Business Analytics Course
At Euphoria GenX, we offer the Best 100% Placement Assistance Business Analytics Course, designed for everyone—from beginners to professionals looking to upskill. With this dedicated program, you can learn both foundational and advanced concepts in data analysis, visualization, and business decision-making. Moreover, we provide not only theoretical knowledge but also practical, real-world project experience. This hands-on approach helps you master analytics from the core and confidently apply your skills in professional scenarios. By joining our Best 100% Placement Assistance Business Analytics Course, you can grow your career by analyzing data, generating actionable insights, and driving strategic decisions for businesses of all sizes.
Our comprehensive course includes 100% placement assistance to help you secure a high-paying job at leading companies. We cover all essential areas such as Excel, SQL, Python, Statistics, Machine Learning, Power BI, Tableau, and business intelligence techniques. Additionally, the program emphasizes problem framing, data ethics, and data storytelling, paired with real projects and a capstone challenge. This combination of theory and practical exposure ensures you are fully prepared to excel in the analytics field and stand out in the competitive job market.
Furthermore, we provide expert-led training by experienced mentors who guide you step-by-step through the course. We also offer 100% placement assistance, supporting you throughout your job search. From resume building and interview preparation to connecting you with top employers, our team is with you at every stage. This support helps you confidently step into the industry and launch a successful career. Consequently, by enrolling in our Best 100% Placement Assistance Business Analytics Course, you gain practical skills, industry-recognized certification, and the confidence to start a rewarding career in Business Analytics, Data Science, and Strategic Decision-Making.
Business Analytics Course
Module 1 - Orientation & BA Mindset
- What is Business Analytics? Roles, lifecycle, data - insight - action loop.
- Lab: Map lifecycle to a familiar business (retail or Saas).
- Problem statements, KPIs vs. vanity metrics, KPI tree design; ethics (PII, bias).
- 1-page problem-framing brief + KPI tree for a chosen domain.
- Brief (PDF) + KPI tree diagram.
Module 2-Spreadsheet Analytics
- Data hygiene, named ranges, structured references; IF, SUMIFS, XLOOKUP.
- Pivot tables, grouping, calculated fields, % of total, quick charts.
- Scenario & sensitivity: Data Tables, Goal Seek, scenario manager.
- Optimization intro: linear models with Solver (capacity, mix problems).
- Spreadsheet QA: auditing formulas, error traps, documentation tab.
- Build a small P&L / ops model on Retail Superstore.
- A 1-sheet financial/ops model with three scenarios + summary chart.
- .xlsx + readme sheet (purpose, inputs, outputs).
Module 3-SQL for Analysts
- SELECT, WHERE, ORDER; data types; casting; NULL handling.
- INNER/LEFT/RIGHT/FULL joins; join pitfalls; primary/foreign keys.
- Aggregations & GROUP BY; HAVING; multi-level aggregations.
- Subqueries & CTES; anti-joins; EXISTS vs IN.
- Window functions: ROW_NUMBER, RANK, LAG/LEAD, rolling sums.
- Date/time; time-series bucketing; cohort tables.
- Basics of indexing, explain plans, and modeling a star schema for Bl.
- Build an analytics mart (facts & dims) from Online Retail II.
- Query pack (10 queries with expected outputs) + ERD PNG.
- .sql file(s) + ERD image + short doc on table grain & keys.
Module 4 - Python for Data Analysis
- Environment, notebooks, data IO, pd.DataFrame essentials.
- Selection, filtering, groupby, agg, reshaping (melt, pivot_table).
- Missing values, outliers, string/datetime ops; feature derivation.
- EDA patterns: distributions, pairwise relationships, profiling mindset.
- Matplotlib for analysts: visuals that explain (bar/line/box/scatter).
- Reusable pipelines: functionizing, parameters, config & seeds.
- Packaging results: CSV/Parquet, notebook to HTML, repo hygiene.
- Two Jupyter notebooks on NYC TLC trips (sample).
- EDA notebook,
- data-prep notebook with reusable functions.
- .ipynb + /data + /src (if created) + README with run steps.
Module 5-Statistics for Business
- Descriptive stats, sampling, CLT intuition.
- Confidence intervals for means/proportions; margin of error tradeoffs.
