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Machine Learning Basics for a Newbie
6 Steps of Machine learning Lifecycle Introduction to Predictive Modeling
Introduction to Exploratory Data Analysis & Data Insights Descriptive Statistics Inferential Statistics How to Understand Population Distributions?
Reading Data Files into Python Different Variable Datatypes
Probability for Data Science Basic Concepts of Probability Axioms of Probability Conditional Probability
Central Tendencies for Continuous Variables Spread of Data KDE plots for Continuous Variable Overview of Distribution for Continuous variables Normal Distribution Skewed Distribution Skeweness and Kurtosis Distribution for Continuous Variable
Central Tendencies for Categorical Variables Understanding Discrete Distributions Performing EDA on Categorical Variables
Dealing with Missing Values Understanding Outliers Identifying Outliers in Data Outlier Detection in Python Outliers Detection Using IQR, Z-score, LOF and DBSCAN
Sample and Population Central Limit Theorem Confidence Interval and Margin of Error
Bivariate Analysis Introduction
Covariance Pearson Correlation Spearman's Correlation & Kendall's Tau Correlation versus Causation Tabular and Graphical methods for Bivariate Analysis Performing Bivariate Analysis on Continuous-Continuous Variables
Tabular and Graphical methods for Continuous-Categorical Variables Introduction to Hypothesis Testing P-value Two sample Z-test T-test T-test vs Z-test Performing Bivariate Analysis on Continuous-Catagorical variables
Chi-Squares Test Bivariate Analysis on Categorical Categorical Variables
Multivariate Analysis A Comprehensive Guide to Data Exploration The Data Science behind IPL
Supervised Learning vs Unsupervised Learning Reinforcement Learning Generative and Descriminative Models Parametric and Non Parametric model
Machine Learning Pipeline Preparing Dataset Build a Benchmark Model: Regression Build a Benchmark Model: Classification
Evaluation Metrics for Machine Learning Everyone should know Confusion Matrix Accuracy Precision and Recall AUC-ROC Log Loss R2 and Adjusted R2
Dealing with Missing Values Replacing Missing Values Imputing Missing Values in Data Working with Categorical Variables Working with Outliers Preprocessing Data for Model Building
Understanding Cost Function Understanding Gradient Descent Math Behind Gradient Descent Assumptions of Linear Regression Implement Linear Regression from Scratch Train Linear Regression in Python Implementing Linear Regression in R Diagnosing Residual Plots in Linear Regression Models Generalized Linear Models Introduction to Logistic Regression Odds Ratio Implementing Logistic Regression from Scratch Introduction to Scikit-learn in Python Train Logistic Regression in python Multiclass using Logistic Regression How to use Multinomial and Ordinal Logistic Regression in R ? Challenges with Linear Regression Introduction to Regularisation Implementing Regularisation Ridge Regression Lasso Regression
Introduction to K Nearest Neighbours Determining the Right Value of K in KNN Implement KNN from Scratch Implement KNN in Python
Bias Variance Tradeoff Introduction to Overfitting and Underfitting Visualizing Overfitting and Underfitting Selecting the Right Model What is Validation? Hold-Out Validation Understanding K Fold Cross Validation
Introduction to Feature Selection Feature Selection Algorithms Missing Value Ratio Low Variance Filter High Correlation Filter Backward Feature Elimination Forward Feature Selection Implement Feature Selection in Python Implement Feature Selection in R
Introduction to Decision Tree Purity in Decision Tree Terminologies Related to Decision Tree How to Select Best Split Point in Decision Tree? Chi-Squares Information Gain Reduction in Variance Optimizing Performance of Decision Tree Train Decision Tree using Scikit Learn Pruning of Decision Trees
Introduction to Feature Engineering Feature Transformation Feature Scaling Feature Engineering Frequency Encoding Automated Feature Engineering: Feature Tools
Introduction to Naive Bayes Conditional Probability and Bayes Theorem Introduction to Bayesian Adjustment Rating: The Incredible Concept Behind Online Ratings! Working of Naive Bayes Math behind Naive Bayes Types of Naive Bayes Implementation of Naive Bayes
Understanding how to solve Multiclass and Multilabled Classification Problem Evaluation Metrics: Multi Class Classification
Introduction to Ensemble Techniques Basic Ensemble Techniques Implementing Basic Ensemble Techniques Finding Optimal Weights of Ensemble Learner using Neural Network Why Ensemble Models Work well?
Introduction to Stacking Implementing Stacking Variants of Stacking Implementing Variants of Stacking Introduction to Blending Bootstrap Sampling Introduction to Random Sampling Hyper-parameters of Random Forest Implementing Random Forest Out-of-Bag (OOB) Score in the Random Forest IPL Team Win Prediction Project Using Machine Learning Introduction to Boosting Gradient Boosting Algorithm Math behind GBM Implementing GBM in python Regularized Greedy Forests Extreme Gradient Boosting Implementing XGBM in python Tuning Hyperparameters of XGBoost in Python Implement XGBM in R/H2O Adaptive Boosting Implementing Adaptive Boosing LightGBM Implementing LightGBM in Python Catboost Implementing Catboost in Python
Different Hyperparameter Tuning methods Implementing Different Hyperparameter Tuning methods GridsearchCV RandomizedsearchCV Bayesian Optimization for Hyperparameter Tuning Hyperopt
Understanding SVM Algorithm SVM Kernels In-depth Intuition and Practical Implementation SVM Kernel Tricks Kernels and Hyperparameters in SVM Implementing SVM from Scratch in Python and R
Introduction to Principal Component Analysis Steps to Perform Principal Compound Analysis Computation of Covariance Matrix Finding Eigenvectors and Eigenvalues Implementing PCA in python Visualizing PCA A Brief Introduction to Linear Discriminant Analysis Introduction to Factor Analysis
Introduction to Clustering Applications of Clustering Evaluation Metrics for Clustering Understanding K-Means Implementation of K-Means in Python Implementation of K-Means in R Choosing Right Value for K Profiling Market Segments using K-Means Clustering Hierarchical Clustering Implementation of Hierarchial Clustering DBSCAN Defining Similarity between clusters Build Better and Accurate Clusters with Gaussian Mixture Models
Understand Basics of Recommendation Engine with Case Study
8 Ways to Improve Accuracy of Machine Learning Models
Introduction to Dask Working with CuML
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to AutoML Implementation of MLBox Introduction to PyCaret TPOT Auto-Sklearn EvalML
Pickle and Joblib Introduction to Model Deployment
Deploying Machine Learning Model using Streamlit Deploying ML Models in Docker Deploy Using Streamlit Deploy on Heroku Deploy Using Netlify Introduction to Amazon Sagemaker Setting up Amazon SageMaker Using SageMaker Endpoint to Generate Inference Deploy on Microsoft Azure Cloud Introduction to Flask for Model Deploying ML model using Flask
Model Deployment in Android Model Deployment in Iphone

