10 data analytic techniques

 

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1. Descriptive Statistics: You are a data analyst working for a music streaming service. Analyze a dataset of listening habits and summarize the key characteristics, such as the most popular genres, the average listening time per user, and the most popular songs among different age groups.

2. Inferential Statistics: A company is considering launching a new marketing campaign. You are tasked with analyzing data from a previous campaign to determine if there was a statistically significant increase in sales after the campaign launched.

3. Data Visualization: Create a visually compelling infographic that represents the global distribution of internet users by continent.

4. Regression Analysis: A university wants to predict student graduation rates based on factors such as high school GPA, standardized test scores, and participation in extracurricular activities. Build a regression model to help them achieve this goal.

5. Hypothesis Testing: A social media platform claims that their new algorithm increases user engagement. Design a hypothesis test to determine if this claim is statistically supported.

6. Time Series Analysis: Analyze the daily stock prices of a company over the past year. Identify any trends, seasonality, or other patterns in the data.

7. Cohort Analysis: A clothing retailer wants to understand the buying habits of their customers who signed up for their loyalty program in the last year. Conduct a cohort analysis to compare their purchase behavior with other customer groups.

8. Cluster Analysis: Analyze a dataset of customer demographics and purchase history to identify different customer segments with similar characteristics.

9. Machine Learning: Develop a machine learning model that can automatically classify customer support tickets based on the issue they describe.

10. Text Mining: Analyze a collection of online reviews for a new restaurant to identify the most common themes and sentiment expressed by the customers.

 10 data analytic techniques:

1. Descriptive Statistics: This technique involves summarizing the main characteristics of a data set. It provides basic information about the data, such as the mean, median, mode, range, and standard deviation.

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   Descriptive Statistics data analytic technique

2. Inferential Statistics: This technique allows you to draw conclusions about a population based on a sample of data. It involves using statistical tests to determine if there is a statistically significant relationship between two or more variables.

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   Inferential Statistics data analytic technique

3. Data Visualization: This technique involves creating visual representations of data, such as charts, graphs, and maps. Data visualization can help you to identify patterns, trends, and relationships in data that would be difficult to see otherwise.

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   Data Visualization data analytic technique

4. Regression Analysis: This technique is used to model the relationship between two or more variables. It can be used to predict the value of one variable (dependent variable) based on the values of other variables (independent variables).

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   Regression Analysis data analytic technique

5. Hypothesis Testing: This technique is used to test a claim or prediction about a population. It involves formulating a null hypothesis and an alternative hypothesis, and then using statistical tests to determine which hypothesis is more likely to be true.

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   Hypothesis Testing data analytic technique

6. Time Series Analysis: This technique is used to analyze data that is collected over time. It can be used to identify trends, seasonality, and other patterns in time-based data.

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   Time Series Analysis data analytic technique

7. Cohort Analysis: This technique is used to compare groups of individuals who share a common experience, such as being born in the same year or joining a company on the same day. It can be used to track the behavior of these groups over time and to identify differences between them.

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   Cohort Analysis data analytic technique

8. Cluster Analysis: This technique is used to group data points into clusters based on their similarities. It can be used to identify different subgroups within a population and to understand the characteristics of these subgroups.

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   Cluster Analysis data analytic technique

9. Machine Learning: This technique involves using algorithms to learn from data and make predictions. Machine learning can be used for a variety of tasks, such as classification, regression, and forecasting.

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   Machine Learning data analytic technique

10. Text Mining: This technique is used to extract insights from text data, such as customer reviews, social media posts, and news articles. Text mining can be used to identify trends, sentiment, and other patterns in text data.

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    Text Mining data analytic technique

These are just a few of the many data analytic techniques that are available. The best technique to use will depend on the specific data you are working with and the question you are trying to answer.