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Data Analytics Level 2

Data Analytics Track

Data Analytics:
Level 2

4.9 (2,450 reviews)

From Complete Beginner to Hired Data Analyst — 3 Months Total. Every module is built around one question: what does a data analyst actually do at work on a Monday morning? Pull data from a database with SQL, clean it with Python or Excel, find the insight that matters, and present it in a dashboard that a manager can act on. Every project uses a real dataset from a real industry. The 3-month internship puts you on live business problems — the exact kind that appear in interviews because they come from real interviews.

Created by Avanteia
8,420 Total Enrolled
5 July 2026 Last Updated
Enroll Now
Data Analytics Level 2 Course
3 Months Duration
ACDAE Certified
Professional Level
10 Modules Syllabus
3 Months Duration
English Language
Certificate Included
Introduction

Overview

Avanteia's Certified Data Analytics Expert is a 6-month, project-driven program: 120 hours of classroom training across 3 phases and 10 modules, followed by a 3-month dual-track industry internship. The course covers Excel, SQL, Python, Power BI, Tableau, Statistics, and AI Tools. SQL and Excel are tested in Round 1 of almost every data analyst interview in India. Statistics is tested in Round 2. Business thinking is what determines whether you get the offer. This program makes Phase 2 effortless — and Phase 2 is what gets the job.

  • Write SQL queries that answer real business questions — window functions, CTEs, cohort analysis
  • Clean and analyse datasets of any size using Python Pandas — the skill that filters 80% of applicants
  • Build Power BI dashboards with DAX measures that non-technical managers actually use
  • Create Tableau stories and Looker Studio reports that drive business decisions
  • Perform A/B test analysis, hypothesis testing, and communicate statistical findings in plain English
  • Build a live Streamlit data app deployable as a URL — rare skill that impresses every interviewer
  • Use AI tools to do in 10 minutes what used to take 2 hours — ChatGPT for SQL, Copilot for Python
  • Answer every round of a data analyst interview: SQL round, Python round, case study round, statistics round
Excel SQL Python Power BI Tableau Statistics AI Tools
Goal

Program Promise

Every ACDAE graduate leaves with 9 portfolio pieces — all with live links, a Power BI report published to Service, a Tableau story on Tableau Public, a live Streamlit data app, 3 months of real business analytics work, ACDAE certification, and the ability to walk into any data analyst interview, answer every round, and prove it with a portfolio they can click through.

Curriculum

Syllabus

Click any module to expand and view topics, hands-on labs, and mini projects included.

