</li><li>? Regression Analysis</li><li>? Simple Linear Regression Model</li><li>? Linear vs Non-Linear Regression</li><li>? Linear Regression Illustration</li><li>? Major Underlying Assumptions for Regression Analysis</li><li>? Least-Squares Method (LSM)</li><li>? Locally Weighted Linear Regression</li><li>? Regression Models in Excel</li><li>? Multiple Regression Analysis</li><li>? Logistic Regression</li><li>? Regression vs Classification</li><li>? Time-Series Analysis</li><li>? Decomposing Time-Series</li><li>? Summary</li></ul><h4>Lab Exercises</h4><ul><li>Lab 1 - Learning the Lab Environment     </li><li>Lab 2 -  Using Jupyter Notebook </li><li>Lab 3 -  Repairing and Normalizing Data  </li><li>Lab 4 -  Computing Descriptive Statistics </li><li>Lab 5 - Data Grouping and Aggregation   </li><li>Lab 6 - Data Visualization with matplotlib              </li><li>Lab 7 - Data Splitting       </li><li>Lab 8 -  k-Nearest Neighbors Algorithm  </li><li>Lab 9 -  The k-means Algorithm  </li><li>Lab 10 -  The Random Forest Algorithm </li></ul></div>

Duration
2 Days
12 CPD hours
This course is intended for
Audience: Data Scientists, Software Developers, IT Architects, and Technical Managers. Participants should have the general knowledge of statistics and programming
Also familiar with Python
Overview
? NumPy, pandas, Matplotlib, scikit-learn
? Python REPLs
? Jupyter Notebooks
? Data analytics life-cycle phases
? Data repairing and normalizing
? Data aggregation and grouping
? Data visualization
? Data science algorithms for supervised and unsupervised machine learning
Covers theoretical and technical aspects of using Python in Applied Data Science projects and Data Logistics use cases.
Python for Data Science
? Using Modules
? Listing Methods in a Module
? Creating Your Own Modules
? List Comprehension
? Dictionary Comprehension
? String Comprehension
? Python 2 vs Python 3
? Sets (Python 3+)
? Python Idioms
? Python Data Science ?Ecosystem?
? NumPy
? NumPy Arrays
? NumPy Idioms
? pandas
? Data Wrangling with pandas' DataFrame
? SciPy
? Scikit-learn
? SciPy or scikit-learn?
? Matplotlib
? Python vs R
? Python on Apache Spark
? Python Dev Tools and REPLs
? Anaconda
? IPython
? Visual Studio Code
? Jupyter
? Jupyter Basic Commands
? Summary
Applied Data Science
? What is Data Science?
? Data Science Ecosystem
? Data Mining vs. Data Science
? Business Analytics vs. Data Science
? Data Science, Machine Learning, AI?
? Who is a Data Scientist?
? Data Science Skill Sets Venn Diagram
? Data Scientists at Work
? Examples of Data Science Projects
? An Example of a Data Product
? Applied Data Science at Google
? Data Science Gotchas
? Summary
Data Analytics Life-cycle Phases
? Big Data Analytics Pipeline
? Data Discovery Phase
? Data Harvesting Phase
? Data Priming Phase
? Data Logistics and Data Governance
? Exploratory Data Analysis
? Model Planning Phase
? Model Building Phase
? Communicating the Results
? Production Roll-out
? Summary
Repairing and Normalizing Data
? Repairing and Normalizing Data
? Dealing with the Missing Data
? Sample Data Set
? Getting Info on Null Data
? Dropping a Column
? Interpolating Missing Data in pandas
? Replacing the Missing Values with the Mean Value
? Scaling (Normalizing) the Data
? Data Preprocessing with scikit-learn
? Scaling with the scale() Function
? The MinMaxScaler Object
? Summary
Descriptive Statistics Computing Features in Python
? Descriptive Statistics
? Non-uniformity of a Probability Distribution
? Using NumPy for Calculating Descriptive Statistics Measures
? Finding Min and Max in NumPy
? Using pandas for Calculating Descriptive Statistics Measures
? Correlation
? Regression and Correlation
? Covariance
? Getting Pairwise Correlation and Covariance Measures
? Finding Min and Max in pandas DataFrame
? Summary
Data Aggregation and Grouping
? Data Aggregation and Grouping
? Sample Data Set
? The pandas.core.groupby.SeriesGroupBy Object
? Grouping by Two or More Columns
? Emulating the SQL's WHERE Clause
? The Pivot Tables
? Cross-Tabulation
? Summary
Data Visualization with matplotlib
? Data Visualization
? What is matplotlib?
? Getting Started with matplotlib
? The Plotting Window
? The Figure Options
? The matplotlib.pyplot.plot() Function
? The matplotlib.pyplot.bar() Function
? The matplotlib.pyplot.pie () Function
? Subplots
? Using the matplotlib.gridspec.GridSpec Object
? The matplotlib.pyplot.subplot() Function
? Hands-on Exercise
? Figures
? Saving Figures to File
? Visualization with pandas
? Working with matplotlib in Jupyter Notebooks
? Summary
Data Science and ML Algorithms in scikit-learn
? Data Science, Machine Learning, AI?
? Types of Machine Learning
? Terminology: Features and Observations
? Continuous and Categorical Features (Variables)
? Terminology: Axis
? The scikit-learn Package
? scikit-learn Estimators
? Models, Estimators, and Predictors
? Common Distance Metrics
? The Euclidean Metric
? The LIBSVM format
? Scaling of the Features
? The Curse of Dimensionality
? Supervised vs Unsupervised Machine Learning
? Supervised Machine Learning Algorithms
? Unsupervised Machine Learning Algorithms
? Choose the Right Algorithm
? Life-cycles of Machine Learning Development
? Data Split for Training and Test Data Sets
? Data Splitting in scikit-learn
? Hands-on Exercise
? Classification Examples
? Classifying with k-Nearest Neighbors (SL)
? k-Nearest Neighbors Algorithm
? k-Nearest Neighbors Algorithm
? The Error Rate
? Hands-on Exercise
? Dimensionality Reduction
? The Advantages of Dimensionality Reduction
? Principal component analysis (PCA)
? Hands-on Exercise
? Data Blending
? Decision Trees (SL)
? Decision Tree Terminology
? Decision Tree Classification in Context of Information Theory
? Information Entropy Defined
? The Shannon Entropy Formula
? The Simplified Decision Tree Algorithm
? Using Decision Trees
? Random Forests
? SVM
? Naive Bayes Classifier (SL)
? Naive Bayesian Probabilistic Model in a Nutshell
? Bayes Formula
? Classification of Documents with Naive Bayes
? Unsupervised Learning Type: Clustering
? Clustering Examples
? k-Means Clustering (UL)
? k-Means Clustering in a Nutshell
? k-Means Characteristics
? Regression Analysis
? Simple Linear Regression Model
? Linear vs Non-Linear Regression
? Linear Regression Illustration
? Major Underlying Assumptions for Regression Analysis
? Least-Squares Method (LSM)
? Locally Weighted Linear Regression
? Regression Models in Excel
? Multiple Regression Analysis
? Logistic Regression
? Regression vs Classification
? Time-Series Analysis
? Decomposing Time-Series
? Summary
Lab Exercises
Lab 1 - Learning the Lab Environment
Lab 2 - Using Jupyter Notebook
Lab 3 - Repairing and Normalizing Data
Lab 4 - Computing Descriptive Statistics
Lab 5 - Data Grouping and Aggregation
Lab 6 - Data Visualization with matplotlib
Lab 7 - Data Splitting
Lab 8 - k-Nearest Neighbors Algorithm
Lab 9 - The k-means Algorithm
Lab 10 - The Random Forest Algorithm