Data Analytics vs Data Analysis, Are they similar?

S
Shankar Last Updated : 05 May, 2021
5 min read
This article was published as a part of the Data Science Blogathon.

If you have a basic knowledge of tech, you must have come across the terms: Data Analytics and Data Analysis. Have you ever thought of the difference or the similarity between them? Many times people have a misconception that both are the same. Where do you stand in this argument? Do you think they are the same or similar?

The answer is ‘No! they are not the same. They have considerable differences between them like the two chutneys: Onion Chutney and Coconut Chutney. They both are used as a side dish for the well-known South Indian dish Idli. Since both are chutneys in general that doesn’t mean they are the same in-depth. Without Idli, there is no worth for both. Similarly, there is not much importance for both the terms Data Analytics and Data Analytics without the Data.

What are Data Analytics and Data Analysis?

What are Data Analytics and Data Analysis?

Literally, “Analysis” is the detailed examination of the elements or structure of something. On the other side “Analytics” is the systematic computational analysis of data or statistics. In detail, Data Analytics is a wide area involving handling data with a lot of necessary tools to produce helpful decisions with useful predictions for a better output, while Data Analysis is actually a subset of Data Analytics which helps us to understand the data by questioning and to collect useful insights from the information already available.

In simple terms, Data Analytics is the process of exploring the data from the past to make appropriate decisions in the future by using valuable insights. Whereas Data Analysis helps in understanding the data and provides required insights from the past to understand what happened so far.

