Satori Analytics’ Post

Such a simple concept, yet an important one! We often overlook the detailed analysis of one column, it may yield you all sorts of results ! #UnivariateAnalysis #EDA #DataCleaning #DataScienceBasics

Data Analysis tip; 💎 Univariate Analysis A DataFrame is made up of different variables, which are represented by columns. Univariate analysis involves examining the distribution and statistical properties of each variable (column) in isolation. This is important when you want to get a deep understanding of the values in a column. Univariate analysis includes summary statistics, visualizations, and outlier detection. For example, we can use a box plot for outlier detection. Outliers are data points that are significantly different from the rest of the dataset. Outliers can significantly impact the results and insights derived from the data. Identifying outliers allows you to verify their validity and determine if they are genuine data points or errors that need correction. In the example below, we use the box plot to catch outliers in the "Height" column.

2. EXPLORATORY DATA NALYSIS linkcode Let's proceed with the Exploratory Data Analysis (EDA): 1. Univariate Analysis: In this analysis, we will look at each variable individually. This could include looking at the distribution of a variable, checking for outliers, or checking the central tendency (mean, median, mode) of the variable. 2. Bivariate Analysis: In this analysis, we will look at the relationship between two variables. This could include looking at correlation, cross-tabulation, or using visualization to observe the relationship. 3. Multivariate Analysis: In this analysis, we will look at the interactions between multiple variables at once. This could include complex visualization, factor analysis, or other statistical methods

Exploratory Data Analysis, The purpose of this analysis is to understand the data before preprocess and build the algorithms, data models. It used statistical technique to visualise the data. various steps ivolved in statistical methods likely, Missing and invalid data handling Median,standard deviation, quartile,covariance,outliers of the data Visualisation, Box plot _ finding outliers,skewness,spread of the data line chart _ context of time series data. compare two variables over a time to find trends,cycles,patterns over specified period. Bar chart _ categorical data frequency of each data Histogram _ visualize distribution of variable Scatter plot _ visualize relationship between two continuous variables. Types of analysis -- univariate analysis bivariate analysis multivariate analysis

Effectively handling missing data is crucial for accurate data analysis, but choosing the right method can be challenging. Stochastic Regression Imputation and Predictive Mean Matching are both techniques commonly used to handle missing data, but they differ significantly in their approach and effectiveness, especially in heteroscedastic data contexts. 🔹 Stochastic Regression Imputation involves creating a regression model to predict missing values based on other available data. Once the model is built, the missing value is estimated, and a random error component is added to introduce variability. 🔹 Predictive Mean Matching (PMM) selects a donor from the observed data whose predicted values (from a regression model) are closest to the predicted missing value. The actual value from this donor is then used to impute the missing data. 🔍 Appropriateness for Heteroscedastic Data: 🔹 Stochastic Regression Imputation might not be ideal for heteroscedastic data due to its reliance on the assumption of homoscedasticity (constant variance) in the regression model. This can lead to biased and inconsistent imputations if the variance of the target variable significantly varies with the predictors. 🔹 Predictive Mean Matching is better suited for such conditions. Since it uses actual observed values from similar cases, PMM naturally adapts to varying variances and is less likely to introduce bias based on the model's assumptions. In summary, while both methods have their merits, Predictive Mean Matching is typically a more reliable choice for handling missing values in heteroscedastic data due to its flexibility and lower dependency on the distributional assumptions of the regression model. I've created a tutorial explaining how to use Predictive Mean Matching Imputation in R programming. More info: https://lnkd.in/eN5Z4P6 #rstats #statistics #missingdata

