Home/Data Analytics Course Syllabus in Noida
Syllabus · Reviewed September 20269 modules, 120+ hours, 75+ live sessions — following the order analysts actually work in: get the data, clean it, analyse it, interpret it, visualise it, communicate it.
Uncodemy's Data Analytics syllabus in Noida is organised into 9 modules across 120+ hours and 75+ live sessions, covering Excel, SQL, Python, Statistics, Data Cleaning/EDA, Power BI, Tableau, Generative AI, and a final Capstone. The curriculum is reviewed by Senior Trainer Irshad Khan (8+ years' experience) and is identical to the syllabus taught at Uncodemy's Delhi centre.
| # | Module | Hours | Tools |
|---|---|---|---|
| 1 | Excel for Data Analytics | 12 | Excel, Power Query |
| 2 | SQL & Database Management | 18 | MySQL |
| 3 | Python for Data Analytics | 20 | Python, Pandas, NumPy |
| 4 | Statistics & Business Mathematics | 12 | Excel, Python |
| 5 | Data Cleaning & Exploratory Data Analysis | 12 | Python, Pandas |
| 6 | Power BI | 18 | Power BI Desktop |
| 7 | Tableau | 10 | Tableau Public |
| 8 | AI & Gen AI for Data Analysts | 8 | ChatGPT, Copilot |
| 9 | Capstone Projects & Career Preparation | 10 | All tools |
| Total | 120 Hours | 10+ Tools | |
Advanced formulas — VLOOKUP, XLOOKUP, INDEX-MATCH, SUMIFS, COUNTIFS, nested IF; data cleaning with text functions, Find & Replace, Flash Fill and duplicate removal; Pivot Tables, PivotCharts, Slicers and Timelines; Power Query for import/transform; data validation, conditional formatting, dashboard design; charts including histogram, Pareto, combo and waterfall.
Relational database concepts, ER diagrams, normalisation; SELECT, WHERE, ORDER BY, GROUP BY, HAVING; all join types (inner, left, right, full outer, self, cross); subqueries and Common Table Expressions; window functions (ROW_NUMBER, RANK, LEAD, LAG); connecting SQL databases to Python.
Python fundamentals — variables, data types, loops, functions; lists, tuples, dictionaries, sets, file operations; NumPy arrays and vectorised operations; Pandas Series and DataFrames — indexing, filtering, merging, grouping; handling missing values and duplicates; Matplotlib, Seaborn and Jupyter Notebook.
Descriptive statistics (mean, median, variance, standard deviation); probability fundamentals and data distributions; sampling methods and the Central Limit Theorem; hypothesis testing — p-values, confidence intervals, t-tests, chi-square, ANOVA; correlation and regression basics; A/B testing in a business context.
Data quality assessment and profiling; handling missing values (deletion, imputation, flagging); outlier detection and treatment; feature creation and binning; univariate, bivariate and multivariate analysis; correlation heatmaps and structuring an EDA report.
Connecting to Excel, SQL, CSV and web sources; Power Query Editor, data modelling and relationships; DAX — calculated columns, measures, time intelligence; visual types, filters, slicers, bookmarks, drill-through; publishing to Power BI Service with scheduled refresh; row-level security basics.
Tableau architecture, data connection and blending; dimensions, measures and calculated fields; filters, groups, sets and hierarchies; parameters and dynamic controls; reference lines, trend lines and forecasting; dashboards, actions and stories.
Prompt engineering for data tasks; generating and debugging SQL/Python with AI assistance; Copilot in Excel and Power BI; AI-assisted insight summaries and report drafting; verifying AI output for hallucinations, wrong aggregations and false confidence; data privacy rules for what should never be pasted into a public AI tool.
End-to-end capstone project on a real business dataset; building a GitHub portfolio and dashboard showcase; analyst resume writing and LinkedIn optimisation; SQL and case-study interview practice; mock interviews with feedback.
The curriculum on this syllabus is designed by industry professional Mr. Irshad Khan (Data Analytics & AI Trainer, 8+ years across consulting and public-sector analytics) and technically reviewed by Mr. Upendra Kumar Tiwari (Senior Data Science Trainer, 15+ years' experience).
Uncodemy keeps the trainer who teaches a batch separate from the domain reviewer who verifies the curriculum — meaning the person teaching a course is not the only person checking whether the content is correct. Both trainers teach live sessions at the Noida centre.
Who want in-person classroom training at Uncodemy's Sector 1 centre.
From B.Com, BBA, B.Sc, B.Tech, BA or BCA backgrounds entering analytics.
In operations, sales, finance, HR or marketing formalising their data skills.
Moving from Excel into SQL, Python and BI tools.
A graduate degree in any discipline. No prior programming experience required — Python and SQL are taught from the first line of code.
9 modules across 120+ hours and 75+ live sessions.
Yes. Module 8 is a dedicated 8-hour module on prompt engineering, AI-assisted SQL/Python, Copilot in Excel and Power BI, and verifying AI output.
No. Python and SQL are taught from scratch — the first two months focus on Excel and SQL, which need no traditional coding background.
5 guided projects across retail, banking, HR, e-commerce and healthcare domains, plus 1 capstone project of your choosing — all becoming part of your portfolio.
Yes. Uncodemy runs the identical 9-module, 120+ hour curriculum, reviewed by the same trainers, across its Noida and Delhi centres and live online batches.
Yes, a downloadable curriculum is available on the main Data Analytics Course in Noida page.
Last reviewed: September 2026. Curriculum verified by: Mr. Irshad Khan. Technical review: Mr. Upendra Kumar Tiwari.
Talk to a counsellor about batch timings, EMI plans & the 2-class trial.