How To Correctly Validate Machine Learning Models
Whitepaper discussing the 4 main components for correctly validating machine learning models.
39 remove_red_eyeDiscuss
free
by RapidMiner
by RapidMinerData Science techniques
What is data science?
Data science is a multidisciplinary field that uses a range of techniques in order to extract data, draw insights, and solve analytical problems. With the end goal normally being to create business value.
The fields that are involved in data science vary from mathematics, statistics, information science, and computer science.
Data science has been growing in importance due to the rise of 'big data'. The increase in the size of data, and its more unstructured form, means that it is less manageable to analyse. Data science has therefore become an important field in which to deal with these issues.
What are the techniques of data science?
There are a wealth of techniques used by data scientists, some of these include:
Linear Regression: This is the linear approach, i.e. a graphical representation on a straight line, which models the relationship of a dependent variable and independent variable in order to predict a target variable.
Clustering: This is where you divide and sort data points into specific groups so that the data points share similar traits. There are two types of clustering, hard clustering - where data points either fit into a group or they don't -, and soft clustering - this is where the probability of a particular data point being in the category is made.
Association analysis: This is where machine learning models analyse data points in a database for patterns, and consequently identifies 'if-then' associations, also known as 'association rules'. After this analysis, you are able to see the commonly occurring associations. Follow this link to find a more detailed definition of association analysis.
Logistic Regression: This type of model, frequently used in statistics, uses a logistic curve, or logistic function, for modelling a binary dependent variable, overcoming the classification problem. Read this useful article on logistic regression to learn more.
What are the main phases in data science?
Data Science involves many stages in order to reach the end goal. These can include:
Discover: This stage involves the formulation of the initial hypothesis, after the framing of the business problem. It is also necessary to evaluate the resources needed for the project.
Data Preparation: This is where search for, pre-process, and ready the data needed for the modeling process. This may involve preparing the analytics sandbox.
Model planning: This stage requires planning of the methods and techniques that are needed in order to draw relevant results.
Model building: Following planning, you collate the methods and techniques so that they form a model
Put in to the model practice running the data.
Present results: After collecting all the results, it is necessary to translate them into a more efficient and concise presentation.
To find out more about data science and the techniques necessary, please refer to these webpages:
To find techniques and templates for data science, please refer to the tools on Eloquens below.
Most popular techniques
Machine Learning Algorithms Tutorial
Teaching the basics of machine learning, along with the ways in which you can use machine learning for problem solving.32 remove_red_eyeDiscussfreeby EdurekaThe Top 5 Algorithms used in Data Science
This video discusses the 5 most widely used algorithms in Data Science and how to use them.56 remove_red_eyeDiscussfreeby EdurekaHow to Learn Machine Learning in 6 Months
Senior Data Scientist Zach Millar explains how you can learn machine learning in 6 months through a roadmap process.71 remove_red_eyeDiscussfreeby IDEASHow to Apply INDEX and MATCH Separately and Combined | Advanced Excel
Learn how to apply both functions, INDEX and MATCH, separately and combined on Excel.New!DiscussHow to Classify Data | Types of Data
Read our article to find out the two main ways of classifying data.New!DiscussHow to Use Student's T Distribution
Learn everything you need to know about Student's T Distribution.New!DiscussHow to Create a Database
Learn more about basic database terminology before you start coding.New!DiscussHow to Differentiate Database and Spreadsheet
In this post, we will focus on the differences between database vs spreadsheet.New!DiscussBuilding Robust Machine Learning Models
This presentation focuses on the fundamentals of building robust machine learning models.32 remove_red_eyeDiscussMeasuring Model Performance
Video tutorial on how to measure your model's performance.22 remove_red_eyeDiscussfreeby Data CampChoosing the Right Machine Learning Algorithm
Seth Mottaghinejad discusses the things we should be thinking about when choosing a machine learning algorithm.39 remove_red_eyeDiscussLinear Regression Algorithm Tutorial
Edureka explains the basics of linear regression with the use of examples and use cases.52 remove_red_eyeDiscussfreeby EdurekaDecision Tree Algorithm & Analysis
Edureka gives a comprehensive tutorial on decision tree analysis with the help of examples.41 remove_red_eyeDiscussfreeby EdurekaHow to use and implement the Interpolate-Lookup function
This is a detailed guide on how to use and implement the Interpolate-Lookup function.New!DiscussHow to Include Dummy Variables into a Regression
Learn how to include Dummy Variables into a Regression.New!Discuss
