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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

Role of an IT Business Analyst

Ayushi Trivedi
Ayushi Trivedi Last Updated : 06 Aug, 2024
8 min read

Introduction

Picture this: An energetic IT firm is at the precipice of releasing creative and modern software product. Everyone is spirited up and ready for work but it’s equally problematic: the team is to reconcile the technical developers with the business side. This is where the role of the IT Business Analyst comes into play to guarantee that the product on the technical side is built to suit the firm’s strategic plans. Perhaps, the most crucial role in the project is the IT Business Analyst, who act as the middleman and help to turn IT concepts into terms comprehensible for the business and vice versa. Explaining the organizational context, working scope, required competencies, and the path to advance in the IT Business Analyst profession.

Role of an IT Business Analyst

Learning Outcomes

  • Identify the position of an IT Business Analyst and his area of operation.
  • Who is the position and what are the comprehensive machine job requirements and duties.
  • Determine what steps an IT Business Analyst usually takes in a company and what promotion prospects he or she has.
  • The article aims at providing information about the tools and techniques applied by the IT Business Analysts.
  • Understand what educational and training IT Business Analyst should undergo in order to venture into the career.

Table of contents

Who is an IT Business Analyst?

An IT Business Analyst is a specialist who is in a borderline position between the business structures of an organization and the IT ones. It can be stated that they are supposed to hold the knowledge of business requirements and needs and present these demands to IT in the proper form of technical specifications that reflect business goals to the maximum extent. Their major focus is process, product, and service enhancement by the application of data analysis and technology.

What Does an IT Business Analyst Do?

We will now look into the role of an IT business analyst in detail below:

Requirements Gathering and Analysis

  • Engaging Stakeholders: IT Business Analysts spend a lot of time with the stakeholders to ensure that they obtain all the necessary business specifications. To gather the required information, they perform interviews, surveys, and workshops to identify the necessities and goals of the business.
  • Analyzing Requirements: They make decisions about requirements to meet business objectives and apply expertise in ranking these requirements.

Documentation and Communication

  • Creating Documentation: Business requirements, functional specifications and process flow documentation are the activities performed by IT Business Analysts. They develop technical documents that are useful in documenting the work done by the business and technical personnel.
  • Facilitating Communication: On one hand they are responsible for translating expectations and requirements of the business-side decision makers into IT related plans, objectives, and initiatives, and on the other hand they minimize the potential for misunderstanding between business clients and IT on the course and progress of the project.

Solution Design and Implementation

  • Designing Solutions: IT Business Analysts work hand in hand with the IT teams to develop the appropriate solutions in the form of technologies. It means they guarantee the introduced solutions are as effective and as efficient as needed, and they are consistent with business strategies.
  • Overseeing Implementation: Co-ordinate the execution of I.T solutions, to make sure that the particular project remains on schedule and produces the necessary outcome.

Testing and Validation

  • Conducting Testing: In this regard, IT Business Analysts test processes and validate solutions to confirm they meet the desired functionality.
  • Troubleshooting Issues: They pinpoint defects that arise during testing to ensure the final product is of the highest quality.

Process Improvement

  • Evaluating Processes: IT Business Analysts assess the efficiency of business’ current practices and determine the opportunities for increased effectiveness. They make suggestions on how to improve on the laid down measures with a view of improving efficiency and productivity.
  • Implementing Changes: They introduce process enhancements with other stakeholders to ensure the changes integrate properly into business operations.

Skills Required for an IT Business Analyst

We will now look into the skills required for an IT Business Analyst.

Analytical Skills

The IT Business Analyst is also required to review great amounts of data and comprehend business processes, and thus quantitative reasoning skills are vital. It is the process of analyzing patterns and trends as well as deciding based on the trends. In particular, they should possess knowledge of how to perform certain sets of analyses with the help of different tools and methods that would possibly contribute to the enhancement of business effectiveness. Data analysis is also essential because it helps them realize the areas that have to be improved, the relative trends, and the possible recommendations they could give to the company.

Communication Skills

It is important for an IT Business Analyst to possess good communication skills as it is will facilitate the passing of information to technical and non technical personnel. To successfully satisfy these duties, they have to express requirements, submit results, and lead the discussions coherently and effulgently. The documentation of requirements, preparation of reports and generally the keeping of records all require one to bethurated in writing. Often, meetings, presentations, or collaboration with the team and stakeholders are critical to the project; that is why oral communication is one of the key skills required.

Technical Knowledge

To be precise, IT business analysts need sound knowledge of the IT environment, the SDLC, and data analysis tools. Because of this technical expertise they are able to decipher what the business needs and what technical solution meets that need. They should retrieve knowledge about databases, systems, and different programming languages as well as tools. This knowledge also assists them in collaborating with technical groups and assessing the practicality of proposed strategies. Also guaranteeing that the implemented systems uphold the objectives and specifications of the businesses.

Problem-Solving Skills

Analytical skills are required in observing problems and defining possible and suitable solutions. Moreover, IT Business Analysts should be capable of applying their creativity incline to solve a specific and critical issue. They have to identify some problem, analyze possible variants for its solving, and choose the best one. This also encompasses not only technical issues with an emphasis on problem solving but also facing business issues, defining efficiency and productivity, and enhancing the functions of the systems in the business field. Effective problem solving allows the analysts to be able to overcome challenges and produce successful projects.