Nexus Human

Data Engineering

Machine Learning (ML)

Big Data Analytics

Data Science

Data Mining

Data Skills

Python With Data Science

<div id="course-pills-1">Duration<p>0.5 Days</p><p>3 CPD hours</p>This course is intended for<p>This course is designed for business leaders and decision makers, including C-level executives, project managers, HR leaders, Marketing and Sales leaders, and technical sales consultants, who want to increase their knowledge of and familiarity with concepts surrounding data science. Other individuals who want to know more about basic data science concepts are also candidates for this course.<br />This course is also designed to assist learners in preparing for the CertNexus DSBIZâ¢ (Exam DSZ-110) credential.</p>Overview<p>In this course, you will identify how data science supports business decisions. You will:<br />Explain the fundamentals of data science<br />Describe common implementations of data science.<br />Identify the impact data science can have on a business</p><p>The ability to identify and respond to changing trends is a hallmark of a successful business. Whether those trends are related to customers and sales or to regulatory and industry standards, businesses are wise to keep track of the variables that can affect the bottom line. In today's business landscape, data comes from numerous sources and in diverse forms. By leveraging data science concepts and technologies, businesses can mold all of that raw data into information that facilitates decisions to improve and expand the success of the business.</p></div><div id="course-pills-2"><h4>Data Science Fundamentals</h4><ul><li>What is Data Science?</li><li>Types of Data</li><li>Data Science Roles</li></ul><h4>Data Science Implementation</h4><ul><li>The Data Science Lifecycle</li><li>Data Acquisition and Preparation</li><li>Data Modeling and Visualization</li></ul><h4>The Impact of Data Science</h4><ul><li>Benefits of Data Science</li><li>Challenges of Data Science</li><li>Business Use Cases for Data Science</li></ul></div><div id="course-pills-3"><h4>Additional course details:</h4><br /><p>Nexus Humans CertNexus Data Science for Business Professionals (DSBIZ) training program is a workshop that presents an invigorating mix of sessions, lessons, and masterclasses meticulously crafted to propel your learning expedition forward. </p><p></p> This immersive bootcamp-style experience boasts interactive lectures, hands-on labs, and collaborative hackathons, all strategically designed to fortify fundamental concepts. <p></p>Guided by seasoned coaches, each session offers priceless insights and practical skills crucial for honing your expertise. Whether you're stepping into the realm of professional skills or a seasoned professional, this comprehensive course ensures you're equipped with the knowledge and prowess necessary for success.  <p></p>While we feel this is the best course for the CertNexus Data Science for Business Professionals (DSBIZ) course and one of our Top 10 we encourage you to read the course outline to make sure it is the right content for you.  <p></p>Additionally, private sessions, closed classes or dedicated events are available both live online and at our training centres in Dublin and London, as well as at your offices anywhere in the UK, Ireland or across EMEA.<p></p></div>