Newly published
Data Science in Audit Investment Valuation Testing PSX
Testing year-end Investment Valuation of PSX listed companiesNew!DiscussHow to use and implement the Interpolate-Lookup function
This is a detailed guide on how to use and implement the Interpolate-Lookup function.New!DiscussData Science for Audit- Dividend Income Testing
Data Science for Audit, Testing Dividend Income Using PythonNew!DiscussPython for Audit Testing (Valuation)
Data Science in External AuditingNew!DiscussHow to Define Relational Database Essentials
Learn about the two main types of databases.New!DiscussHow to Differentiate Database and Spreadsheet
In this post, we will focus on the differences between database vs spreadsheet.New!DiscussHow to Create a Database
Learn more about basic database terminology before you start coding.New!DiscussHow to Add a Second “if” Statement | ELIF
Learn an elegant way of adding a second “if” statement to one of our expressions.New!DiscussHow to Define Python Tuples
Python tuples are another type of data sequences, but differently to lists, they are immutable...New!DiscussHow to Use Conditionals and Loops in Python
Let’s see how to combine conditionals and loops in Python.New!DiscussHow to Measure Asymmetry with Skewness
The most commonly used tool to measure asymmetry is skewness. Learn more about it by checking out this article.New!DiscussHow to Handle large data tables with ease | VLOOKUP COLUMN and ROW
Learn how to handle large data tables with ease!New!DiscussHow to Use the Simple Linear Regression Model | Geometrical Representation
Find out how to use the simple linear regression model through geometrical representation.New!DiscussHow to Use VLOOKUP and MATCH in Excel
We’ve seen several function combinations so far. In this lesson, we’ll present another one that can be useful.New!DiscussHow to Perform the Population vs Sample Data Check
The first step of every statistical analysis you will perform is the population vs sample data check.New!DiscussHow to Define and Use the Simple Linear Regression Model
Learn everything you need to know about the simple linear regression model.New!Discuss
Full catalog
How to Learn Machine Learning in 6 Months
Senior Data Scientist Zach Millar explains how you can learn machine learning in 6 months through a roadmap process.71 remove_red_eyeDiscussfreeby IDEASHow To Correctly Validate Machine Learning Models
Whitepaper discussing the 4 main components for correctly validating machine learning models.39 remove_red_eyeDiscussfreeby RapidMinerMachine Learning Algorithms Tutorial
Teaching the basics of machine learning, along with the ways in which you can use machine learning for problem solving.32 remove_red_eyeDiscussfreeby EdurekaThe Top 5 Algorithms used in Data Science
This video discusses the 5 most widely used algorithms in Data Science and how to use them.56 remove_red_eyeDiscussfreeby EdurekaHow to Apply INDEX and MATCH Separately and Combined | Advanced Excel
Learn how to apply both functions, INDEX and MATCH, separately and combined on Excel.New!DiscussHow to Classify Data | Types of Data
Read our article to find out the two main ways of classifying data.New!DiscussHow to Use Student's T Distribution
Learn everything you need to know about Student's T Distribution.New!DiscussHow to Create a Database
Learn more about basic database terminology before you start coding.New!DiscussHow to Differentiate Database and Spreadsheet
In this post, we will focus on the differences between database vs spreadsheet.New!DiscussBuilding Robust Machine Learning Models
This presentation focuses on the fundamentals of building robust machine learning models.32 remove_red_eyeDiscussMeasuring Model Performance
Video tutorial on how to measure your model's performance.22 remove_red_eyeDiscussfreeby Data CampChoosing the Right Machine Learning Algorithm
Seth Mottaghinejad discusses the things we should be thinking about when choosing a machine learning algorithm.39 remove_red_eyeDiscussLinear Regression Algorithm Tutorial
Edureka explains the basics of linear regression with the use of examples and use cases.52 remove_red_eyeDiscussfreeby EdurekaDecision Tree Algorithm & Analysis
Edureka gives a comprehensive tutorial on decision tree analysis with the help of examples.41 remove_red_eyeDiscussfreeby EdurekaHow to use and implement the Interpolate-Lookup function
This is a detailed guide on how to use and implement the Interpolate-Lookup function.New!DiscussHow to Include Dummy Variables into a Regression
Learn how to include Dummy Variables into a Regression.New!Discuss- Have a Data Mining Technique to Share
Your Data Science Technique
Publish your technique
Learn more about digital publishing
Most popular Data Science expert author Channels (last 30 days)
Prof. Ed Bodmer
Consultant and Workshop Leader
1365 Data Science
Data Science Education Platform
2Rizwan Ahmed Surhio
Experienced Auditor with Data Analytics Skills
3RapidMiner
Data Science on an Enterprise Scale
4Edureka
Interactive e-learning platform.
5Data Science Dojo
Data Science for everyone.
6Data Camp
Data Camp's aim is to enable you to become a data science expert through practical learning.
7Conference Videos
Conference Videos allows you to watch the TechEd North America Conference from your computer.
8IDEAS
International Data Engineering and Science Association
9
Related categories
Any questions on Data Science?
The user community is here to help. Go ahead!
