Business analyst with agentic AI

Blitz Academy, Kerala’s premier software training institute, offers a 6-month Business Analytics program with a structured weekly plan, delivered by the Blitz Academy Data Science Department. The course takes learners from business problem framing to data-driven recommendations, combining concepts, hands-on labs and a weekly assignment every week.

Duration: 24 weeks (6 months)
Level: UG/PG (B.Tech, MCA, M.Sc CS)
Mode: Concepts + hands-on labs + weekly assignment
Tools: Excel, SQL (MySQL/PostgreSQL), Python (Pandas, NumPy, Matplotlib, Scikit-learn, Statsmodels), Power BI / Tableau

Outcomes: By the end of the course, learners can frame business problems, clean and analyse data, build dashboards, apply statistical and predictive models, and present data-driven recommendations through a capstone project.

Assessment Scheme (Suggested):
Weekly labs and assignments: 20%
Monthly tests (6): 20%
Mid-course practical (SQL + Python + dashboard): 20%
Capstone project and presentation: 30%
Attendance and participation: 10%

Prerequisites: Basic computer literacy and school-level mathematics. Prior programming is helpful but not required.

The program runs at Blitz Academy centres in Kochi, Calicut and Bangalore. Enroll now in Blitz Academy’s Business Analytics program and build a career in analytics.

Month 1 - Foundations of business analytics & Excel
Week 1 - Introduction to Business Analytics
• Descriptive, diagnostic, predictive and prescriptive analytics
• Analytics lifecycle, roles (analyst, scientist, engineer), industry use cases
• Lab: Exploring a sample business dataset; setting up tools
Week 2 - Business Problem Framing & KPIs
• Defining objectives, hypotheses and success metrics
• KPIs by domain: sales, marketing, finance, HR, operations
• Lab: Writing a problem statement and KPI tree for a case study
Week 3 - Excel for Analysis
• Formulas, lookup functions (VLOOKUP/XLOOKUP, INDEX-MATCH), logical and text functions
• Data cleaning, sorting, filtering, data validation
• Lab: Cleaning a messy sales dataset
Week 4 - Excel Pivot Tables & Dashboards
• Pivot tables, pivot charts, slicers, conditional formatting
• What-if analysis, Goal Seek, Solver basics
• Assignment: Build an interactive Excel sales dashboard
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Month 2 - SQL & data management
Week 5 - Database Fundamentals & SQL Basics
• Relational concepts, keys, schemas, ER diagrams
• SELECT, WHERE, ORDER BY, LIMIT, aggregate functions
• Lab: Querying a retail database
Week 6 - Joins, Grouping & Subqueries
• INNER/LEFT/RIGHT/FULL joins, GROUP BY, HAVING
• Subqueries and CTEs
• Lab: Customer and order analysis
Week 7 - Advanced SQL for Analytics
• Window functions (RANK, ROW_NUMBER, LAG/LEAD, running totals)
• Date functions, CASE statements, views
• Lab: Cohort and retention queries
Week 8 - Data Warehousing & ETL Concepts
• OLTP vs OLAP, star and snowflake schema, data marts
• ETL/ELT pipelines, data quality and governance
• Assignment: SQL case study with a business report
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Month 3 - Statistics & Python for analytics
Week 9 - Descriptive Statistics & Probability
• Mean, median, variance, skewness, distributions
• Probability basics, normal distribution, sampling
• Lab: Statistical summary in Excel and Python
Week 10 - Python for Data Analysis
• Python essentials, NumPy, Pandas DataFrames
• Importing data, handling missing values and duplicates, feature creation
• Lab: Data wrangling on a real dataset
Week 11 - Exploratory Data Analysis & Visualization
• Univariate, bivariate and multivariate analysis
• Matplotlib and Seaborn; outlier detection; correlation
• Lab: EDA report on a customer dataset
Week 12 - Inferential Statistics
• Confidence intervals, hypothesis testing (t-test, chi-square, ANOVA)
• A/B testing for business decisions
• Assignment: A/B test analysis for a marketing campaign
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Month 4 - Data visualization & business intelligence
Week 13 - Principles of Data Visualization
• Choosing the right chart, colour, layout, avoiding misleading visuals
• Data storytelling framework
• Lab: Redesigning poor charts
Week 14 - Power BI / Tableau: Data Modelling
• Connecting sources, Power Query transformations, relationships
• Calculated columns and measures (DAX basics / Tableau calculations)
• Lab: Building a data model from multiple tables
Week 15 - Interactive Dashboards
• Filters, slicers, drill-through, tooltips, parameters
• Publishing and sharing; performance tips
• Lab: Executive sales and profit dashboard
Week 16 - Domain Dashboards & Storytelling
• Marketing, finance, HR and supply chain dashboards
• Presenting insights to stakeholders
• Assignment: Domain dashboard with a short insights presentation
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Month 5 - Predictive & prescriptive analytics
Week 17 - Regression Analysis
• Simple and multiple linear regression, assumptions, diagnostics
• Model evaluation: R-squared, RMSE, MAE
• Lab: Sales and price prediction
Week 18 - Classification Models
• Logistic regression, decision trees, random forest
• Confusion matrix, precision, recall, ROC-AUC
• Lab: Customer churn prediction
Week 19 - Clustering & Customer Analytics
• K-Means, hierarchical clustering, RFM analysis
• Customer segmentation, CLV, market basket analysis
• Lab: Customer segmentation project
Week 20 - Time Series & Forecasting
• Trend, seasonality, moving averages, exponential smoothing, ARIMA
• Demand and sales forecasting
• Assignment: Forecast next-quarter sales and compare models
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Month 6 - Applied analytics, capstone & career readiness
Week 21 - Prescriptive Analytics & Decision Making
• Optimization, linear programming, scenario and sensitivity analysis
• Pricing, inventory and resource allocation cases
• Lab: Solver / Python optimization case
Week 22 - Business Analytics in Practice
• Marketing, web and product analytics; financial and HR analytics
• Introduction to text/sentiment analysis; ethics, privacy and responsible use of data
• Lab: Web and campaign analytics case
Week 23 - Capstone Project Development
• Problem definition, data collection, analysis, modelling, dashboard
• Mentor review and iteration
• Deliverables: Project report, dashboard, code/queries
Week 24 - Capstone Presentation & Career Preparation
• Final presentations to panel; viva
• Resume, portfolio and GitHub; interview questions (SQL, statistics, case studies)
• Mock interviews

• IT
• Business Intelligence
• Marketing Analytics
• Research and Development (R&D)
• Finance
• Healthcare
• Operations
• Customer Service
• Engineering
• Consulting
• Logistics
• Marketing