𝗠𝗮𝘀𝘁𝗲𝗿𝗶𝗻𝗴 𝗦𝗤𝗟 𝘃𝘀 𝗣𝘆𝗦𝗽𝗮𝗿𝗸 𝗗𝗮𝘁𝗮 𝗧𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝗮𝘁𝗶𝗼𝗻𝘀 Unlock the power of SQL and PySpark for data transformations with a detailed comparison between the two! 💡 → SQL helps you handle data with precision: SELECT for retrieving data efficiently. WHERE to filter data for targeted insights. JOIN for combining datasets smoothly. GROUP BY and HAVING for aggregating data effectively. → PySpark brings the same power to big data processing: filter() for clean data selection. groupBy() and agg() for smart aggregations. join() for merging datasets in parallel processing. In a world of evolving data technologies, it’s essential to master both tools. SQL is perfect for relational databases, while PySpark is your go-to for processing large datasets at scale in distributed environments. I’ve put together a guide to help you understand how PySpark and SQL can achieve similar results, each with its unique strengths. 🚀 Resource Credit - Unknown to me. Check it out and transform your skills👍 Some outstanding data professionals you should definitely follow! Aishwarya Srinivasan - Sumit Mittal - Darshil Parmar - Ankit Bansal - Rishabh Mishra - Zach Morris Wilson - Dhaval Patel - Benjamin Rogojan - Alex Freberg - Ankur Ranjan - Brij kishore Pandey - Munna Das - Deepak Goyal - Shashank Mishra 🇮🇳 - Ajay Kadiyala - Ankita Gulati ♻️ Like or Repost to help others prepare and succeed!

Earlier on, I wrote about data cleaning. In it, I mentioned outlier detection. Let's delve deeper into Outlier Detection and Treatment. Outliers can significantly impact the accuracy of statistical models and machine learning algorithms. Here are some common techniques to identify and handle outliers: Identifying Outliers: Univariate Methods: Box Plots: Visually identify outliers based on their position relative to the quartiles. Z-Score: Calculate the number of standard deviations a data point is from the mean. Outliers typically have Z-scores greater than 3 or less than -3. Interquartile Range (IQR): Identify outliers based on their distance from the quartiles. Multivariate Methods: Mahalanobis Distance: Measures the distance of a data point from the multivariate mean. Clustering: Group similar data points and identify outliers as those that don't belong to any cluster. Handling Outliers: Trimming: Remove outliers from the dataset. This method is suitable when outliers are few and clearly identifiable. Capping: Replace outliers with a predefined limit. This can be useful when outliers are due to measurement errors or data entry mistakes. Winsorization: Replace outliers with a specified percentile value (e.g., 5th or 95th percentile). Log Transformation: Compress the range of values, especially for skewed data. Robust Statistical Methods: Use statistical methods that are less sensitive to outliers, such as robust regression or median-based statistics. It's important to note that the best approach for handling outliers depends on the specific context and the nature of the data. Ann Kibe Your informed data analyst. #dataanalyst hashtag #outliers

An insightful visual representation of the core and shared skillset of a Data Scientist / Analyst / Engineer 📈. While these roles have overlapping expertise, each brings its distinct strengths to the data field #data #datafield

