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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

Business Analyst vs. Data Analyst: A Comprehensive Comparison

Ayushi Trivedi
Ayushi Trivedi Last Updated : 02 Aug, 2024
7 min read

Introduction

Picture yourself in a dynamic office where two professionals are working together on a major project. The business analyst is focused on understanding the company’s goals, identifying opportunities for improvement, and ensuring that the business strategy aligns with market demands. Meanwhile, the data analyst is deep into datasets, uncovering trends, and providing actionable insights based on data analysis. Both roles are crucial for the success of the project, but their approaches and responsibilities differ significantly. This article will explore the key differences between business analysts and data analysts, helping you understand their unique contributions and determine which path might be right for you.

Overview

  • Identify the tasks related with the business analyst and data analyst professions.
  • Know the competency profile needed for each post.
  • Find out the basic tools and profession approaches used by business analysts and data analysts.
  • This will help one to have an understanding of the prospects for a given career and positions existing in that line.
  • Do understand the difference in the scope of responsibilities that business analysts have in comparison to data analysts.

Table of contents

Who is a Business Analyst?

BA mostly addresses business requirements, improvements of the existing processes, and recommendations for work improvement that would be beneficial to business stakeholders. They act middlemen between the IT and the business so as to ensure that the techniques used are relevant to the exploits of the business.

Responsibilities: Capturing requirements, analyzing feasibility, mapping and recreating business processes, creating business cases, and representing stakeholders’ interest.

Who is a Data Analyst?

People working in the field of data analysis work on acquisition, manipulation, and the performance of statistical actions on big data sets. Their aim is to arrive at useful insights that can help in the running and management of business organizations.

Responsibilities: It involves data preprocessing which encompasses operation such as data cleansing, data transformation, removal of irrelevant data, data reduction and data discretization; data analysis which entails descriptive statistics, inferential statistics, regression analysis and classification analysis; data presentation through charts, graphs, tables and other graphical methods; result reporting and data conclusion and modeling where patterns are identified.

Qualifications and Requirements of Business Analyst vs Data Analyst

Let us now look into the qualifications and requirements of business analyst vs data data analyst.

Requirements for a Business Analyst Role

Normally the business analysts have relevant degrees in the field of business, finance, or economics. An undergraduate degree holder can enter the entry-level position for BA. However, for higher roles, an MBA in Business Administration is preferred with some relevant experience.

The requirements of business analysts include:

  • Expert in researching data
  • Should possess mathematical mindset and
  • Good Analytical Mindset & A Critical Thinker
  • Strong Hold in MS Excel, Access, SQL
  • Should know how to operate Power Bi, Tableau, Jira Software
  • Experience in Handling & Managing Project
  • Good communicator and should know how to communicate with the client and the programming team to meet the business requirements

Requirements for a Data Analyst Role

To become a data analyst, you must have a degree in the following subjects like Mathematics, Statistics, Engineering or Economics degree. You can enter the career path even with an undergraduate degree.

Other requirements to become a successful Data Analyst include:

  • Should be able to handle data models
  • Can analyse and extract information from huge datasets
  • Should have programming knowledge in languages like R, Python, SQL etc
  • Should be able to generate actionable reports
  • Problem-solving ability
  • Analytical mindset
  • Can understand the problem and extract important features from the data set by performing activities like EDA( Exploratory Data Analysis)
  • Proper knowledge of data mining techniques as well

Business Analyst vs. Data Analyst

Let us now explore the key differences between business analyst and data analyst in the below table:

AspectBusiness AnalystData Analyst
TermsDetermination of requirements, assessment of activities, and suggestion of actions.Uses statistics to make sense of complex data to reveal insights from large datasets.
Primary ConcernsRelationship between technological solutions and organizational initiatives.Analyzing data to reveal new knowledge for decision-making.
RolesGathering requirements, constructing process models, creating business use cases, communicating with stakeholders.Data cleaning, pre-processing, manipulation, statistical analysis, data visualization, and reporting.
SkillsAnalytical skills, business acumen, problem-solving, communication, stakeholder management.Data analysis and summarization, data transformations, and data representation techniques.
RequirementsDegree in business administration or management; CBAP or PMI-PBA certifications.Degree in statistics, mathematics, computer science; CAP certification.
ToolsMicrosoft Visio, JIRA, Trello, Jama, Tableau. ExcelSQL, Python, R, Tableau, Power BI
Business SkillsSWOT Analysis, PEST Analysis, Gap Analysis, business process mapping, use case development.Measurement using averages, proportions, variation; hypothesis generation and testing; mathematical models.
Career StagesEntry level, Experienced/Professional, Managerial, Expert/Executive, Consultant, C-level.Junior Data Analyst, Data Analyst, Senior Data Analyst, Data Scientist, Data Analytics Manager, CDO.
FocusAligning IT solutions with business objectives.Generating actionable insights from data.
Industry ApplicationsFinance, banking, healthcare, IT, marketing.Finance, healthcare, e-commerce, advertising.
ApproachStrategic and business-oriented.Data-centric and analytical.

What Exactly Do Data Analysts Do?

Data Analysts play a crucial role in transforming raw data into actionable insights. Here’s a closer look at their responsibilities:

  • Data Collection and Cleaning: Data Analysts work with data from several sources: databases and spreadsheets and others from external sources. They also clean the data to eliminate duplicates and mistakes and also verify the data’s accuracy.
  • Data Processing: It then goes through cleaning where it is processed and arranged. Hence, analysts apply methods and scripting languages such as SQL, Python, R to solve different data handling problems.
  • Statistical Analysis: Statistician looks into the relationship in the data sets and apply various techniques to highlight trends, correlations and patterns. They include Quantitative dataanalysis methods like; measures of central tendency (Mean, Median, mode) and measures of variability (Regression analysis, Hypothesis testing)
  • Data Visualization: Thus, Data Analysts translate the outcomes in the form of charts, graphs and dashboard that include tools such as Tableau, Power BI or Excel. These aids enable the recipient to understand abstract reflections of certain data swiftly.
  • Reporting: It is worthy of note that analysts prepare reports containing analysis of data, possible interpretations regarding such data, and necessary recommendations. Such reports are effective for managers as decision-makers of the business operations and plans.
  • Decision Support: Data Analysts help in decision-making by supplying information that someone might think could be useful in increasing the productivity of a certain process, another method that Data Analysts have is identifying opportunities. Their work assists firms to think and act on data.

What Exactly Do Business Analysts Do?

Business Analysts are key players in bridging the gap between business needs and IT solutions. Here’s a breakdown of their tasks:

  • Requirement Gathering: BA interacts with end users to establish goal and specifications of the project. They record these requirements rather strictly to match solutions with the goals of the business.
  • Process Analysis: They engage in the assessment of the prevailing business processes and deduce corrective measures. This might include creating workflows, analysing the inefficiencies, and suggesting changes.
  • Solution Design: According to the collected necessities, Business Analysts develop systems that fit organizational requirements. To achieve this, they leverage solution implementations that are possible in line with the required objectives through collaboration with technical teams.
  • Business Case Development: Based on the definitions above we can say that Business Analysts develop clear business cases that contain the advantages, disadvantages and implications for change associated with business solutions. They assist in decision making and; ‘locking-down’ projects as well as getting approvals.
  • Stakeholder Communication: Essentially, they mediate between the business stakeholders and technical functional specialists to align each other’s expectations and limitations.
  • Project Management Support: With the help of Business Analysts, activities like the establishment of a project scope, goals and objectives, as well as the monitoring of the progress, can be performed to guarantee timely and cost-efficient delivery of solutions.
  • Change Management: They assist in the coordination of the new systems or processes in the organization by guaranteeing proper adoption of changes and training of the stakeholders.

Can a Business Analyst Become a Data Analyst?

A business analyst can choose to learn data analysis tools like SQL, R, Python, etc to become proficient with data collection, interpretation, and management. It is important for both business analysts and data analysts to be comfortable with handling data such that they can make a career transition to a data analyst or scientist role. They need to be comfortable with programming and coding to make a switch to these roles.

Are analyst jobs hard?