Duration
0.5 Days
3 CPD hours
This course is intended for
This course is designed for business leaders and decision makers, including C-level executives, project managers, HR leaders, Marketing and Sales leaders, and technical sales consultants, who want to increase their knowledge of and familiarity with concepts surrounding data science. Other individuals who want to know more about basic data science concepts are also candidates for this course.
This course is also designed to assist learners in preparing for the CertNexus DSBIZâ¢ (Exam DSZ-110) credential.
Overview
In this course, you will identify how data science supports business decisions. You will:
Explain the fundamentals of data science
Describe common implementations of data science.
Identify the impact data science can have on a business
The ability to identify and respond to changing trends is a hallmark of a successful business. Whether those trends are related to customers and sales or to regulatory and industry standards, businesses are wise to keep track of the variables that can affect the bottom line. In today's business landscape, data comes from numerous sources and in diverse forms. By leveraging data science concepts and technologies, businesses can mold all of that raw data into information that facilitates decisions to improve and expand the success of the business.
Data Science Fundamentals
What is Data Science?
Types of Data
Data Science Roles
Data Science Implementation
The Data Science Lifecycle
Data Acquisition and Preparation
Data Modeling and Visualization
The Impact of Data Science
Benefits of Data Science
Challenges of Data Science
Business Use Cases for Data Science
Additional course details:
Nexus Humans CertNexus Data Science for Business Professionals (DSBIZ) training program is a workshop that presents an invigorating mix of sessions, lessons, and masterclasses meticulously crafted to propel your learning expedition forward. 

This immersive bootcamp-style experience boasts interactive lectures, hands-on labs, and collaborative hackathons, all strategically designed to fortify fundamental concepts.

Guided by seasoned coaches, each session offers priceless insights and practical skills crucial for honing your expertise. Whether you're stepping into the realm of professional skills or a seasoned professional, this comprehensive course ensures you're equipped with the knowledge and prowess necessary for success.

While we feel this is the best course for the CertNexus Data Science for Business Professionals (DSBIZ) course and one of our Top 10 we encourage you to read the course outline to make sure it is the right content for you.

Additionally, private sessions, closed classes or dedicated events are available both live online and at our training centres in Dublin and London, as well as at your offices anywhere in the UK, Ireland or across EMEA.


Service Design Courses (SDC)

Diploma for School Business Leaders (DSBM)

Diploma in Management Studies (DMS)

Trainers Course

CertNexus Data Science for Business Professionals (DSBIZ)

<div id="course-pills-1">Duration<p>5 Days</p><p>30 CPD hours</p>This course is intended for<p>This course is designed for business professionals who leverage data to address business issues. The typical student in this course will have several years of experience with computing technology, including some aptitude in computer programming. However, there is not necessarily a single organizational role that this course targets. A prospective student might be a programmer looking to expand their knowledge of how to guide business decisions by collecting, wrangling, analyzing, and manipulating data through code; or a data analyst with a background in applied math and statistics who wants to take their skills to the next level; or any number of other data-driven situations. Ultimately, the target student is someone who wants to learn how to more effectively extract insights from their work and leverage that insight in addressing business issues, thereby bringing greater value to the business.</p>Overview<p>In this course, you will learn to:<br />Use data science principles to address business issues.<br />Apply the extract, transform, and load (ETL) process to prepare datasets.<br />Use multiple techniques to analyze data and extract valuable insights.<br />Design a machine learning approach to address business issues.<br />Train, tune, and evaluate classification models.<br />Train, tune, and evaluate regression and forecasting models.<br />Train, tune, and evaluate clustering models.<br />Finalize a data science project by presenting models to an audience, putting models into production, and monitoring model performance.</p><p>For a business to thrive in our data-driven world, it must treat data as one of its most important assets. Data is crucial for understanding where the business is and where it's headed. Not only can data reveal insights, it can also inform?by guiding decisions and influencing day-to-day operations. This calls for a robust workforce of professionals who can analyze, understand, manipulate, and present data within an effective and repeatable process framework. In other words, the business world needs data science practitioners. This course will enable you to bring value to the business by putting data science concepts into practice<br /></p></div><div id="course-pills-2"><h4>Addressing Business Issues with Data Science</h4><ul><li>Topic A: Initiate a Data Science Project</li><li>Topic B: Formulate a Data Science Problem</li></ul><h4>Extracting, Transforming, and Loading Data</h4><ul><li>Topic A: Extract Data</li><li>Topic B: Transform Data</li><li>Topic C: Load Data</li></ul><h4>Analyzing Data</h4><ul><li>Topic A: Examine Data</li><li>Topic B: Explore the Underlying Distribution of Data</li><li>Topic C: Use Visualizations to Analyze Data</li><li>Topic D: Preprocess Data</li></ul><h4>Designing a Machine Learning Approach</h4><ul><li>Topic A: Identify Machine Learning Concepts</li><li>Topic B: Test a Hypothesis</li></ul><h4>Developing Classification Models</h4><ul><li>Topic A: Train and Tune Classification Models</li><li>Topic B: Evaluate Classification Models</li></ul><h4> Developing Regression Models</h4><ul><li>Topic A: Train and Tune Regression Models</li><li>Topic B: Evaluate Regression Models</li></ul><h4>Developing Clustering Models</h4><ul><li>Topic A: Train and Tune Clustering Models</li><li>Topic B: Evaluate Clustering Models</li></ul><h4>Finalizing a Data Science Project</h4><ul><li>Topic A: Communicate Results to Stakeholders</li><li>Topic B: Demonstrate Models in a Web App</li><li>Topic C: Implement and Test Production Pipelines</li></ul></div>