    Detailed Explanation of Data Analysis Techniques This is digital era, where everyone is working on data, every step you need to follow some data, analysis of data will give you best way of making decision for regular and special jobs. I. Univariate Techniques  A. Non-Metric Data 1. Chi-Square Goodness of Fit Test 2. Graphical Presentation: Pie Charts, Bar Charts   B. Metric Data: Metric data, also referred to as numerical or quantitative data, are values on a continuous or discrete scale (e.g., age, height). i. Non-Normal Data  a. Graphical Presentation b. Statistical Tests: 1. Chi-Square Goodness of Fit Test 2. Kolmogorov-Smirnov (KS) Test 3. Runs Test ii. Normal Data: For data that follow a normal distribution, use these parametric tests 1. Student’s One-Sample t-Test 2. Z-Test   II. Bivariate Techniques: These methods examine relationships or differences between two variables, which can be either metric or non-metric. A. Non-Metric Data (Both Variables): When both variables are categorical, the focus is on identifying associations or dependencies: a. Cross Tabulation: b. Multiple/Divisional Bar Charts c. Chi-Square Test of Independence B. Metric Data + Non-Metric Data: When one variable is   numerical  and the other is categorical: i. Matric + Cat.=2 (Two Groups) 1. Mann-Whitney U Test  (for non-normal data) 2. Independent t-Test  (for normal data) ii. Matric + Cat.>2 (More Than Two Groups) 1. Kruskal-Wallis H Test  (for non-normal data) 2. ANOVA (F-Test)  (for normal data) C. Metric Data (Both Variables): When both variables are numerical, these techniques evaluate relationships or differences between the variables: i. Dependent Sample- When the same sample is measured twice or pairs are inherently linked 1. Wilcoxon Signed-Rank Test (for non-normal data) 2. Paired t-Test (for normal data) ii. Independent Sample: When two samples are independent of each other: 1. Correlation 2. Regression Analysis Conclusion  This framework categorizes various statistical tools based on data type and distribution, ensuring analysts select the most appropriate method. This structured approach optimizes the accuracy and relevance of insights derived from data analysis. #DataAnalysis #ChiSquareTest #ANOVA #RegressionAnalysis #Correlation #NonParametricTests # Woxsen University # Liberal Arts & Humanities# ||Raul Villamarin Rodriguez|| Dr. Konrad Gunesch|| Dr. Ravikant Kisana|| Dr. Rohin Gaddam || Dr. Areeba Moazzam ||Dr. T Seikhotinthang Haokip ||Dr. Imran Muzaffar|| Dr. Suantak Demkhosei Vaiphei Ph.D.||Dr. Naveen Kolloju || Dr. Srinivas Junuguru|| Dr. Siriman Naveen, Ph.D. || Dr. Pankaj Kumar Verma || Dr. Sunaina Arya, PhD || Dheeraj Rayalu Tadi ||Dr. Dinesh C.|| Dr. Chakradhaar Indurkar|| Dr Shah Khatoon || Dr. Satyanarayana Turangi || Dr. Jyotasana . || Abhishikta Pramanik || || Dr. Dibyangana Biswas|| Dr. Antarleena Basu, Ph.D.|| Dr. RAJU CHAKETI|| Dr. Mahesh Admankar ||

Data Exploration Exploring a dataset involves understanding its structure, contents, and underlying patterns before performing any detailed analysis or building models. By thoroughly exploring your dataset, you can gain valuable insights, identify potential issues, and guide subsequent analysis or modeling steps effectively. A typical data exploration involves the following methods and steps in practice using Pandas. •Understanding the Structure: *Types of Data: Identifying the type of data you are working with (e.g., numerical, categorical, datetime). *Dimensions: Checking the number of rows and columns using ( .shape). *Data Types: Understanding the data types of each column (integer, float, string, etc.) Using (.info()). •Summary Statistics: *Descriptive Statistics: Calculating measures such as mean, median, mode, standard deviation, min, max, quartiles for numerical data using (.describe()). *Distribution: Analyzing the distribution of the data using histograms, box plots, etc. *Frequency Counts: For categorical data, examine the frequency of each category using ( .value_counts()). •Data Quality Checks: *Missing Values: Identifying missing data using ( .isnull().sum()). *Outliers: Detecting and understanding outliers in the data using ( Domain knowledge, z_score, standard deviation, etc). *Consistency: Ensuring consistency in data entries, especially for categorical data (e.g., no mixed-case or spelling errors). •Relationships in Data: *Correlation: Investigating correlations between numerical variables using correlation coefficients and scatter plots. *Patterns: Identifying patterns and relationships in the data, such as trends over time or clusters of data points. •Visualization: *Creating visual representations of the data to better understand its characteristics. Common visualizations include: Histograms, Box Plots, Scatter Plots, Line Graphs, Bar Charts Heatmaps. •Contextual Understanding: •Domain Knowledge: Applying knowledge from the domain relevant to the data to interpret the findings correctly. *Objectives: Keeping the analysis aligned with the specific goals or questions you aim to address. Please like, comment, and connect.