Interview Prep
Career
GenAI
Prompt Engg
ChatGPT
LLM
Langchain
RAG
AI Agents
Machine Learning
Deep Learning
GenAI Tools
LLMOps
Python
NLP
SQL
AIML Projects

Reading list

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

Business Analytics vs. Data Science – Which One to Choose?

Ram Dewani
Ram Dewani Last Updated : 23 May, 2023
8 min read

Organizations rely on valuable insights to drive their success in today’s data-driven world. Business Analytics and Data Science are two key disciplines at the forefront of this data revolution. Similarly, professionals in the roles of Business Analyst vs Data Scientist play crucial roles in extracting meaningful insights from data. But what sets them apart? In this article, we delve into the fascinating realms of Business Analytics vs Data Science and explore the distinctive responsibilities and skills of Business Analysts and Data Scientists. Join us as we unravel the power of data and uncover the nuances between these disciplines, shedding light on their respective contributions in leveraging data to make informed business decisions.

Table of contents

What is Business Analysis?

Business Analytics is the practice of using data analysis and statistical methods to derive meaningful insights and make data-driven decisions that optimize business performance. It involves collecting, organizing, and analyzing data from various sources to identify trends, patterns, and correlations. Business analytics focuses on solving specific business problems, improving efficiency, identifying opportunities, and guiding strategic planning by providing actionable insights and recommendations.

To start your business analytics journey, register for our exclusive course on CBAP!

What is Data Science?

Data Science is a multidisciplinary field that combines statistical analysis, machine learning, data visualization, and computer science to extract knowledge and insights from structured and unstructured data. Data scientists employ a combination of data exploration, data preprocessing, predictive modeling, and data visualization techniques to uncover patterns, make predictions, and gain valuable insights that drive decision-making and solve complex problems across various industries. Data science encompasses the entire data lifecycle, from data collection and cleaning to analysis and interpretation.

Business Analytics vs Data Science – Skills and Tools Covered

The skills and tools required to make a career in Business Analytics and Data Science differ to a great extent. Here is the list of the same for both profiles:

Business Analytics

Business Analytics professionals must be proficient in presenting business simulations and business planning. A large part of their role would be to analyze business trends. For eg, web analytics/pricing analytics.

Some of the tools used extensively in business analytics are Excel, Tableau, SQL, Python. The most commonly used techniques are – Statistical Methods, Forecasting, Predictive Modeling and storytelling.

Data Science

A data scientist must be proficient in Linear algebra, programming, computer science fundamentals. Some examples of data science projects vary from building recommendation engines to personalized E-mails.

The common tools of a data scientist are R, Python, scikit-learn, Keras, PyTorch and the most widely used techniques are Statistics, Machine Learning, Deep Learning, NLP, CV.

And for both the roles, structure thinking, and problem formulation is a key skill to do well in their respective domain.

Data Science vs Business Analytics

Data ScienceBusiness Analytics
Data Science involves advanced statistical analysis, machine learning, and predictive modeling techniques to extract insights and solve complex problems.Business Analytics focuses on using data analysis and statistical methods to make data-driven decisions that optimize business performance.
Data Science utilizes programming languages like Python or R, as well as big data technologies, to handle and process both structured and unstructured data from various sources.Business Analytics relies on tools like Excel, SQL, and data visualization software to work primarily with structured data from internal systems and databases.
Strong programming, mathematical, and statistical skills are required in Data Science.Business Analytics requires strong analytical skills, statistical knowledge, and business acumen for effective decision-making.
Data Science can involve data engineering and big data processing tasks.Business Analytics primarily focuses on data analysis and interpretation rather than data engineering.
Data Science finds applications in industries such as finance, healthcare, retail, and technology.Business Analytics is applied in various industries, including finance, marketing, operations, and more.
Data Science roles typically command higher salaries due to the advanced technical expertise required.Salaries in Business Analytics may vary based on industry, location, and experience, but they are generally lower compared to Data Science roles.

Business Analyst vs. Data Scientist – Role & Skills

Let us take an example of an exciting electrical vehicle startup. This startup is now big for creating job families. And, they have decided to create three job families, one is a scientist, and the other two are an engineer and a management professional. Now I want you to take time and imagine what kind of role they play in the company.

