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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?
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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

Who is a Business Intelligence Analyst and How to Become One?

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

Introduction

Suppose you are a representative of a particular company that’s lead to the board of directors making important decisions about the corporation. The main role in decision-making reflected in this choice is assigned to Business Intelligence Analyst who provides relevant information to be used in decision-making. This role is very crucial in the ability to convert, for lack of a better word, raw data into intelligence gain that propels growth and efficiency.

If you have ever felt the desire to know how to be that one, who can transform the business constructs into the tools for success, then this article is for you. The following article provides information about what exactly Business Intelligence Analyst is, or what they do, as well as the guidelines that will help you to become such a professional.

Who is a Business Intelligence Analyst and How to Become One?

Learning Outcomes

  • Know the duties of Business Intelligence Analyst.
  • Discover more on the education required to be a BI Analyst.
  • Explore more information relating to the usual route of progression and promotional avenues within the BI occupation.
  • Get to know more about the BI Analysts tools and technologies.
  • Check how the role of BI Analysts is specified and what education and training one needs in order to become one.

Table of contents

Who is a Business Intelligence Analyst?

A Business Intelligence Analyst is an expert who makes use of business intelligence methodologies to assist in decision making in an organization. They capture and sort the big data then they try to look for some patterns, trends and anomalies. Using these opportunities, BI Analysts contribute to the effective management of business processes, increase performance, and achieve key objectives.

What Does a Business Intelligence Analyst Do?

With role of business intelligence analyst comes lot of responsibilities. Let us look into those one by one.

  • Data Collection and Analysis: BI Analysts collect data from different sources, then preprocess it and analyze for efficient information delivery. They have an analytical bent of mind that enables them to use statistical tools and methodologies to analyze data and make useful recommendations.
  • Reporting: Sitting down with the higher command and presenting the results in the written form, as well as in the form of diagrams and charts. These include the uses of Tableau, Power BI, and excel for purposes of developing dashboards and presentations.
  • Performance Metrics: Setting up goals and objectives of a business, and then consistently monitoring the business outcomes in an effort to determine which among the proposed goals and objectives are relevant to the business. This assists in revealing how well an organization is performing in relation to the set objectives.
  • Data Modeling: Setting up and defining the various data models required for business reporting requirements. BI Analysts also have the responsibility of arranging data to fit a layout that makes analysis easier and more efficient.
  • Collaboration: Co-ordination with various different departments in order to identify their requirements and what kind of reports and analyses are required from the available data. Another general responsibility of BI Analysts is that they are in-between IT and business departments.
  • Forecasting and Trend Analysis: Obliviously, this is the basis of most statistical models that use historical data to forecasts future trends. This assists various companies especially in the process of strategizing for the future.
  • Data Governance: Hard Work data accuracy, consistency and security. Hiring BI Analysts provides the organization with a sense of professional responsibility when it comes to the handling of the data.
  • Training and Support: Supplying information about BI to other members of the team besides teaching them the necessary tools and techniques.

What is the Salary of Business Intelligence Analyst?

Therefore, the average pay that has been set for the Business Intelligence Analyst varies with region, experience level, and industry. On average:

  • Entry-Level: $55 000 – $75 000 yearly.
  • Mid-Level: $75,000-$95,000 per Annum
  • Senior-Level: $95,000 to $120,000+ for each year

It should be mentioned that earnings can go significantly higher in some industries, such technology and finance, as well as in places with high cost of living.

How to Become a Business Intelligence Analyst?

To become a BI Analyst one has to have a solid education background, technical skills, and experience. BI Analyst is an important role that enables organizations to leverage on data analysis to make sound business decisions thus the need for business acumen and communication skills.

Obtain a Relevant Degree

  • Bachelor’s Degree: A Bachelor’s degree is usually needed in Computer Science or Information Technology, Mathematics and Statistics, Business Administration or other related fields. These are information management, statutory, statistics, and business classes and courses.
  • Advanced Degrees and Certifications: Although not mandatory, having a master’s degree or certification in data science, business analytics or similar fields will be helpful. Certifications like Microsoft Certified: A Data Analyst Associate, CBIP – Certified Business Intelligence Professional, and Tableau Certification are useful.

Develop Technical Skills

  • Learn SQL: It is necessary to know the basic knowledge of SQL in order to manage and inquire about databases. Everyone who wants to become a BI Analyst, knows that working with SQL is a necessity at the moment.
  • Master Data Visualization Tools: The report Wizard, Report Builder, and Chrystal reports are other tools used in generating reports; however, tools such as Tableau, Power BI, and QlikView are other essential tools in generating interactive and appealing reports.
  • Understand BI Software: It is crucial to know such BI software as SAP BusinessObjects and Microsoft Business Intelligence.
  • Forecasting and Trend Analysis: For forecasting and trend analysis, tools like R, Python (with NumPy, Pandas, and StatsModels), and Tableau are highly effective. For data modeling, consider using ER/Studio, Oracle SQL Developer Data Modeler, and Microsoft Visio.