  • Excel navigation, keyboard shortcuts, cell referencing — the analyst's daily toolkit
  • Data cleaning — removing duplicates, TRIM, CLEAN, text functions, handling blanks
  • Essential formulas — VLOOKUP, INDEX/MATCH, IF/IFS, COUNTIFS, SUMIFS, AVERAGEIFS
  • Pivot Tables — grouping, filtering, calculated fields, slicers, drill-down
  • Charts — choosing the right chart, formatting for business, before/after design
  • Power Query — connecting data sources, transforming columns, merge and append
  • Power Pivot and data models — table relationships, DAX basics inside Excel
  • Building an interactive Excel dashboard — slicers, dynamic charts, named ranges
  • Excel shortcuts and professional habits — what separates a fast analyst from a slow one
Hands-on Lab
Mini Project 1 — Retail Sales Dashboard
Tools: Microsoft Excel 365, Power Query, Power Pivot
Dataset: Superstore Sales dataset (50,000 rows), E-commerce returns data
  • Relational databases — tables, primary keys, foreign keys, ER diagrams, data types
  • SELECT, FROM, WHERE, ORDER BY, LIMIT — the foundation of every analyst query
  • Filtering — AND/OR/NOT, BETWEEN, IN, LIKE, IS NULL, IS NOT NULL
  • Aggregations — GROUP BY, HAVING, COUNT, SUM, AVG, MIN, MAX, COUNT DISTINCT
  • JOINs — INNER, LEFT, RIGHT, FULL OUTER — when each is correct with real examples
  • Subqueries and CTEs — layered queries, WITH clause, readable analytical SQL
  • Window functions — ROW_NUMBER, RANK, DENSE_RANK, LAG, LEAD, running total, moving avg
  • Date and time functions — DATEADD, DATEDIFF, DATE_TRUNC, FORMAT, period comparisons
  • String functions and CASE statements — cleaning in SQL, conditional columns
  • Analytical query patterns — cohort analysis, retention rates, funnel, RFM in SQL
  • SQL interview patterns — the 10 question types that appear in every DA interview
Hands-on Lab
Mini Project 2 — E-Commerce SQL Analysis
Tools: PostgreSQL, MySQL, SQLiteOnline, DBeaver, Mode Analytics
Dataset: E-commerce orders database (100K rows), HR database, SaaS subscription database
  • Descriptive statistics — mean, median, mode, std deviation, variance, IQR, percentiles
  • Distributions — normal, skewed, binomial — recognising them in real data visually
  • Probability basics — conditional probability, Bayes theorem, independent events
  • Hypothesis testing — null hypothesis, p-value, significance level, Type I vs Type II error
  • A/B testing — experiment design, sample size, statistical significance, Go/No-Go decision
  • Correlation vs causation — Pearson, Spearman, correlation matrix, common pitfalls
  • Linear regression — simple regression, R², slope interpretation, business use cases
  • Statistics in plain English — translating every concept for a non-technical manager
  • Statistics interview questions — the 10 most common statistics questions in DA interviews
Hands-on Lab
Mini Project 3 — A/B Test Decision Report
Tools: Excel, Python (SciPy, Statsmodels intro), Google Sheets
Dataset: Marketing A/B test dataset, product conversion dataset
  • How analysts think — decomposing a business question into 3–5 data questions
  • Metric frameworks — North Star metric, KPI trees, OKRs — reading data like a business
  • Business domains — retail, finance, marketing, product, HR, healthcare analytics patterns
  • Data quality — types of dirty data, detection, treatment, documentation
  • The analytics workflow — business question → data → clean → analyse → insight → action
  • Communicating data findings — SCQA framework, executive one-pagers, slide structure
  • Case study methodology — how to approach a business case in 20 minutes in an interview
  • Ethics in data — privacy, GDPR basics, bias in data, responsible analytics
  • Phase 1 review — SQL quiz, Excel speed test, statistics concepts check
Hands-on Lab
Mini Project 4 — Business Case Presentation
Tools: Google Slides, Notion, Miro (metric trees), Canva
Dataset / Lab: Zomato, Flipkart, Meesho real-world case studies
  • Python setup — Anaconda, Jupyter Notebook, VS Code, virtual environments, first script