Duration
5 Days
30 CPD hours
This course is intended for
This course is designed for business professionals who leverage data to address business issues. The typical student in this course will have several years of experience with computing technology, including some aptitude in computer programming. However, there is not necessarily a single organizational role that this course targets. A prospective student might be a programmer looking to expand their knowledge of how to guide business decisions by collecting, wrangling, analyzing, and manipulating data through code; or a data analyst with a background in applied math and statistics who wants to take their skills to the next level; or any number of other data-driven situations. Ultimately, the target student is someone who wants to learn how to more effectively extract insights from their work and leverage that insight in addressing business issues, thereby bringing greater value to the business.
Overview
In this course, you will learn to:
Use data science principles to address business issues.
Apply the extract, transform, and load (ETL) process to prepare datasets.
Use multiple techniques to analyze data and extract valuable insights.
Design a machine learning approach to address business issues.
Train, tune, and evaluate classification models.
Train, tune, and evaluate regression and forecasting models.
Train, tune, and evaluate clustering models.
Finalize a data science project by presenting models to an audience, putting models into production, and monitoring model performance.
For a business to thrive in our data-driven world, it must treat data as one of its most important assets. Data is crucial for understanding where the business is and where it's headed. Not only can data reveal insights, it can also inform?by guiding decisions and influencing day-to-day operations. This calls for a robust workforce of professionals who can analyze, understand, manipulate, and present data within an effective and repeatable process framework. In other words, the business world needs data science practitioners. This course will enable you to bring value to the business by putting data science concepts into practice

Addressing Business Issues with Data Science
Topic A: Initiate a Data Science Project
Topic B: Formulate a Data Science Problem
Extracting, Transforming, and Loading Data
Topic A: Extract Data
Topic B: Transform Data
Topic C: Load Data
Analyzing Data
Topic A: Examine Data
Topic B: Explore the Underlying Distribution of Data
Topic C: Use Visualizations to Analyze Data
Topic D: Preprocess Data
Designing a Machine Learning Approach
Topic A: Identify Machine Learning Concepts
Topic B: Test a Hypothesis
Developing Classification Models
Topic A: Train and Tune Classification Models
Topic B: Evaluate Classification Models
Developing Regression Models
Topic A: Train and Tune Regression Models
Topic B: Evaluate Regression Models
Developing Clustering Models
Topic A: Train and Tune Clustering Models
Topic B: Evaluate Clustering Models
Finalizing a Data Science Project
Topic A: Communicate Results to Stakeholders
Topic B: Demonstrate Models in a Web App
Topic C: Implement and Test Production Pipelines

AWS Certified Cloud Practitioner

Certified in Data Protection (CDP)

CertNexus Certified Data Science Practitioner (CDSP)

<div id="course-pills-1">Duration<p>2 Days</p><p>12 CPD hours</p>This course is intended for<p>This introductory-level course is intended for Business Analysts and Data Analysts (or anyone else in the data science realm) who are already comfortable working with numerical data in Excel or other spreadsheet environments. No prior programming experience is required, and a browser is the only tool necessary for the course.</p>Overview<p>This course is approximately 50% hands-on, combining expert lecture, real-world demonstrations and group discussions with machine-based practical labs and exercises.  Our engaging instructors and mentors are highly experienced practitioners who bring years of current 'on-the-job' experience into every classroom.  Throughout the hands-on course students, will learn to leverage Python scripting for data science (to a basic level) using the most current and efficient skills and techniques.<br />Working in a hands-on learning environment, guided by our expert team, attendees will learn about and explore (to a basic level):<br />How to work with Python interactively in web notebooks<br />The essentials of Python scripting<br />Key concepts necessary to enter the world of Data Science via Python</p><p>This course introduces data analysts and business analysts (as well as anyone interested in Data Science) to the Python programming language, as it?s often used in Data Science in web notebooks.  This goal of this course is to provide students with a baseline understanding of core concepts that can serve as a platform of knowledge to follow up with more in-depth training and real-world practice.</p></div><div id="course-pills-2"><h4>An Overview of Python</h4><ul><li>Why Python?</li><li>Python in the Shell</li><li>Python in Web Notebooks (iPython, Jupyter, Zeppelin)</li><li>Demo: Python, Notebooks, and Data Science</li></ul><h4>Getting Started</h4><ul><li>Using variables </li><li>Builtin functions </li><li>Strings </li><li>Numbers</li><li>Converting among types </li><li>Writing to the screen </li><li>Command line parameters </li></ul><h4>Flow Control</h4><ul><li>About flow control </li><li>White space </li><li>Conditional expressions </li><li>Relational and Boolean operators </li><li>While loops </li><li>Alternate loop exits</li></ul><h4>Sequences, Arrays, Dictionaries and Sets</h4><ul><li>About sequences </li><li>Lists and list methods </li><li>Tuples </li><li>Indexing and slicing </li><li>Iterating through a sequence </li><li>Sequence functions, keywords, and operators </li><li>List comprehensions </li><li>Generator Expressions</li><li>Nested sequences </li><li>Working with Dictionaries</li><li>Working with Sets</li></ul><h4>Working with files</h4><ul><li>File overview</li><li>Opening a text file </li><li>Reading a text file </li><li>Writing to a text file</li><li>Reading and writing raw (binary) data</li></ul><h4>Functions</h4><ul><li>Defining functions </li><li>Parameters  </li><li>Global and local scope</li><li>Nested functions </li><li>Returning values </li></ul><h4>Essential Demos</h4><ul><li>Sorting</li><li>Exceptions</li><li>Importing Modules</li><li>Classes</li><li>Regular Expressions</li></ul><h4>The standard library</h4><ul><li>Math functions </li><li>The string module</li></ul><h4>Dates and times</h4><ul><li>Working with dates and times</li><li>Translating timestamps</li><li>Parsing dates from text</li><li>Formatting dates</li><li>Calendar data</li></ul><h4>Python and Data Science</h4><ul><li>Data Science Essentials</li><li>Pandas Overview</li><li>NumPy Overview</li><li>SciKit Overview</li><li>MatPlotLib Overview</li><li>Working with Python in Data Science</li></ul></div><div id="course-pills-3"><h4>Additional course details:</h4><br /><p>Nexus Humans Python for Data Science: Hands-on Technical Overview (TTPS4873) training program is a workshop that presents an invigorating mix of sessions, lessons, and masterclasses meticulously crafted to propel your learning expedition forward. </p><p></p> This immersive bootcamp-style experience boasts interactive lectures, hands-on labs, and collaborative hackathons, all strategically designed to fortify fundamental concepts. <p></p>Guided by seasoned coaches, each session offers priceless insights and practical skills crucial for honing your expertise. Whether you're stepping into the realm of professional skills or a seasoned professional, this comprehensive course ensures you're equipped with the knowledge and prowess necessary for success.  <p></p>While we feel this is the best course for the Python for Data Science: Hands-on Technical Overview (TTPS4873) course and one of our Top 10 we encourage you to read the course outline to make sure it is the right content for you.  <p></p>Additionally, private sessions, closed classes or dedicated events are available both live online and at our training centres in Dublin and London, as well as at your offices anywhere in the UK, Ireland or across EMEA.<p></p></div>

