20 techniques of data analysis No pictures

Here are 20 techniques of data analysis:

    Descriptive Statistics: This involves summarizing the basic features of a data set, such as measures of central tendency (mean, median, mode) and measures of dispersion (variance, standard deviation).

    Inferential Statistics: This allows you to make conclusions about a population based on a sample of data. It includes techniques like hypothesis testing, confidence intervals, and correlation analysis.

    Regression Analysis: This is a statistical method used to estimate the relationship between a dependent variable and one or more independent variables.

    Time Series Analysis: This is used to analyze data points collected at successive points in time. It helps to identify trends, seasonality, and other patterns in time-related data.

    Cohort Analysis: This technique focuses on understanding the behavior of specific groups or cohorts over time. It is commonly used in marketing and customer relationship management (CRM) to track customer acquisition, retention, and churn.

    Survival Analysis: This is used to analyze data related to the time it takes for an event to occur, such as customer churn, product failure, or patient recovery.

    Content Analysis: This is a qualitative data analysis technique used to study the content of textual, visual, or multimedia data. It helps to identify themes, patterns, and biases in the data.

    Sentiment Analysis: This is a technique used to understand the emotional tone of text data, such as social media posts, customer reviews, or survey responses.

    Market Basket Analysis: This is used to identify patterns of products that are frequently purchased together. This helps retailers develop targeted promotions and optimize product placement.

    Social Network Analysis: This is used to analyze the structure and relationships within social networks. It can be used to identify influencers, understand information flow, and track the spread of ideas.

    Clustering: This is a data mining technique used to group data points into clusters based on their similarities. It is used to identify patterns and segment data for further analysis.

    Classification: This is a machine learning technique used to classify data points into predefined categories. It is used for tasks such as spam filtering, fraud detection, and image recognition.

    Dimensionality Reduction: This is a technique used to reduce the number of variables in a data set while retaining as much information as possible. This is useful for preparing data for machine learning algorithms and for data visualization.

    Anomaly Detection: This is used to identify data points that deviate significantly from the expected pattern. It can be used for fraud detection, system monitoring, and network security.

    Text Analysis: This is a broad category of techniques used to extract meaning from textual data. It includes tasks such as text pre-processing, tokenization, stemming, and lemmatization.

    Forecasting: This is the process of predicting future events based on past data and trends. It is used for tasks such as sales forecasting, demand forecasting, and financial forecasting.

    Data Visualization: This is the process of creating visual representations of data. It helps to communicate insights and patterns from data in a clear and concise way.

    Data Wrangling: This is the process of cleaning, transforming, and preparing data for analysis. It includes tasks such as handling missing values, correcting inconsistencies, and formatting data.

    Data Storytelling: This is the art of using data to communicate a story or message. It helps to make data analysis more engaging and impactful for audiences.

    A/B Testing: This is a statistical method used to compare two versions of something to see which one performs better. It is commonly used in web design, marketing, and product development.