  • Data types, variables, lists, dictionaries, tuples — everything through a data lens
  • Control flow — if/else, for loops, while, list comprehensions — analyst use cases
  • Functions — writing reusable analysis functions, default arguments, docstrings
  • File handling — reading CSV, Excel, JSON into Python, writing results back to file
  • NumPy — arrays, vectorised operations, broadcasting — why it is faster than loops
  • Introduction to Pandas — Series, DataFrame, read_csv, head, info, describe, dtypes
  • Pandas data selection — loc, iloc, boolean indexing, column selection patterns
  • Calling APIs in Python — requests library, parsing JSON, pulling real data
  • Connecting Python to SQL — SQLAlchemy, pandas read_sql, write_sql
  • Python for analysts best practices — clean notebooks, markdown cells, reproducibility
Hands-on Lab
Mini Project 5 — Python Data Explorer
Tools: Python 3.11, Anaconda, Jupyter Notebook, NumPy, Pandas, requests, SQLAlchemy, VS Code
Dataset / Lab: IPL match dataset, Indian stock market CSV, public REST APIs
  • Data cleaning with Pandas — fillna, dropna, duplicates, type conversion, rename, replace
  • String operations — str.contains, str.split, str.extract, str.replace, regex in Pandas
  • Merging and joining — merge(), concat(), join() — SQL-equivalent operations in Python
  • Groupby and aggregation — groupby(), agg(), transform(), named aggregations
  • Pivot tables in Python — pivot_table(), unstack(), cross-tabulations
  • Date and time analysis — pd.to_datetime, dt accessor, resample, time-series grouping
  • Apply, map, applymap — applying custom logic across rows, columns, elements
  • Full EDA pipeline — systematic exploration from raw data to 5 business insights
  • Matplotlib — figure anatomy, subplots, axes, styling, saving publication-quality charts
  • Seaborn — distribution plots, box plots, heatmaps, pairplots, categorical plots
  • Plotly — interactive charts, hover, zoom, dropdown filters — the interview differentiator
  • Chart design principles — right chart type, colour theory, titles, annotations, declutter
  • Storytelling with charts — sequencing visuals to lead an audience to one clear insight
  • Streamlit — building and deploying a Python data app that anyone can use via a URL
  • Automating analysis — Python scripts that run on a schedule and email results
  • Handling large datasets — chunked reading, dtype optimisation, memory profiling
  • Python analytics interview questions — the 15 most tested Pandas questions
Hands-on Lab
Mini Project 6 — Live EDA + Streamlit App
Tools: Pandas, NumPy, Matplotlib, Seaborn, Plotly, Streamlit, schedule, smtplib, Jupyter Notebook
Dataset / Lab: Zomato restaurant dataset, Airbnb listings data, IPL dataset, Indian census data
  • Time series fundamentals — trend, seasonality, cyclicality, decomposition
  • Moving averages, rolling stats — 7-day, 30-day rolling mean, std for anomaly detection
  • Prophet for business forecasting — fit, predict, plot, interpret confidence intervals
  • ChatGPT and Claude for data analysis — writing SQL with AI, explaining code, insight prompts
  • GitHub Copilot in Jupyter — AI-assisted Pandas and visualisation coding
  • pandas-ai — asking natural language questions directly to your DataFrame
  • Text-to-SQL tools — Vanna.ai, asking your database questions in plain English
  • Web scraping basics — BeautifulSoup, scraping a public product listing or news site
  • Building an automated weekly report pipeline — pull data → analyse → generate charts → email
  • Phase 2 review — Pandas speed test, visualisation critique, EDA portfolio check
Hands-on Lab
Mini Project 7 — Automated Analytics Pipeline
Tools: Prophet, Statsmodels, ChatGPT, Claude AI, GitHub Copilot, pandas-ai, Vanna.ai, BeautifulSoup
Dataset / Lab: Stock price time series, retail sales monthly data, web-scraped product data
  • Power BI Desktop interface — report, data, and model views — understanding all three