Duration
2 Days
12 CPD hours
This course is intended for
This introductory-level course is intended for Business Analysts and Data Analysts (or anyone else in the data science realm) who are already comfortable working with numerical data in Excel or other spreadsheet environments. No prior programming experience is required, and a browser is the only tool necessary for the course.
Overview
This course is approximately 50% hands-on, combining expert lecture, real-world demonstrations and group discussions with machine-based practical labs and exercises. Our engaging instructors and mentors are highly experienced practitioners who bring years of current 'on-the-job' experience into every classroom. Throughout the hands-on course students, will learn to leverage Python scripting for data science (to a basic level) using the most current and efficient skills and techniques.
Working in a hands-on learning environment, guided by our expert team, attendees will learn about and explore (to a basic level):
How to work with Python interactively in web notebooks
The essentials of Python scripting
Key concepts necessary to enter the world of Data Science via Python
This course introduces data analysts and business analysts (as well as anyone interested in Data Science) to the Python programming language, as it?s often used in Data Science in web notebooks. This goal of this course is to provide students with a baseline understanding of core concepts that can serve as a platform of knowledge to follow up with more in-depth training and real-world practice.
An Overview of Python
Why Python?
Python in the Shell
Python in Web Notebooks (iPython, Jupyter, Zeppelin)
Demo: Python, Notebooks, and Data Science
Getting Started
Using variables
Builtin functions
Strings
Numbers
Converting among types
Writing to the screen
Command line parameters
Flow Control
About flow control
White space
Conditional expressions
Relational and Boolean operators
While loops
Alternate loop exits
Sequences, Arrays, Dictionaries and Sets
About sequences
Lists and list methods
Tuples
Indexing and slicing
Iterating through a sequence
Sequence functions, keywords, and operators
List comprehensions
Generator Expressions
Nested sequences
Working with Dictionaries
Working with Sets
Working with files
File overview
Opening a text file
Reading a text file
Writing to a text file
Reading and writing raw (binary) data
Functions
Defining functions
Parameters
Global and local scope
Nested functions
Returning values
Essential Demos
Sorting
Exceptions
Importing Modules
Classes
Regular Expressions
The standard library
Math functions
The string module
Dates and times
Working with dates and times
Translating timestamps
Parsing dates from text
Formatting dates
Calendar data
Python and Data Science
Data Science Essentials
Pandas Overview
NumPy Overview
SciKit Overview
MatPlotLib Overview
Working with Python in Data Science
Additional course details:
Nexus Humans Python for Data Science: Hands-on Technical Overview (TTPS4873) training program is a workshop that presents an invigorating mix of sessions, lessons, and masterclasses meticulously crafted to propel your learning expedition forward. 

This immersive bootcamp-style experience boasts interactive lectures, hands-on labs, and collaborative hackathons, all strategically designed to fortify fundamental concepts.

Guided by seasoned coaches, each session offers priceless insights and practical skills crucial for honing your expertise. Whether you're stepping into the realm of professional skills or a seasoned professional, this comprehensive course ensures you're equipped with the knowledge and prowess necessary for success.

While we feel this is the best course for the Python for Data Science: Hands-on Technical Overview (TTPS4873) course and one of our Top 10 we encourage you to read the course outline to make sure it is the right content for you.

Additionally, private sessions, closed classes or dedicated events are available both live online and at our training centres in Dublin and London, as well as at your offices anywhere in the UK, Ireland or across EMEA.


NLP Practitioner

Professional Skills Course (PSC)

Python for Data Science: Hands-on Technical Overview (TTPS4873)