We can infer their role from the general level of understanding:

  1. Scientist – Work on complex, distinct problems such as finding a solution to build an efficient battery, or how to improve the design of the vehicle. While these problems may not give direct gain to the company but are crucial for advanced developments. And, in the future, these developments can help startups have non-linear (exponential) growth.
  2. Engineer – Take these developments and apply industry techniques to transform them into production. For example, making an assembly line to manufacture these vehicles using the right machinery.
  3. Management – Run the business and solve business-related problems on a day-to-day basis. For example, to find the right market to open a store for the vehicle. Decisions regarding the sales and marketing of these products and many others.

Now, let’s take these roles and convert it to data-based profiles.

Business Analyst vs Data Scientist

Business AnalystData Scientist
Focuses on understanding business needs and requirements and translating them into actionable insights and recommendations.Focuses on extracting insights and knowledge from data to solve complex problems and drive decision-making.
Works closely with stakeholders to gather requirements, define metrics, and identify business opportunities.Collaborates with stakeholders to understand their data needs and formulate data-driven strategies.
Utilizes business analysis techniques such as requirements gathering, process mapping, and data visualization to support decision-making.Utilizes advanced statistical analysis, machine learning, and predictive modeling techniques to uncover patterns and make predictions.
Develops reports, dashboards, and presentations to communicate insights and recommendations to stakeholders.Develops and deploys sophisticated models and algorithms to analyze large volumes of data and derive actionable insights.
Proficient in tools like Excel, SQL, and data visualization software to analyze and present data effectively.Proficient in programming languages like Python or R, as well as big data technologies, for data manipulation and analysis.
Possesses strong business acumen, domain knowledge, and communication skills to bridge the gap between technical analysis and business objectives.Possesses strong analytical, mathematical, and statistical skills to tackle complex data problems and generate insights.
Works across various business domains, such as finance, marketing, operations, and supply chain management.Works across industries like finance, healthcare, technology, and retail, applying data science techniques to solve domain-specific challenges.
Collaborates with technical teams, such as data engineers and developers, to ensure data availability and quality.Collaborates with cross-functional teams, including domain experts, data engineers, and business leaders, to understand data requirements and align with organizational goals.
Salary ranges vary based on industry, experience, and location, with mid-level business analysts earning around $70,000 to $90,000 per year.Data scientists typically command higher salaries, with mid-level professionals earning around $90,000 to $120,000 per year, depending on factors like experience and expertise.

Data Scientist vs Data Engineer vs Business Analyst

Business Analytics Vs Data Science
  1. Data Scientist –  He Works on complex and specific problems to bring non-linear growth to the company. For example, making a credit risk solution for the banking industry or use images of vehicles & assess the damage for an insurance company automatically.
  2. Data Engineer – He would Implement the outcomes derived by the data scientist in production by using industry best practices. For example, Deploying the machine learning model built for credit risk modeling on  banking software.
  3. Business Analyst – Run the business and take decisions on a day-to-day basis. He’ll be communicating with the IT side and the business side simultaneously.

This is a very basic analogy that you need to keep in mind to differentiate the role of Data Scientist, Business Analyst, and Data Engineer.

Caution: These terms are losely used in the industry. The exact role can depend on the maturity of your organization in data initiatives.

Now that we have our basic analogy clear, let us see the kinds of problem solved by data scientists and business analysts.

Business Analyst vs Data Scientist – Job Role

To understand the difference between a business analyst and a data scientist, it is imperative to understand the problems or projects they work on. Let us take up an interesting example. Imagine that you are a manager of a bank and you decide to implement two important projects. You have a team of a data scientist and a business analyst. How will you do the project mapping job? Below are two problem statements:

  1. Build a business plan to decide how many employees a bank needs to do XXX business in 2021
  2. Build a model to predict which transaction is Fraudulent

Take your time to understand the problems. What do you think, which problem is best suited for which profile?

The first problem statement requires making several business assumptions and incorporating macro changes into the strategy. This will require more business expertise and decision making, this will be the job of a business analyst.

The second problem statement requires processing vast behavioral data from customers and understanding hidden patterns. For this, the professional should have a very good understanding of problem formulation and algorithms. A data scientist will be a suitable person to tackle this kind of specific and complex problem.

Which Career Path to Choose – Data Science or Business Analytics?

A Data scientist’s strengths lie in coding, mathematics, and research abilities and require continuous learning along the career journey whereas a business analyst needs to be more of a strategic thinker and have a strong ability in project management.

Business Analyst tends to take business roles, strategic roles, and entrepreneurship roles as they progress through career while we notice that data scientist are more of tech entrepreneur roles as they have a strong technical background.