  • Connecting data sources — Excel, SQL, SharePoint, REST APIs, web URLs
  • Power Query in Power BI — transforming, cleaning, merging before modelling
  • Data modelling — star schema, fact vs dimension tables, relationships, cardinality
  • DAX fundamentals — measures vs calculated columns, CALCULATE, FILTER, ALL, ALLEXCEPT
  • DAX time intelligence — TOTALYTD, DATEADD, SAMEPERIODLASTYEAR, MoM%, YoY%
  • DAX advanced — RANKX, SWITCH, variables (VAR), ISINSCOPE, dynamic titles
  • Visualisations — choosing visuals, conditional formatting, KPI cards, custom tooltips
  • Interactive features — slicers, drill-through, drill-down, bookmarks, page navigation
  • Row-level security — dynamic RLS by username, testing in desktop before publishing
  • Publishing to Power BI Service — workspaces, scheduled refresh, sharing, embedding
  • Power BI interview questions — DAX scenarios, model design questions, top 15
  • Capstone Power BI dashboard — 3-page executive report built end to end in class
Hands-on Lab
Mini Project 8 — Executive Power BI Report
Tools: Power BI Desktop, Power BI Service, DAX Studio, Bravo for Power BI, Power BI Copilot
Dataset / Lab: AdventureWorks sales database, HR analytics dataset, financial P&L data
  • Tableau Desktop interface — shelves, marks, show me, data pane — first chart in 5 minutes
  • Connecting and preparing data — live vs extract, joins, unions, data interpreter
  • Core chart types — bar, line, scatter, map, treemap, bullet, Gantt — choosing correctly
  • Calculated fields — basic formulas, table calculations (percent of total, running sum, rank)
  • LOD expressions — FIXED, INCLUDE, EXCLUDE — the hardest and most powerful Tableau skill
  • Geographic maps — filled maps, symbol maps, custom geocoding for Indian cities
  • Dashboard design — layout containers, actions, filters, device designer, mobile view
  • Tableau Stories — narrative analytics, guided data walkthrough, story points
  • Publishing to Tableau Public — shareable link, embedding, maintaining updates
  • Google Looker Studio — connecting GA4, Sheets, BigQuery, building live dashboards
  • Looker Studio blended sources — joining GA4 + Google Ads + Sheets in one report
  • KPI design — choosing actionable metrics, avoiding vanity metrics, one-page scorecards
  • Data storytelling — SCQA structure, sequencing charts, the 5-second test for dashboards
  • Dashboard critique workshop — reviewing bad dashboards and redesigning them live
  • Power BI vs Tableau vs Looker Studio — how to answer this in an interview
Hands-on Lab
Mini Project 9 — Tableau Public Portfolio Story
Tools: Tableau Desktop (Public), Tableau Public, Google Looker Studio, GA4 (demo account), BigQuery (free)
Dataset / Lab: Indian election data, World Bank development indicators, GA4 demo account
  • AI-powered analytics — ChatGPT + Claude for SQL generation, data insights, report writing
  • AI for Power BI — Copilot in Power BI, smart narratives, Q&A visual
  • AI for Excel — Copilot in Excel, formula generation, data insights, Python in Excel
  • Capstone project — full end-to-end analytics project on chosen real-world domain
  • Portfolio building — GitHub, Tableau Public, Power BI Service, Notion portfolio site
  • Resume writing for data analysts — ATS-friendly format, quantified achievements, tech stack
  • LinkedIn optimisation — headline, about, featured projects, skills, activity
  • SQL interview preparation — top 20 questions with live solutions
  • Python interview preparation — top 15 Pandas questions with live solutions
  • Case study interview preparation — solving product analytics cases in 20 minutes
  • Statistics interview preparation — explaining p-value, A/B testing, regression without jargon
  • Mock interview — full 45-minute DA interview: SQL round + Python round + case study
Hands-on Lab
CAPSTONE — End-to-End Analytics Project
Tools: ChatGPT, Claude AI, Copilot (Excel + Power BI), GitHub, LinkedIn, LeetCode, Notion
Dataset / Lab: Student's chosen domain: retail, HR, finance, marketing, or product
Internship