<div id="course-pills-1">Duration<p>3 Days</p><p>18 CPD hours</p>This course is intended for<p>Data Science for Marketing Analytics is designed for developers and marketing analysts looking to use new, more sophisticated tools in their marketing analytics efforts. It'll help if you have prior experience of coding in Python and knowledge of high school level mathematics. Some experience with databases, Excel, statistics, or Tableau is useful but not necessary.</p>Overview<p>By the end of this course, you will be able to build your own marketing reporting and interactive dashboard solutions.</p><p>The course starts by teaching you how to use Python libraries, such as pandas and Matplotlib, to read data from Python, manipulate it, and create plots, using both categorical and continuous variables. Then, you'll learn how to segment a population into groups and use different clustering techniques to evaluate customer segmentation.As you make your way through the course, you'll explore ways to evaluate and select the best segmentation approach, and go on to create a linear regression model on customer value data to predict lifetime value. In the concluding sections, you'll gain an understanding of regression techniques and tools for evaluating regression models, and explore ways to predict customer choice using classification algorithms. Finally, you'll apply these techniques to create a churn model for modeling customer product choices.</p></div><div id="course-pills-2"><h4>Data Preparation and Cleaning</h4><ul><li>Data Models and Structured Data</li><li>pandas</li><li>Data Manipulation</li></ul><h4>Data Exploration and Visualization</h4><ul><li>Identifying the Right Attributes</li><li>Generating Targeted Insights</li><li>Visualizing Data</li></ul><h4>Unsupervised Learning: Customer Segmentation</h4><ul><li>Customer Segmentation Methods</li><li>Similarity and Data Standardization</li><li>k-means Clustering</li></ul><h4>Choosing the Best Segmentation Approach</h4><ul><li>Choosing the Number of Clusters</li><li>Different Methods of Clustering</li><li>Evaluating Clustering</li></ul><h4>Predicting Customer Revenue Using Linear Regression </h4><ul><li>Understanding Regression</li><li>Feature Engineering for Regression</li><li>Performing and Interpreting Linear Regression</li></ul><h4>Other Regression Techniques and Tools for Evaluation</h4><ul><li>Evaluating the Accuracy of a Regression Model</li><li>Using Regularization for Feature Selection</li><li>Tree-Based Regression Models</li></ul><h4>Supervised Learning: Predicting Customer Churn</h4><ul><li>Classification Problems</li><li>Understanding Logistic Regression</li><li>Creating a Data Science Pipeline</li></ul><h4>Fine-Tuning Classification Algorithms</h4><ul><li>Support Vector Machine</li><li>Decision Trees</li><li>Random Forest</li><li>Preprocessing Data for Machine Learning Models</li><li>Model Evaluation</li><li>Performance Metrics</li></ul><h4>Modeling Customer Choice</h4><ul><li>Understanding Multiclass Classification</li><li>Class Imbalanced Data</li></ul></div><div id="course-pills-3"><h4>Additional course details:</h4><br /><p>Nexus Humans Data Science for Marketing Analytics training program is a workshop that presents an invigorating mix of sessions, lessons, and masterclasses meticulously crafted to propel your learning expedition forward. </p><p></p> This immersive bootcamp-style experience boasts interactive lectures, hands-on labs, and collaborative hackathons, all strategically designed to fortify fundamental concepts. <p></p>Guided by seasoned coaches, each session offers priceless insights and practical skills crucial for honing your expertise. Whether you're stepping into the realm of professional skills or a seasoned professional, this comprehensive course ensures you're equipped with the knowledge and prowess necessary for success.  <p></p>While we feel this is the best course for the Data Science for Marketing Analytics course and one of our Top 10 we encourage you to read the course outline to make sure it is the right content for you.  <p></p>Additionally, private sessions, closed classes or dedicated events are available both live online and at our training centres in Dublin and London, as well as at your offices anywhere in the UK, Ireland or across EMEA.<p></p></div>

Duration
3 Days
18 CPD hours
This course is intended for
Data Science for Marketing Analytics is designed for developers and marketing analysts looking to use new, more sophisticated tools in their marketing analytics efforts. It'll help if you have prior experience of coding in Python and knowledge of high school level mathematics. Some experience with databases, Excel, statistics, or Tableau is useful but not necessary.
Overview
By the end of this course, you will be able to build your own marketing reporting and interactive dashboard solutions.
The course starts by teaching you how to use Python libraries, such as pandas and Matplotlib, to read data from Python, manipulate it, and create plots, using both categorical and continuous variables. Then, you'll learn how to segment a population into groups and use different clustering techniques to evaluate customer segmentation.As you make your way through the course, you'll explore ways to evaluate and select the best segmentation approach, and go on to create a linear regression model on customer value data to predict lifetime value. In the concluding sections, you'll gain an understanding of regression techniques and tools for evaluating regression models, and explore ways to predict customer choice using classification algorithms. Finally, you'll apply these techniques to create a churn model for modeling customer product choices.
Data Preparation and Cleaning
Data Models and Structured Data
pandas
Data Manipulation
Data Exploration and Visualization
Identifying the Right Attributes
Generating Targeted Insights
Visualizing Data
Unsupervised Learning: Customer Segmentation
Customer Segmentation Methods
Similarity and Data Standardization
k-means Clustering
Choosing the Best Segmentation Approach
Choosing the Number of Clusters
Different Methods of Clustering
Evaluating Clustering
Predicting Customer Revenue Using Linear Regression
Understanding Regression
Feature Engineering for Regression
Performing and Interpreting Linear Regression
Other Regression Techniques and Tools for Evaluation
Evaluating the Accuracy of a Regression Model
Using Regularization for Feature Selection
Tree-Based Regression Models
Supervised Learning: Predicting Customer Churn
Classification Problems
Understanding Logistic Regression
Creating a Data Science Pipeline
Fine-Tuning Classification Algorithms
Support Vector Machine
Decision Trees
Random Forest
Preprocessing Data for Machine Learning Models
Model Evaluation
Performance Metrics
Modeling Customer Choice
Understanding Multiclass Classification
Class Imbalanced Data
Additional course details:
Nexus Humans Data Science for Marketing Analytics training program is a workshop that presents an invigorating mix of sessions, lessons, and masterclasses meticulously crafted to propel your learning expedition forward. 