The reason behind why the Data receives that much attraction

The reason behind why the Data receives that much attraction

Now let’s have a small and quick discussion in between this discussion to know why they are the hot tech topics in recent days. Both the concepts run around the information called the Data. Everyone knows that Data is a collection of information, but nowadays information is the richest wealth when compared to all the other wealth including Gold, Diamond, Fuel, etc.

It is because, with Data, one can rule the world only if they know how to use it. Even the world-famous tech giants like Google, Microsoft, Amazon, and other companies collect data and analyze it for various purposes, primarily to improve customer’s feed by analyzing customer preferences and their mindsets, the reason is customers are the wealth givers for any commercial industry.

 

Hot tech talks of 21st century

That’s why the craze for handling, understanding and effectively analyzing the data is increasing like a summer temperature nowadays. And hence the craze behind the two terms of our discussion Data Analytics and Data Analysis and they have become one of the notable hot topics in the tech world in this twenty-first century.

Analytics vs Analysis

Understanding the insights hidden behind the datasets, the analysis and analytics patterns play a major role in fetching and showcasing a lot more about the data, they attain various transformations and cross numerous stages to produce valuable output.

 

Tools and Process involved

The journey of Data Analytics consists of various stages including identifying the problem, finding the Data, Data Filtering, Data Validation, Data Cleaning, Data Visualization, Data Analysis, Inference, Prediction, etc. The most common tools employed in Data Analytics are R, Python, SAS, SPARK, Google Analytics, Excel, etc.

Similarly, the journey of Data Analysis comprises Data gathering, Data validation, Interpretation, Analysis, Results, etc., shortly it tries to find what the data is trying to express. The most common tools employed in Data Analysis are Tableau, Excel, SPARK, Google Fusion tables, Node XL, etc.

Tools and Process involved

Analytics is commonly used in many distinct ways to find some strange patterns like finding the preferences, compute various correlations, trend forecastings, etc. The most common real-life findings found through analytics are market trend forecastings, customer preferences, and effective business decisions.

With the help of Analysis, it is quite simple and easy to explore more valuable insights from the available data by performing the various types of Data Analysis such as Exploratory Data Analysis, Predictive Analysis, and Inferential Analysis, etc. They play a major role by providing more insights in understanding the data.

 

Which is beneficial?

In general, the outputs from the Data Analysis are the affordable equipment for a user to understand the actual reality behind the Data and also easy to produce better pictorial and graphical representations in the presentation to make even an illiterate understand the information hide behind the dataset much better and quicker.

But it is quite struggling for a common person to understand the analysis and process made by the Analytics person to produce predictions and the inference. Because the post-process like creating something new from the dataset for producing a better and expected output may be difficult for a third person to understand without similar background.

Examples for better understanding

Let’s try to understand the concepts with the following real-life examples,

Which is beneficial?

Example 1:

Almost every one of us has at least some little knowledge about the Share Market. Just think if you are a beginner and you want to start your trade with some profit there. Now say what you will do initially?

  • Most probably before starting trading you just try to examine the past trend records of the shares in the share market to understand what happened so far in order to frame your strategies to get more profit right? This kind of process is an example of Data Analysis.

  • After understanding the trend of the shares, now you may use different techniques to predict the future price trend of the shares, and based on that you buy some shares right? This is an example process of Data Analytics.

I hope you should gain some more extra knowledge about the difference between Data Analytics and Data Analytics. I believe I’ve given some useful insights for you to enrich your tech desire.

I request you to share your valuable thoughts about this article. It will be more useful for me during my future works.

About the author:

I’m Shankar DK, a Data Science student. Connect with me on Linkedin https://www.linkedin.com/in/shankar-d-k-03470b1a2

The media shown in this article are not owned by Analytics Vidhya and is used at the Author’s discretion. 

S
Shankar

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Responses From Readers

Clear

Eben Ezer
Eben Ezer

Wonderful Article. Concise explanation of the difference. Though I would have loved it more if you used an example that would relate with an illiterate farming. Maybe an example in farming or trading in the market.

123

Shanmukha
Shanmukha

This was really helpful, very nice content. Thank you.

123

Ahsan fraz
Ahsan fraz

Big gap which not difficult to grasp such idea of what the work load, Technical expert about writing complex scripts and examine the data in typical technical fashion which is sure basis of Big O notation on oyher hand Analysis has domain or business expert right now Professionals which not necessary an expert of computer science easily manipulate with data for their forcaster of their business need.

123

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