You can refer to the following career path to see a more in-depth route from the start of data science and business analytics journey:

Business Analytics Vs Data Science

Final Verdict

As we conclude our exploration of Business Analytics vs. Data Science and the roles of Business Analysts vs. Data Scientists, one thing becomes clear: the world of data holds immense potential for driving business growth and innovation. Whether you choose the strategic application of data in business operations or the advanced data analysis and modeling techniques, there is a path for you to excel.

To further enhance your skills and gain a competitive edge in Business Analytics, consider enrolling in our Certified Business Analytics Professional (CBAP) program. This comprehensive program equips you with the necessary knowledge and practical expertise to become a proficient Business Analyst adept at translating data insights into actionable strategies.

For those aspiring to master the intricacies of Data Science and take on the role of a Data Scientist, our Blackbelt program offers an immersive learning experience. With a focus on advanced statistical analysis, machine learning algorithms, and real-world projects, this program helps you become a seasoned data expert.

Remember, the power of data lies in your hands. Choose your path and embark on an exciting adventure with endless possibilities.

Frequently Asked Questions

Q1. Which is better business analytics or data science?

A. The choice between business analytics and data science depends on your interests, career goals, and the specific job market you are targeting. Here are some insights to consider:

Business Analytics: Business analytics focuses on analyzing data to gain insights and make data-driven decisions to optimize business operations, improve efficiency, and drive strategic planning.
Data Science: Data science involves extracting actionable insights and building predictive models using advanced statistical analysis and machine learning techniques to solve complex problems and support decision-making.

Q2. Which pays more data science or business analytics?

A. Salaries can vary based on factors such as location, industry, experience, and skill set. In general, data science roles tend to command higher salaries compared to business analytics positions due to the higher demand for advanced technical skills and expertise.

Q3. Does business analytics involve coding?

A. Business analytics may involve coding, but the extent of coding required can vary depending on the specific role and organization. Basic coding skills, such as working with data manipulation and visualization in tools like Python or R, are often beneficial for business analysts.

Q4. Is MBA or business analytics better?

A. The choice between an MBA and business analytics depends on your career goals. If you aim to develop a comprehensive business skill set and pursue leadership roles across various industries, an MBA may be a better fit. However, if you have a specific interest in data analysis and want to focus on leveraging data for decision-making, business analytics can offer more specialized training in that area.

Ram Dewani
Ram Dewani

Product Growth Analyst at Analytics Vidhya. I'm always curious to deep dive into data, process it, polish it so as to create value. My interest lies in the field of marketing analytics.

Free Courses

Generative AI 4.7

Generative AI - A Way of Life

Explore Generative AI for beginners: create text and images, use top AI tools, learn practical skills, and ethics.
Large Language Models 4.5

Getting Started with Large Language Models

Master Large Language Models (LLMs) with this course, offering clear guidance in NLP and model training made simple.
Prompt Engineering 4.6

Building LLM Applications using Prompt Engineering

This free course guides you on building LLM apps, mastering prompt engineering, and developing chatbots with enterprise data.
LlamaIndex 4.8

Improving Real World RAG Systems: Key Challenges & Practical Solutions

Explore practical solutions, advanced retrieval strategies, and agentic RAG systems to improve context, relevance, and accuracy in AI-driven applications.
RAG systems using LlamaIndex 4.7

Microsoft Excel: Formulas & Functions

Master MS Excel for data analysis with key formulas, functions, and LookUp tools in this comprehensive course.

Recommended Articles

Responses From Readers

Clear

Omkar
Omkar

While I see a lot of influencers and mentors who post and guide about Data Science on LinkedIn, I rarely see anyone for Business Analytics! Can you please help me with a list of such people to follow?

123

Tarul ahsan
Tarul ahsan

Thanks for the information. It was detailed and easy to understand

123

Ram Dewani
Ram Dewani

Thanks Tarul - Glad you found it useful!

123
Rajan Gupta
Rajan Gupta

Thanks for such a great article. Cleared my doubt

123

Ram Dewani
Ram Dewani

Thanks Rajan. Really glad that it helped you!

123

Write for us

Write, captivate, and earn accolades and rewards for your work
  • Reach a Global Audience
  • Get Expert Feedback
  • Build Your Brand & Audience
  • Cash In on Your Knowledge
  • Join a Thriving Community
  • Level Up Your Data Science Game
imag

We use cookies essential for this site to function well. Please click to help us improve its usefulness with additional cookies. Learn about our use of cookies in our Privacy Policy & Cookies Policy.

Show details