In the beginning, all sorts of analytical tasks seem hard, however with regular practice and hands-on practice on analytical tools like PowerBI, SQL, Tableau, extracting, cleansing data, and preparing proper reports becomes easier.

Data Analyst vs Business Analyst: Salary Comparison

When considering a career in data or business analysis, salary expectations are an important factor. Here’s a detailed comparison of average salaries for Data Analysts and Business Analysts:

Data Analyst

A Data Analysts is a professional in analyzing large volumes of data for the purpose of coming up with conclusions to be adopted by a business organization. Their work may include use of statistics, data and information presentation, and generation of reports.

Average Salary (as of 2024):

  • Entry-Level: About $60, 000 to $75, 000 per year
  • Mid-Level: $75,000 to $90,000 per year
  • Senior-Level: Its range is between ninety thousand to one hundred and fifteen thousand US dollars per year.

Factors Affecting Salary:

  • Experience: Senior Data Analysts earn more compared to the Junior Data Analysts.
  • Location: The salaries depend on location as well, opting for higher rates in large technological centers of, for example, San Francisco, New York, or Seattle.
  • Industry: In general, practical DataAnalysts in companies that belong to the financial or high-tech business sectors can receive better pay in contrast to organizations belonging to the non-profit field.

Business Analyst

Business Analysts concentrate on identifying the requirements of an enterprise and its operations in order to create the solutions necessary to reach particular agendas and optimize the business’s functionality.

Average Salary (as of 2024):

  • Entry-Level: $65,000 – $80,000 per year
  • Mid-Level: $80,000-$100,000 a year
  • Senior-Level: Less than or more than one hundred thousand dollars to one hundred and thirty thousand U.S. dollars per annum.

Factors Affecting Salary:

  • Experience: There could also be disparities in business analyst salary based on experience; those business analysts that have academic experience as well as business experience earn large salaries than new business analysts.
  • Location: Like the data analysts’ salary, business analyst also has regional differences and more significant wages in San Francisco, Boston, and Washington, D. C.
  • Industry: The compensation of Business Analysts can be influenced by the industry which may be higher in the business sectors like finance, consulting, or the technology industry.

Conclusion

Business analysis and data analysis are some of the significant professions in the current companies, yet their findings and duties are divergent. In the first case, business analysts focus on how within a business and determine how some technical developments meet strategic business objectives, on the other hand we have data analysts who spend their time analyzing data and deriving knowledge from them. Depending on the type of person who is an analyst, whether he or she is is drawn more towards the strategic processes of a business or the computational procedures of analytical, there is a healthy amount of worthwhile work to be done and issues to face in each career path.

Frequently Asked Questions

Q1. What is the main difference between a business analyst and a data analyst?

A. The main difference lies in their focus: business analyst stays more on the business requirements and business value proposition. While data analyst fixes heavily on the data values to draw business value propositions.

Q2. What skills are essential for a business analyst?

A. Knowledge of the Business Analyst is a good system in knowledge processing and analyzing, good knowing in managing a business career, good problem solving, good communication and Business Analyst stake holder management.

Q3. What tools do data analysts commonly use?

A. Analyzing and presenting tools for data analyst are SQL, Python, R, Excel, Tableau, and power BI to mention a few.

Q4. Can a business analyst transition to a data analyst role?

A. Yes, with the right training and skills in data analysis, statistical methods, and data visualization tools, a business analyst can transition to a data analyst role.

Q5. What educational background is required for a data analyst?

A. A bachelor’s degree in statistics, mathematics, computer science, or a related field is typically required for a data analyst position.

Ayushi Trivedi
Ayushi Trivedi

My name is Ayushi Trivedi. I am a B. Tech graduate. I have 3 years of experience working as an educator and content editor. I have worked with various python libraries, like numpy, pandas, seaborn, matplotlib, scikit, imblearn, linear regression and many more. I am also an author. My first book named #turning25 has been published and is available on amazon and flipkart. Here, I am technical content editor at Analytics Vidhya. I feel proud and happy to be AVian. I have a great team to work with. I love building the bridge between the technology and the learner.

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