This immersive bootcamp-style experience boasts interactive lectures, hands-on labs, and collaborative hackathons, all strategically designed to fortify fundamental concepts.

Guided by seasoned coaches, each session offers priceless insights and practical skills crucial for honing your expertise. Whether you're stepping into the realm of professional skills or a seasoned professional, this comprehensive course ensures you're equipped with the knowledge and prowess necessary for success.

While we feel this is the best course for the Data Science for Marketing Analytics course and one of our Top 10 we encourage you to read the course outline to make sure it is the right content for you.

Additionally, private sessions, closed classes or dedicated events are available both live online and at our training centres in Dublin and London, as well as at your offices anywhere in the UK, Ireland or across EMEA.


Online Course Marketing

Marketing Analytics

Data Science for Marketing Analytics

<div id="course-pills-1">Duration<p>1 Days</p><p>6 CPD hours</p>This course is intended for<p>This course is intended for:<br />A technical audience at an intermediate level</p>Overview<p>Using Amazon SageMaker, this course teaches you how to:<br />Prepare a dataset for training.<br />Train and evaluate a machine learning model.<br />Automatically tune a machine learning model.<br />Prepare a machine learning model for production.<br />Think critically about machine learning model results</p><p>In this course, learn how to solve a real-world use case with machine learning and produce actionable results using Amazon SageMaker. This course teaches you how to use Amazon SageMaker to cover the different stages of the typical data science process, from analyzing and visualizing a data set, to preparing the data and feature engineering, down to the practical aspects of model building, training, tuning and deployment.</p></div><div id="course-pills-2"><h4>Day 1</h4><ul><li>Business problem: Churn prediction<br />Load and display the dataset<br />Assess features and determine which Amazon SageMaker algorithm to use<br />Use Amazon Sagemaker to train, evaluate, and automatically tune the model<br />Deploy the model<br />Assess relative cost of errors</li></ul></div><div id="course-pills-3"><h4>Additional course details:</h4><br /><p>Nexus Humans Practical Data Science with Amazon SageMaker training program is a workshop that presents an invigorating mix of sessions, lessons, and masterclasses meticulously crafted to propel your learning expedition forward. </p><p></p> This immersive bootcamp-style experience boasts interactive lectures, hands-on labs, and collaborative hackathons, all strategically designed to fortify fundamental concepts. <p></p>Guided by seasoned coaches, each session offers priceless insights and practical skills crucial for honing your expertise. Whether you're stepping into the realm of professional skills or a seasoned professional, this comprehensive course ensures you're equipped with the knowledge and prowess necessary for success.  <p></p>While we feel this is the best course for the Practical Data Science with Amazon SageMaker course and one of our Top 10 we encourage you to read the course outline to make sure it is the right content for you.  <p></p>Additionally, private sessions, closed classes or dedicated events are available both live online and at our training centres in Dublin and London, as well as at your offices anywhere in the UK, Ireland or across EMEA.<p></p></div>

Duration
1 Days
6 CPD hours
This course is intended for
This course is intended for:
A technical audience at an intermediate level
Overview
Using Amazon SageMaker, this course teaches you how to:
Prepare a dataset for training.
Train and evaluate a machine learning model.
Automatically tune a machine learning model.
Prepare a machine learning model for production.
Think critically about machine learning model results
In this course, learn how to solve a real-world use case with machine learning and produce actionable results using Amazon SageMaker. This course teaches you how to use Amazon SageMaker to cover the different stages of the typical data science process, from analyzing and visualizing a data set, to preparing the data and feature engineering, down to the practical aspects of model building, training, tuning and deployment.
Day 1
Business problem: Churn prediction
Load and display the dataset
Assess features and determine which Amazon SageMaker algorithm to use
Use Amazon Sagemaker to train, evaluate, and automatically tune the model
Deploy the model
Assess relative cost of errors
Additional course details:
Nexus Humans Practical Data Science with Amazon SageMaker training program is a workshop that presents an invigorating mix of sessions, lessons, and masterclasses meticulously crafted to propel your learning expedition forward. 

This immersive bootcamp-style experience boasts interactive lectures, hands-on labs, and collaborative hackathons, all strategically designed to fortify fundamental concepts.

Guided by seasoned coaches, each session offers priceless insights and practical skills crucial for honing your expertise. Whether you're stepping into the realm of professional skills or a seasoned professional, this comprehensive course ensures you're equipped with the knowledge and prowess necessary for success.

While we feel this is the best course for the Practical Data Science with Amazon SageMaker course and one of our Top 10 we encourage you to read the course outline to make sure it is the right content for you.

Additionally, private sessions, closed classes or dedicated events are available both live online and at our training centres in Dublin and London, as well as at your offices anywhere in the UK, Ireland or across EMEA.