Project Management Skills

Project management skills are vital for IT Business Analysts to manage tasks, timelines, and resources effectively. This includes incident management, where they handle unexpected issues, and change control, where they manage alterations to project scope or requirements. They need to be proficient in risk management, identifying potential issues and developing mitigation strategies. Documentation skills are important for maintaining detailed records and ensuring traceability. Effective planning and scheduling help manage project timelines and resources, ensuring timely and budget-compliant completion.

Educational and Training Requirements

  • Bachelor’s Degree: Generally, the candidates are expected to hold a bachelor’s degree either in Computer Science, Information Technology, Business Administration or any related university degree.
  • Certifications: Credentials like Certified Business Analysis Professional (CBAP), Project Management Professional (PMP), or any Agile certifications will help.
  • Experience: Internship in any organization, a fresh job position, or project can be useful for future IT Business Analysts.

Career Path and Advancement

  • Junior Business Analyst: Some of the initial positions include working under more experienced analysts and acquainting with the subject of business analysis.
  • Business Analyst: With experience, the scope of activities of IT Business Analysts increases, they work with more difficult projects and tasks.
  • Senior Business Analyst: Senior positions comprise responsibilities for implementing projects, training the newcomers, and defining strategies.
  • Business Systems Analyst: Concentrating its efforts on the linking of business solutions and IT applications.
  • Product Manager: Supervising the creation and delivery of specific goods or satisfying certain customer needs.

Tools and Techniques Used by IT Business Analysts

Let us now look into tools and techniques used by IT Business Analysts.

Requirements Management Tools

  • Jira: Jira is one of the most popular tools which enable teams to organize software development projects. Primarily used for project management, IT Business Analysts use it in an agile environment to document requirements, set priorities, oversee progress, and communicate with development teams.
  • Confluence: Confluence is a wiki and creation tool that allows several workers to work on any content collectively. IT Business Analysts document the specifications in Confluence, prepare working documents for project requirements, and ensure all participants share project-related information.
  • Trello: Trello is a form of project management software which is based on board, list, and card to manage tasks and projects. IT Business Analysts use Trello to organize top-priority actions and share information with staff members.

Data Analysis Tools

  • Excel: Microsoft Excel is effective software for manufacturing spreadsheets that in current days used in data analysis. IT Business Analysts also rely on Excel to manipulate numeric data, and run calculations and formulas; and use the pivot tables as well as charts and graphs to display the results.
  • SQL: SQL is a standard computer language for accomplishing the obligations of DBMS (Database Management System). From the above definitions of business analysts, it is evident that a part of their daily working involves using SQL to execute queries to data-bases, data extraction and analysis and data report generation as a form of decision-support.
  • Tableau/Power BI: Tableau and Power BI are business analytics software that can work on different data sets and turn them into attractive and engaging visually interactive dashboards. IT Business Analyst apply these tools in generating appealing and informative report that can help in decision making.
  • Python: Python is a programming language mostly used for analyzing data and performing automated tasks. IT Business Analysts use Python for data processing, analytics, and developing data visualizations.

Process Modeling Tools

  • Microsoft Visio: Microsoft Visio is a diagramming tool used to create flowcharts, process maps, and organizational charts. IT Business Analysts use Visio to model business processes, visualize workflows, and design system architecture diagrams that clearly communicate complex information.
  • Lucidchart: Lucidchart is an online diagramming tool that allows teams to collaborate in real-time on flowcharts, process maps, and diagrams. IT Business Analysts use Lucidchart to create and share process models, visualize system interactions, and collaborate with stakeholders on process improvement initiatives.

Communication Tools

  • Slack: Slack is a messaging platform designed for team collaboration. IT Business Analysts use Slack to communicate with team members, share project updates, and integrate with other tools to streamline project workflows and enhance team collaboration.
  • Microsoft Teams: Microsoft Teams is a collaboration platform that combines chat, video meetings, file storage, and application integration. IT Business Analysts use Teams to conduct virtual meetings, collaborate on documents, and maintain communication with project stakeholders.
  • Zoom: Zoom is a video conferencing tool that enables virtual meetings, webinars, and collaboration sessions. IT Business Analysts use Zoom to host remote meetings, present project updates, and facilitate communication with distributed teams and stakeholders.

Conclusion

An IT Business Analyst is a progressive and crucial member of any business organization in the present world with increasing technical advancement. They demand that IT solutions are not just technologically integrated, but initiative, in ways that serve business objectives. Therefore, a career in IT Business Analyst position is quite fulfilling and unchallenging provided that the candidate possesses the right skills, education and working experience.

Frequently Asked Questions

Q1. What does an IT Business Analyst do?

A. They gathers business requirements, designs IT solutions, and ensures that they meet business needs.

Q2. What skills are required to become an IT Business Analyst?

A. Key skills include analytical skills, communication skills, technical knowledge, problem-solving skills, and project management skills.

Q3. What education is needed for an IT Business Analyst role?

A. Typically, you need a bachelor’s degree in Computer Science, Information Technology, Business Administration, or a related field.

Q4. How can I advance in an IT Business Analyst career?

A. Gain experience, pursue advanced education or certifications, network with professionals, and specialize in specific areas of business analysis.

Q5. What tools do IT Business Analysts use?

A. They use tools like Jira, Confluence, Trello, Excel, SQL, Tableau, Microsoft Visio, Lucidchart, Slack, Microsoft Teams, and Zoom.

business analysisBusiness AnalystcareerIT business analyst
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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