3-Month Dual Track Internship

Official Internship Title: Junior Data Analyst — Business Intelligence & Analytics. Students work within Avanteia's Analytics team and on client-facing data work. Track 1 covers data operations, SQL reporting, Power BI dashboard delivery, and weekly business reporting. Track 2 gives full project ownership — student chooses the domain, finds the data, runs the analysis, builds the dashboard, and presents findings to a business panel. Every week has one clear deliverable.

Track 1 — Data Operations (Weeks 1–8)

Real business data, SQL reports, Python cleaning pipelines, Power BI dashboards, weekly business reporting — delivered to real stakeholders with real deadlines.

  • Set up professional analytics workspace: PostgreSQL + DBeaver connected, Python venv with Pandas/Matplotlib, Power BI Desktop
  • Study Avanteia's data request workflow — how requests come in, how work is tracked, how deliverables are formatted
  • Receive first SQL data request: write a query returning last 90 days of revenue by region and product category
  • Validate query results manually: check total against known figure, spot-check 5 individual rows
  • Clean the output in Excel — fix column types, rename headers, remove test/null records
  • Build a pivot table summary: total revenue by region, top 5 products, WoW change for each
Weekly Deliverable
Deliver Week 1 SQL report to manager: query file + clean CSV + pivot summary + 2-sentence insight
  • Write a cohort retention query: customers who ordered in Month 1 and returned in Months 2 and 3
  • Write a window function query: rank products by revenue within each category using DENSE_RANK, show top 3 per category
  • Write a month-over-month revenue change query using LAG — output: month, revenue, prev month revenue, % change
  • Identify 3 data quality issues in the database — document each: table, column, issue type, count affected, suggested fix
  • Write a data quality monitoring query for each issue — can be run weekly to catch new occurrences
  • Build an Excel report from the SQL outputs: cohort table, ranking table, MoM trend line
Weekly Deliverable
Deliver Week 2: all SQL queries saved and commented, Excel report, data quality findings document
  • Receive a real messy dataset — run first-pass EDA: shape, dtypes, nulls, duplicates, value distributions, outliers
  • Write a Python cleaning script: fix all identified issues, standardise column names, save clean version to CSV with log
  • Write a Pandas EDA notebook: distribution plots for all numeric columns, correlation heatmap, top 5 insights with markdown
  • Automate the cleaning script: parameterise the input path so it works on any new CSV dropped in the folder
  • Connect the cleaned data to PostgreSQL using SQLAlchemy — push to a new table, verify row count matches
  • Build 4 Matplotlib charts from the clean data: revenue trend, category breakdown, top customers, monthly growth rate
Weekly Deliverable
Deliver Week 3: Python notebook on GitHub, cleaning script documented, 3 actionable business observations
  • Build a weekly report template in Excel: previous week sales, WoW change %, top and bottom 3 products, flag for attention
  • Write a Python script that generates this report automatically: connects to DB, runs queries, writes to Excel, saves with date
  • Test the script: run it for last 4 weeks of data — verify output matches manual calculation
  • Schedule the script to run every Monday morning — document setup steps so anyone on the team can maintain it
  • Build a Looker Studio dashboard connected to the same data source — live refresh, 4 scorecards, 2 charts
  • Present the automated report system to manager — demonstrate that it runs without human input
Weekly Deliverable
Deliver: Python script + schedule setup + Looker Studio link + 1-page system documentation
  • Receive Power BI dashboard brief: what the stakeholder needs to see, how often they will use it, what decisions it drives
  • Connect the brief's data sources to Power BI, clean in Power Query, build star schema data model
  • Write 6 DAX measures: total revenue, MoM growth %, YTD cumulative, top N filter, category share, moving average
  • Build Page 1: executive overview — 4 KPI cards, revenue trend line, top product bar chart
  • Build Page 2: drill-down view — matrix with conditional formatting, slicer for date/region/category
  • Add row-level security for the stakeholder's region — test it works correctly before publishing
  • Publish to Power BI Service — configure scheduled refresh, share link with manager for review
Weekly Deliverable
Deliver: published dashboard to Power BI Service with scheduled refresh and shareable link
  • Receive stakeholder feedback on Week 5 dashboard — categorise each comment: bug vs enhancement vs scope change
  • Implement all feedback: update DAX measures if needed, adjust layout, add or remove visuals
  • Add a bookmarked 'comparison mode' — stakeholder can toggle between current period and same period last year
  • Write a dashboard user guide: what each page shows, how to use slicers, how to interpret the KPIs
  • Present the revised dashboard to the stakeholder in a 10-minute walkthrough — explain every number
  • Write a post-delivery retrospective: what took longer than expected, what would you do differently
  • Publish final version — this dashboard goes into your portfolio with the stakeholder's permission
Weekly Deliverable
Deliver: final dashboard + user guide + post-delivery retrospective
  • Receive a real or simulated A/B test dataset from a marketing or product campaign
  • Run the statistical analysis in Python: sample sizes, conversion rates, p-value, confidence interval, effect size
  • Build distribution plots showing control vs variant performance — make the difference visible visually
  • Run a power analysis: was the test adequately powered? Would you need more data to be sure?
  • Write the Go / No-Go recommendation with full reasoning — this is what a real analyst delivers
  • Build a single Power BI page showing the A/B results for a non-technical audience — clear, actionable
  • Present findings to manager: state the recommendation clearly in the first sentence, back it up after
Weekly Deliverable
Deliver: Go/No-Go recommendation + Power BI A/B results page + presentation to manager
  • Receive a full analytics brief: business problem + 3 data sources + stakeholder who needs the answer
  • Day 1: understand the business context, write 5 analytical questions the data can answer
  • Day 2: SQL extraction + Python cleaning + EDA — document every finding as you go
  • Day 3: build the Power BI dashboard — all pages, all DAX, all formatting — in one day
  • Day 4: write the analysis report — insights, recommendations, supporting data, executive summary
  • Day 5: present to Avanteia panel in mock stakeholder review — 10 minutes + 10 minutes Q&A
  • Receive Track 1 written evaluation — technical quality, communication, speed, business thinking assessment
Weekly Deliverable
Deliver: full analytics brief response — SQL + Python + Power BI + report + live presentation + evaluation

Track 2 — Own Your Project (Weeks 9–12)

Complete ownership — student chooses domain, finds data, cleans it, analyses it, builds the dashboard, and presents findings live. This is the portfolio centrepiece.