Practical Data Science with Amazon SageMaker

<div id="course-pills-1">Duration<p>5 Days</p><p>30 CPD hours</p>This course is intended for<p>This course is designed for students who want to learn the R programming language, particularly students who want to leverage R for data analysis and data science tasks in their organization. The course is also designed for students with an interest in applying statistics to real-world problems.<br />A typical student in this course should have several years of experience with computing technology, along with a proficiency in at least one other programming language.</p>Overview<p>In this course, you will use R to perform common data science tasks.You will:<br />Set up an R development environment and execute simple code.<br />Perform operations on atomic data types in R, including characters, numbers, and logicals.<br />Perform operations on data structures in R, including vectors, lists, and data frames.<br />Write conditional statements and loops.<br />Structure code for reuse with functions and packages.<br />Manage data by loading and saving datasets, manipulating data frames, and more.<br />Analyze data through exploratory analysis, statistical analysis, and more.<br />Create and format data visualizations using base R and ggplot2.<br />Create simple statistical models from data.</p><p>In our data-driven world, organizations need the right tools to extract valuable insights from that data. The R programming language is one of the tools at the forefront of data science. Its robust set of packages and statistical functions makes it a powerful choice for analyzing data, manipulating data, performing statistical tests on data, and creating predictive models from data. Likewise, R is notable for its strong data visualization tools, enabling you to create high-quality graphs and plots that are incredibly customizable.<br /><br />This course will teach you the fundamentals of programming in R to get you started. It will also teach you how to use R to perform common data science tasks and achieve data-driven results for the business.</p></div><div id="course-pills-2"><h4>Lesson 1: Setting Up R and Executing Simple Code</h4><ul><li>Topic A: Set Up the R Development Environment</li><li>Topic B: Write R Statements</li></ul><h4>Lesson 2: Processing Atomic Data Types</h4><ul><li>Topic A: Process Characters</li><li>Topic B: Process Numbers</li><li>Topic C: Process Logicals</li></ul><h4>Lesson 3: Processing Data Structures</h4><ul><li>Topic A: Process Vectors</li><li>Topic B: Process Factors</li><li>Topic C: Process Data Frames</li><li>Topic D: Subset Data Structures</li></ul><h4>Lesson 4: Writing Conditional Statements and Loops</h4><ul><li>Topic A: Write Conditional Statements</li><li>Topic B: Write Loops</li></ul><h4>Lesson 5: Structuring Code for Reuse</h4><ul><li>Topic A: Define and Call Functions</li><li>Topic B: Apply Loop Functions</li><li>Topic C: Manage R Packages</li></ul><h4>Lesson 6: Managing Data in R</h4><ul><li>Topic A: Load Data</li><li>Topic B: Save Data</li><li>Topic C: Manipulate Data Frames Using Base R</li><li>Topic D: Manipulate Data Frames Using dplyr</li><li>Topic E: Handle Dates and Times</li></ul><h4>Lesson 7: Analyzing Data in R</h4><ul><li>Topic A: Examine Data</li><li>Topic B: Explore the Underlying Distribution of Data</li><li>Topic C: Identify Missing Values</li></ul><h4>Lesson 8: Visualizing Data in R</h4><ul><li>Topic A: Plot Data Using Base R Functions</li><li>Topic B: Plot Data Using ggplot2</li><li>Topic C: Format Plots in ggplot2</li><li>Topic D: Create Combination Plots</li></ul><h4>Lesson 9: Modeling Data in R</h4><ul><li>Topic A: Create Statistical Models in R</li><li>Topic B: Create Machine Learning Models in R</li></ul></div>

Duration
5 Days
30 CPD hours
This course is intended for
This course is designed for students who want to learn the R programming language, particularly students who want to leverage R for data analysis and data science tasks in their organization. The course is also designed for students with an interest in applying statistics to real-world problems.
A typical student in this course should have several years of experience with computing technology, along with a proficiency in at least one other programming language.
Overview
In this course, you will use R to perform common data science tasks.You will:
Set up an R development environment and execute simple code.
Perform operations on atomic data types in R, including characters, numbers, and logicals.
Perform operations on data structures in R, including vectors, lists, and data frames.
Write conditional statements and loops.
Structure code for reuse with functions and packages.
Manage data by loading and saving datasets, manipulating data frames, and more.
Analyze data through exploratory analysis, statistical analysis, and more.
Create and format data visualizations using base R and ggplot2.
Create simple statistical models from data.
In our data-driven world, organizations need the right tools to extract valuable insights from that data. The R programming language is one of the tools at the forefront of data science. Its robust set of packages and statistical functions makes it a powerful choice for analyzing data, manipulating data, performing statistical tests on data, and creating predictive models from data. Likewise, R is notable for its strong data visualization tools, enabling you to create high-quality graphs and plots that are incredibly customizable.

This course will teach you the fundamentals of programming in R to get you started. It will also teach you how to use R to perform common data science tasks and achieve data-driven results for the business.
Lesson 1: Setting Up R and Executing Simple Code
Topic A: Set Up the R Development Environment
Topic B: Write R Statements
Lesson 2: Processing Atomic Data Types
Topic A: Process Characters
Topic B: Process Numbers
Topic C: Process Logicals
Lesson 3: Processing Data Structures
Topic A: Process Vectors
Topic B: Process Factors
Topic C: Process Data Frames
Topic D: Subset Data Structures
Lesson 4: Writing Conditional Statements and Loops
Topic A: Write Conditional Statements
Topic B: Write Loops
Lesson 5: Structuring Code for Reuse
Topic A: Define and Call Functions
Topic B: Apply Loop Functions
Topic C: Manage R Packages
Lesson 6: Managing Data in R
Topic A: Load Data
Topic B: Save Data
Topic C: Manipulate Data Frames Using Base R
Topic D: Manipulate Data Frames Using dplyr
Topic E: Handle Dates and Times
Lesson 7: Analyzing Data in R
Topic A: Examine Data
Topic B: Explore the Underlying Distribution of Data
Topic C: Identify Missing Values
Lesson 8: Visualizing Data in R
Topic A: Plot Data Using Base R Functions
Topic B: Plot Data Using ggplot2
Topic C: Format Plots in ggplot2
Topic D: Create Combination Plots
Lesson 9: Modeling Data in R
Topic A: Create Statistical Models in R
Topic B: Create Machine Learning Models in R

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