# Data 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 modelPut 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.

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## Newly published

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## Last downloaded

- 4 seconds ago
#### How to use and implement the Interpolate-Lookup function

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Learn everything you need to know about Student's T Distribution.161free by 365 Data Science - 4 days ago
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Work paper to test reasonablness of Bonus payment compare to Salary and any indication of fraud.40Discuss - 4 days ago
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USING EXCEL TO AUTOMATE PROCESS IN XERO AND QBO? YES YOU CAN Using DataDear you are able to both pull and push data.128Discussfree by Lance Rubin - 6 days ago
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#### How to Use VLOOKUP and MATCH in Excel

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#### Decision Tree Algorithm & Analysis

Edureka gives a comprehensive tutorial on decision tree analysis with the help of examples.105Discussfree by Edureka - Earlier
#### Arrow: Australian Stock Analysis in Python

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#### OFFSET MATCH and Data Validation Excel Model Template

Quick and easy to use 2-tab Excel template for OFFSET MATCH and data validation.109Discussfree by Wall Street Prep - Earlier
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Whitepaper discussing the 4 main components for correctly validating machine learning models.69Discussfree by RapidMiner - Earlier
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Learn how to apply the Central Limit Theorem in Statistics.121free by 365 Data Science - Earlier
#### Data Science for Audit- Dividend Income Testing

Data Science for Audit, Testing Dividend Income Using Python48Discuss - Earlier
#### How to Classify Data | Types of Data

Read our article to find out the two main ways of classifying data.321free by 365 Data Science - Earlier
#### How to Create a Database

Learn more about basic database terminology before you start coding.311free by 365 Data Science - Earlier
#### The Top 5 Algorithms used in Data Science

This video discusses the 5 most widely used algorithms in Data Science and how to use them.98Discussfree by Edureka - Earlier
#### How to Measure Asymmetry with Skewness

The most commonly used tool to measure asymmetry is skewness. Learn more about it by checking out this article.192free by 365 Data Science - Earlier
#### Machine Learning Algorithms Tutorial

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Learn how to apply both functions, INDEX and MATCH, separately and combined on Excel.281free by 365 Data Science

## Full catalog

#### How to use and implement the Interpolate-Lookup function

This is a detailed guide on how to use and implement the Interpolate-Lookup function.204Discussfree by Prof. Ed Bodmer#### Python for Audit Testing (Valuation)

Data Science in External Auditing488Discuss#### How to Use Student's T Distribution

Learn everything you need to know about Student's T Distribution.161free by 365 Data Science#### How to Apply The Central Limit Theorem

Learn how to apply the Central Limit Theorem in Statistics.121free by 365 Data Science#### OFFSET MATCH and Data Validation Excel Model Template

Quick and easy to use 2-tab Excel template for OFFSET MATCH and data validation.109Discussfree by Wall Street Prep#### 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.93Discussfree by IDEAS#### Data analysis & dynamic reporting for cloud accounting Xero and QBO using Excel

128Discussfree by Lance Rubin#### How To Correctly Validate Machine Learning Models

Whitepaper discussing the 4 main components for correctly validating machine learning models.69Discussfree by RapidMiner#### How to Classify Data | Types of Data

Read our article to find out the two main ways of classifying data.321free by 365 Data Science#### How to Create a Database

Learn more about basic database terminology before you start coding.311free by 365 Data Science#### Machine Learning Algorithms Tutorial

Teaching the basics of machine learning, along with the ways in which you can use machine learning for problem solving.57Discussfree by Edureka#### The Top 5 Algorithms used in Data Science

This video discusses the 5 most widely used algorithms in Data Science and how to use them.98Discussfree by Edureka#### Decision Tree Algorithm & Analysis

Edureka gives a comprehensive tutorial on decision tree analysis with the help of examples.105Discussfree by Edureka#### How to Apply INDEX and MATCH Separately and Combined | Advanced Excel

Learn how to apply both functions, INDEX and MATCH, separately and combined on Excel.281free by 365 Data Science#### How 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.361free by 365 Data Science#### How to Measure Asymmetry with Skewness

192free by 365 Data Science- Have a Data Mining Technique to Share
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