  • Choose your domain: retail, finance, HR, healthcare, sports, or product analytics — write a 1-page project brief
  • Define 5 specific business questions your project will answer — these drive every analysis decision after this
  • Find and download your primary dataset: Kaggle, data.gov.in, World Bank, or a public API
  • Read the dataset documentation: understand every column, how data was collected, known limitations
  • First-pass data quality assessment: shape, nulls, duplicates, outliers, date ranges, value consistency
  • Write a data dictionary: for every column — name, data type, description, null rate, sample values
Weekly Deliverable
Deliver project brief + dataset + data dictionary to manager — get approval before building anything
  • Load data into PostgreSQL — write the CREATE TABLE statement, import CSV, verify row count
  • Write 10 SQL queries answering your 5 business questions — 2 per question, different angles
  • Run the full Python EDA pipeline on the dataset — cleaning, distributions, correlations, groupby
  • Engineer 3 new features from the existing columns — document the reasoning for each
  • Build 6 Python visualisations: 2 distribution plots, 2 trend charts, 1 heatmap, 1 grouped bar
  • Write a 1-page SQL findings document and a 1-page EDA findings document — key insight per chart
  • Peer review: show findings to one other intern, collect feedback, update top 2 charts based on it
Weekly Deliverable
Deliver: peer-reviewed SQL findings + EDA findings + 6 Python visualisations
  • Choose your BI tool for this project — Power BI if you want DAX depth, Tableau if you want visual flexibility
  • Connect your cleaned dataset, build the data model, create all calculated measures or fields
  • Build Page 1: executive overview — what the business needs to see at a glance
  • Build Page 2: detailed breakdown — the analysis behind the overview, drill-down capability
  • Build Page 3: recommendation — what should the business do next based on what you found?
  • Publish the dashboard: Power BI Service or Tableau Public — get the live shareable URL
  • Write the executive summary: 1 page, 5 insights, 3 recommendations, quantified where possible
Weekly Deliverable
Deliver: live dashboard link + executive summary
  • Polish the full project: fix any issues from Week 11, ensure all charts are labelled, all numbers verified
  • Prepare a 10-slide presentation deck: problem, data, methodology, 5 findings, 3 recommendations, impact
  • Record a 3-minute project walkthrough video — upload to LinkedIn and portfolio site
  • Compile complete GitHub repository: SQL file, Python notebook, README with project overview and findings
  • Build or update portfolio site: project showcase with live dashboard link, executive summary, and video
  • Final presentation to Avanteia panel: 10-minute presentation + 10-minute Q&A — receive certificate
  • Apply to 7 jobs: 3 data analyst, 2 business analyst, 1 BI developer, 1 product analyst — track in Notion
Weekly Deliverable
Deliver: polished project + presentation deck + video + GitHub repo + portfolio site + 7 job applications
Results

What You Will Learn

SQL & Databases

Write analytical SQL with window functions, CTEs, cohort analysis, and RFM segmentation on real business databases.

Python & Pandas

Clean, analyse, and visualise datasets of any size using Pandas, NumPy, Matplotlib, Seaborn, and Plotly.

Power BI & DAX

Build production dashboards with star schema models, time intelligence measures, RLS, and Power BI Service publishing.

Tableau & Storytelling

Create narrative Tableau stories, Looker Studio reports, and dashboards that pass the 5-second test for clarity.

Statistics & A/B Testing

Run hypothesis tests, A/B tests, regression analysis, and communicate statistical findings in plain English.

AI-Powered Analytics

Use ChatGPT, Claude, Copilot, pandas-ai, and Vanna.ai to generate SQL, write Python, and accelerate insight delivery.

Careers

Career Outcomes

Data Analyst

₹4L – ₹14L/yr at Flipkart, Amazon, Swiggy, Zepto, and startups.

