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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

What is Business Analytics and which tools are used for analysis?

Kunal Jain
Kunal Jain Last Updated : 27 Aug, 2021
5 min read

Business Analytics has become a catch all word for any thing to do with data.

So if you are new to this field and don’t understand what people refer to as “Business Analytics”, don’t worry!

Even after spending more than 6 years in this industry, there are times when it is difficult for me to understand the work a person has done by reading his CV.

Here is how an excerpt from a typical JD might look like:

  • Analyze, prepare reports and present to Leadership team on a defined frequency

or

  • Lead multiple analytical projects and business planning to assist Leadership team deliver business performance.

On one hand, this creates confusion in mind of person applying for a particular role. On the other hand, it leaves the selectors with a difficult role to understand and judge what a person has done in past.

Now, if I got this as description for one of the jobs I had applied to, I would be scared! Scared, not because I don’t know the subject, but because, these could mean anything. The work could refer to preparing basic reports at a junior level to performing multi-variate deep dives on various subjects.

So, what do you do when you are in such a situation?

Well, the first thing you should do is understand Business Analytics spectrum. Once you have understood it, ask which part of the spectrum, the role applies to and then decide whether it suits your skills or not.

Following is a good representation of this spectrum:

analytics spectrum

Let me explain each of these areas in a bit more detail.

[stextbox id=”section”]Reporting – Answer to What happened?[/stextbox]

The domain of Analytics starts from answering a simple question – What happened? This activity is typically known as reporting. These are typically the MIS which people want to receive first thing in the morning. It is a snapshot of what has happened. Following is an example of how a typical report might look like:

excel_MIS

Tools used in reporting:

Majority of elementary reporting happens on MS Excel across the globe. More evolved Organizations might pull the data through databases using tools like SQL, MS Access or Oracle. But typically, the dissemination of reports happens through Excel.

Skills required for reporting:

  • MS excel
  • Business understanding
  • Ability to perform monotonous task with diligence

[stextbox id=”section”]Detective Analysis – Answer to Why did it happen?[/stextbox]

Detective Analysis starts where reporting ends. You start looking for reasons for unexpected changes. Typical  problems you work on are “Why did the Sales drop in last 2 months?” or “Why did the latest campaign under-perform or over-perform?”. In order to find out answers to these questions, you look at past trends or you look at distribution changes to find out the reasons for the changes. However, all of this is backward looking.

Some of these insights, which you find out after looking at backward analysis can be used for business planning, but the purpose of analysis is typically to find out what has worked and what has not.

Tools used in detective analysis:

Typically used tools are MS excel, MS Access, Minitab, R (basic regression). You tend to use advanced Excel and Pivot tables while dealing with these problems and typically creating time series graphs helps a lot.

Skills required for detective analysis:

  • Structured thinking
  • MS Access, Excel, basic regression
  • Business understanding

[stextbox id=”section”]Dashboards – Answer to What’s happening now?[/stextbox]

Dashboard is an Organized and well presented summary of key business metrics. They are usually interactive so that the user can find out the exact information he is looking for. Dashboard, in ideal state should provide real time information about performance. Following is an example of how a dashboard might look like:

dashboard

The whole science of creating data model, dashboards and reports based on this data is also known as “Business Intelligence“.

Tools used for creating dashboards:

For limited size of data, dashboards can be made using Advanced excel. But, typically Organizations use more advanced tools for creation and dissemination of tools. Business Objects, Qlikview, Hyperion are names of some such softwares.

Skills required for creating dashboards:

  • Strong structured thinking: The person will need to create the entire architecture and data model
  • Business Understanding: If you don’t understand what you want to represent, God help you!

 [stextbox id=”section”]Predictive Modeling – Answer to What is likely to happen?[/stextbox]

This is where you take all your historical trends and information and apply it to predict the future. You try and predict customer behaviour based on past information. Please note that there is a fine difference in forecasting and predictive modeling. Forecasting is typically done at aggregate level, where as predictive modeling is typically done at a customer / instance level

Tools used for Predictive modeling:

SAS has the highest market share among tools used for predictive modeling followed by SPSS, R, Matlab.

Skills required for Predictive modeling:

  • Strong structured thinking
  • Business Understanding
  • Problem Solving

[stextbox id=”section”]Big data – Answer to What can happen, given the behaviour of the community?[/stextbox]

Imagine applying predictive modeling with a microscope in hand. What if you can store, analyze and make sense out of every information about the customer. What kind of social media community he is attached to? What kind of searches is he performing? Big data problems arise when data has grown on all three Vs (Volume, Velocity and Variety). You need data scientists to mine this data.

Tools used in Big data:

This is a very dynamic domain right now. A tool which used to be market leader 6 months back is no longer the best. Hence, it is difficult to pin down specific tools. These tools typically work on Hadoop to store the data.

Skills required for harnessing big data:

  • Strong structured thinking
  • Advanced Data Architecture knowledge
  • Ability to work with unstructured data

So, now that you understand the Analytics spectrum, if you come across a role which is not clear to you, please spend the necessary time understanding which domain does it refer to and does it fit right with what you want to achieve.

If you have come across this confusion on understanding “Business Analytics“, this article should have helped you. In case there are any further confusion, do let me know.

If you like what you just read & want to continue your analytics learning, subscribe to our emails or like our facebook page

Kunal Jain
Kunal Jain

Kunal Jain is the Founder and CEO of Analytics Vidhya, one of the world's leading communities of Al professionals. With over 17 years of experience in the field, Kunal has been instrumental in shaping the global Al landscape. His expertise spans diverse markets, from developed economies like the UK to emerging ones like India, where he has successfully led and delivered complex data-driven solutions. As a recognized thought leader, Kunal has empowered countless individuals to realize their Al ambitions through his visionary approach to Al education and community building. Before founding Analytics Vidhya, Kunal earned both his undergraduate and postgraduate degrees from IIT Bombay and held key roles at Capital One and Aviva Life Insurance across multiple geographies. His passion lies at the intersection of analytics, Al, and fostering a thriving community of data science professionals.

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Responses From Readers

Clear

Jay
Jay

Hi Kunal, This is a great article to understand the different domains of analytics. Thanks ! Jay

123

Ruchir
Ruchir

Hi Kunal, I had come across your website through a blog page from Jigsaw academy and I must say that your articles are extremely insightful and easy too read. Please continue to share such knowledge for us aspiring analysts. Many thanks again.

123

BUSINESS ANALYSIS TOOLS
BUSINESS ANALYSIS TOOLS

this was great article and really helpfull tome i was looking for more such article….. thanks for sharing………..

123

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