- Hypothesis testing (one/two-sample), p-values, Type I/II, power.
- A/B test design: sample size, randomization, guardrails, CUPED (concept).
- Correlation vs. causation; confounders; simple regression as inference.
- Practical significance, effect sizes; making a decision memo.
- A/B test plan + mock readout using simulated data.
- Test plan (doc) + calculations (Excel or Python) + 1-page memo.
Module 6 - Data Wrangling & Quality
- Data contracts, schema drift, types; data dictionary creation.
- Missingness mechanisms (MCAR/MAR/MNAR), outlier strategies, dedupe.
- Validation rules (row/column/table), QA checklist, reproducibility log.
- Clean a messy CSV (web analytics or CRM leads) and document rules.
- Cleaned data + data dictionary + QA checklist (CSV/PDF).
Module 7 - Data Visualization & BI
- Visual grammar; choosing charts; color/label/scale best practices.
- Import/Modeling: star schema, relationships, date table, measures.
- Calculations: DAX (Power BI) or LOD/calculated fields (Tableau).
- Interactions, filters, drill-down, bookmarks; performance tuning.
- Publishing/sharing; row-level security basics; governance & refresh.
- UX polish: annotations, QA, accessibility; executive "one-pager" view.
- Executive dashboard (3 core KPIs + 2 drill paths + 1 time view).
- PBIX/TWBX (or packaged Tableau) + screenshot + short user guide.
Module 8 - Business Analytics Methods
- Cohort setup (signup/purchase), retention curves, lifetime metrics.
- Funnel analysis: drop-offs, micro-conversions, attribution basics.
- Segmentation: RFM, k-means (concept + analyst-friendly execution).
- Forecasting basics: moving average, ETS; ARIMA intuition; accuracy metrics.
- Optimization revisited: linear programming for mix/capacity (Excel Solver).
- Experiment analysis: CUPED idea, uplift; variance checks; guardrails.
- Synthesis workshop: combine 2+ methods on a case.
- Choose one domain case (marketing/ops/finance) and deliver a forecast + experiment readout (or segmentation + funnel).
- Notebook/Excel + short slide readout (max 7 slides).
Module 9 - ML for Analysts
- Supervised learning pipeline: split, baseline, feature prep.
- Regression (linear, regularized) - RMSE/MAE, residuals, leakage checks.
- Classification (logistic, tree/forest baseline) - ROC/PR, calibration, lift.
- Feature importance, permutation tests; stability checks; fairness intro.
- Communication: model cards, assumptions, limitations, decision thresholds.
- Build a baseline churn or credit-risk model;
- Produce an evaluation report + model card.
- Code notebook + CSV of predictions + 2-page report + model card.
Module 10-Domain Cases: Marketing, Ops, Finance
- Three parallel mini-cases (pick one):
Marketing: Promo effectiveness & incrementality; audience targeting.
Ops: Staffing & SLAS; queue basics; stockout economics.
Finance: Cashflow risk flags; simple stress testing.
- Decision narratives, trade-off tables, risk/mitigation plans.
- 1-page executive memo + "if-then" action plan.
- Memo (PDF) + 1 slide with decision table.
Module 11 - Storytelling & Stakeholder Communication
- Narrative arc: context - insight - implication - recommendation; metric glossary writing.
- Dry-run presentations; handling objections; defining "done" & next steps.
- 5-slide stakeholder deck + 2-minute pitch (recorded or live).
- Slides (PPT/PDF) + metric glossary appendix.
Module 12 - Capstone
- Ship an end-to-end, decision-ready analytics project.
- Retail demand & promo - forecast units; test promo uplift; inventory implications.
- SaaS churn & expansion - cohort + churn model; expansion propensity; playbook.
- Ops & inventory service levels vs. cost; LP optimization for replenishment.
- Scoping & data access; risk log; acceptance criteria.
- Data audit, dictionary, quality plan.
- SQL mart build (facts/dims) and extracts.
- Python EDA + feature prep (or Excel if chosen track).
- Method core (forecast/model/optimization) + validation.
- BI dashboard build; KPI checks; RLS (if needed).
- Executive report & deck; run-book; model card (if ML).
- Final presentation; Q&A; repository hand-off.