Business Analyst

₹5L – ₹16L/yr at KPMG, Deloitte, Accenture, and the Big 4.

BI Developer

₹6L – ₹18L/yr at TCS, Wipro, Infosys, MNCs, and consulting firms.

Product Analyst

₹6L – ₹20L/yr at PhonePe, CRED, Razorpay, and Meesho.

Marketing Analyst

₹4L – ₹12L/yr at D2C brands, agencies, and e-commerce companies.

Data Associate

₹3.5L – ₹9L/yr at any data-driven company, entry level.

Portfolio

Portfolio at Graduation

Retail Sales Excel Dashboard

Interactive Excel dashboard with slicers — proves Excel mastery, the #1 expected skill for every DA role.

E-Commerce SQL Analysis

15 queries with window functions — proves SQL depth, tested in Round 1 of 90% of analytics interviews.

A/B Test Decision Report

Python + statistics — proves statistical thinking, hypothesis testing, and business judgement.

Full EDA + Streamlit App

Live Jupyter notebook with a deployed Streamlit app URL — proves Python Pandas + deployment capability.

Automated Weekly Report

Self-running Python pipeline — proves automation thinking; analysts who save time get promoted.

Executive Power BI Report

Published to Power BI Service with live link — proves Power BI at production level, most requested BI tool in India.

Tableau Public Story

Published narrative with shareable link — proves Tableau + storytelling for consulting and product roles.

Track 1 Capstone Engagement

Full pipeline on real business data — proves you can deliver under real deadlines with real stakeholders.

Track 2 Independent Project

Domain of choice, end to end — the interview centrepiece that proves self-directed thinking and ownership.

Stack

Complete Tools & Technology Stack

Spreadsheets

Microsoft Excel 365, Power Query, Power Pivot, Google Sheets, Copilot in Excel

SQL & Databases

PostgreSQL, MySQL, SQLiteOnline, DBeaver, Mode Analytics, Google BigQuery

Python — Core

Python 3.11, Anaconda, Jupyter Notebook, VS Code, NumPy, Pandas, requests, SQLAlchemy

Python — Viz

Matplotlib, Seaborn, Plotly, Altair, Streamlit (deploy live data apps)

Python — Analytics

SciPy, Statsmodels, Prophet (forecasting), BeautifulSoup (scraping), schedule (automation)

AI for Analytics

ChatGPT, Claude AI, GitHub Copilot, pandas-ai, Vanna.ai, Power BI Copilot

Power BI

Power BI Desktop, Power BI Service, DAX Studio, Bravo for Power BI, Power BI Copilot

Tableau

Tableau Desktop (Public), Tableau Public platform, Tableau Prep (intro)

BI & Reporting

Google Looker Studio, Google Analytics 4 (demo), Metabase (intro), Google Data Studio

Datasets

Kaggle, data.gov.in, UCI ML Repository, World Bank Open Data, Google Dataset Search

Portfolio & Career

Git, GitHub, Notion, Canva, Loom, LinkedIn, LeetCode (SQL section)

Certification

Certification Details

  • Certification: ACDAE — Avanteia Certified Data Analytics Expert
  • Total Duration: 6 Months — 3 months classroom (120 hours) + 3 months internship
  • Exam Format: Live 30-minute SQL test + Python Pandas exercise + dashboard review
  • Portfolio Requirement: Minimum 5 completed projects with live links
  • Internship Requirement: Complete all 12 weeks — Track 1 evaluation passed + Track 2 project presented to panel
  • Passing Standard: Pass technical exam + approved portfolio + approved Track 2 project presentation
  • Certification Validity: 3 Years — annual CPD: 1 public portfolio update or industry certification (Google, Microsoft)
  • Prerequisites: Basic computer skills — no prior data, coding, or statistics knowledge required
  • Next Level: ACDAE Advanced (120 hrs) — ML for analysts, advanced Python, cloud analytics, dbt, Snowflake
Promise

Program Promise

Every ACDAE graduate leaves with: 9 portfolio pieces — all with live links · a Power BI report published to Service · a Tableau story on Tableau Public · a live Streamlit data app · 3 months of real business analytics work · ACDAE certification · and the ability to walk into any data analyst interview, answer every round, and prove it with a portfolio they can click through.

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