Gain Practical Experience

  • Internships: Intern in areas of data analysis or as entry-level in business intelligence departments. It can also be said that it is extremely useful to try all this in action.
  • Projects: To incorporate the skills you have learned into practice you could do projects based on real life or actual cases.

Pursue Certifications

While not always necessary, certifications can enhance your credentials and improve job prospects. Consider certifications such as:

  • Microsoft Certified: Data Analyst Associate
  • Certified Business Intelligence Professional (CBIP)
  • Tableau Certification

Build Analytical Skills

  • Enhance Statistical Knowledge: Deepen your understanding of statistical methods and data modeling.
  • Improve Problem-Solving Abilities: Develop the ability to think critically and solve complex problems using data-driven approaches.

Develop Soft Skills

  • Communication Skills: The ability to explain data analysis findings to executives who lack technical understanding of the topic is a must for BI analysts.
  • Business Acumen: Business operations and how data fits into them and how it is used to achieve organizational goals.

Network and Continue Learning

  • Join Professional Communities: Having a connection with the professional networks and communities to update the prospects of the career.
  • Continuous Education: Make it a practice to attend new and updated BI information from various workshops, webinars, and advanced courses.

Career Path and Advancement

The career path of a BI Analyst can vary, but generally follows a progression through several stages:

  • Entry-Level Positions: beginning by being in the position of a data analyst or junior business intelligence analyst. These roles are more involved with mastering the elementary skills of data analysis and business intelligence.
  • Mid-Level Positions: So, proceeding to the positions like BI Analyst, Data Scientist, or BI Developer. At this stage, professionals receive promotions and take on more tasks and challenging assignments.
  • Senior-Level Positions: Things like Senior BI Analyst, BI Manager and Director of Business Intelligence or positions like that. Senior positions emphasize strategic management, focusing on planning, leading, and making significant decisions.

Advancement Opportunities

BI Analysts can advance their careers by:

  • Gaining Experience: Accumulating hands-on experience with data analysis and BI tools. This helps in building expertise and credibility.
  • Continuing Education: Pursuing advanced degrees or professional certifications. Staying updated with the latest trends and technologies in business intelligence.
  • Networking: Establishing a professional network via internet forums and industry events. Networking can lead to new opportunities and insightful information.
  • Specializing: Focusing on specific industries or types of analysis to become an expert. Specialization can make a BI Analyst more valuable to employers.

Tools and Technologies used by Business Intelligence Analyst

BI Analysts use a variety of tools and technologies to perform their duties, including:

  • Database Management Systems: SQL, Oracle, MySQL. These are essential for managing and querying data.
  • Data Visualization Tools: Tableau, Power BI, QlikView. These tools help in creating interactive and visually appealing reports.
  • BI Software: SAP BusinessObjects, Microsoft BI. BI software platforms provide comprehensive solutions for data analysis and reporting.
  • Programming Languages: Python, R. These languages are used for more advanced data analysis and automation.
  • Spreadsheet Software: Microsoft Excel, Google Sheets. Spreadsheets are widely used for data manipulation and basic analysis.

Also Read: Top 9 Business Intelligence Tools to Use in 2024

Conclusion

Attaining the status of a Business Intelligence Analyst entails the giving of technical skills, the mastery of business knowledge, and passion in the area of data analysis. BI Analyst is a wonderful chance to take the primary information and turn it into business benefits that can help the organization evolve. However, education is necessary if it is done rightly and one can acquire the necessary social skills, hands on experience, and update himself with recent technological advancement in the field he or she is into then this field is rewarding. Regardless of the level at which one is and career path that they are in, the career of a BI Analyst promising future that holds a lot of variety for the holder and can be of great value.

Also Read: Top 9 Business Intelligence Tools to Use in 2024

Frequently Asked Questions

Q1. What does a Business Intelligence Analyst do?

A. A Business Intelligence Analyst collects, processes, and analyzes data to provide insights and support business decision-making.

Q2. What skills are required to become a BI Analyst?

A. Key skills include analytical skills, technical proficiency in SQL and data visualization tools, communication skills, business acumen, and problem-solving abilities.

Q3. What education is needed for a BI Analyst role?

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

Q4. How can I advance in a BI career?

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

Q5. What tools do BI Analysts use?

A. BI Analysts use tools like SQL, Tableau, Power BI, SAP BusinessObjects, Python, R, and spreadsheet software such as Microsoft Excel.

Q6. What is the salary range for a BI Analyst?

A. Entry-level BI Analysts can earn between $55,000 and $75,000 annually, mid-level roles range from $75,000 to $95,000, and senior positions can exceed $120,000 annually.

Business IntelligenceBusiness Intelligence Analystcareer
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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