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Data Analytics Level 2

Job-Ready Track · From Spreadsheets to Dashboards

Data Analytics:
Level 2

4.9 (2,450 reviews)

A build-first programme that takes you from Excel basics to advanced analysis, then into SQL and MySQL databases, Python with NumPy and pandas, and interactive dashboards in Power BI and Tableau. Every phase ends with a portfolio project, every number you present must trace back to the data that produced it, and you finish with a public portfolio, live dashboards, interview practice and a 3-month dual-track internship on real business problems.

Created by Avanteia
8,420 Total Enrolled
02 Oct 2026 Last Updated
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Data Analytics Level 2 ACDAE course in Goa – Excel, SQL, Python, Power BI and Tableau training at Avanteia
3 Months Duration
ACDAE Certified
Level-02 Level
14 Modules Syllabus
3 Months Classroom + 3-Month Internship
120 Hours 40 Labs · 6 Projects
ACDAE Certification
Introduction

Overview

Avanteia's Certified Data Analytics Expert (ACDAE) is a 6-month, build-first programme: 120 hours of classroom training over 12 weeks across 14 modules, followed by a 3-month dual-track industry internship. It starts where most business data already lives, in Excel, then moves the same questions into a real MySQL database with SQL, into code with Python, NumPy and pandas, and finally into the two BI tools employers ask for most: Power BI and Tableau. Classes run Monday to Friday, about 70% hands-on, with every Friday a Build Day — and five responsible-data rules (verify, protect, chart honestly, disclose, be honest) run through every module. No coding or statistics background is required.

  • Clean and analyse data in Excel from basic to advanced — XLOOKUP, pivot tables, what-if analysis and Power Query
  • Design a MySQL database and answer multi-table questions with joins, CTEs and window functions
  • Write Python, compute statistics with NumPy and clean messy data with pandas — and prove your SQL and pandas numbers agree
  • Explore data visually and make honest charts with Matplotlib and Seaborn
  • Model data, write tested DAX measures and publish an interactive Power BI report
  • Build and publish an interactive Tableau story recruiters can open with one click
  • Check every AI-written formula, query and script against the actual result
  • Practise every round of a data analyst interview: Excel test, live SQL, pandas, DAX, case study and HR
Excel Power Query MySQL & SQL Python NumPy & pandas Matplotlib & Seaborn Power BI & DAX Tableau GitHub
Goal

Program Promise

Every ACDAE graduate leaves with six course portfolio projects — an Excel dashboard, a SQL case study, a Python EDA report, a Power BI report, a Tableau story and a capstone — plus two internship projects, five or more public GitHub repositories, a Tableau Public profile, a tailored resume, two scored mock interviews and ACDAE certification. Hiring outcomes depend on effort, the market and the employer.

Curriculum

Syllabus

Click any module to expand and view topics, hands-on labs and tools used.

  • What data analytics is, and how it differs from data science and data engineering
  • The four kinds of analytics: descriptive, diagnostic, predictive, prescriptive
  • The analyst workflow: ask, collect, clean, analyse, visualise, recommend
  • Types of data: structured and unstructured, numerical and categorical
  • Tidy data: one row per record, one column per variable
  • KPIs and metrics, and why a vague question gives a vague answer
  • Data quality: accuracy, completeness, consistency, timeliness
  • Data ethics: privacy, consent and honest reporting
Hands-on Lab
Break Business Questions into Measurable KPIs Write a Manager-Ready Problem Statement Spot Tidy-Data Violations in a Messy Sheet Data-Quality Note
Tools Used
Microsoft Excel (Microsoft 365) Google Sheets Learning Journal
  • Workbook, worksheet, cell and range; Excel Tables and structured references
  • Data types, and why numbers stored as text silently break formulas
  • Relative, absolute and mixed references ($A$1, A$1, $A1)
  • Order of operations, and building a formula one piece at a time
  • Number, date and currency formats; conditional formatting rules
  • Sorting, filtering, and data validation with drop-down lists
  • Core functions: SUM, AVERAGE, COUNT, IF, AND, OR, SUMIFS, COUNTIFS, AVERAGEIFS
  • Text and date functions for cleaning real exports: TRIM, PROPER, TEXTJOIN, DATEDIF, EOMONTH, NETWORKDAYS
  • Reading the common errors: #VALUE!, #DIV/0!, #N/A, #REF!, #NAME?
Hands-on Lab
Validated Employee Records Sheet Clean a Messy Sales Export with a Cleaning Log Answer 10 Business Questions with SUMIFS & COUNTIFS
Tools Used
Excel Tables Data Validation Conditional Formatting Text to Columns Flash Fill Remove Duplicates
  • Lookup logic: exact versus approximate match
  • VLOOKUP and its limits; INDEX-MATCH; XLOOKUP with a not-found value
  • Nested IF versus IFS and SWITCH; IFERROR for clean output; SUMPRODUCT
  • Dynamic arrays: FILTER, SORT, UNIQUE, SEQUENCE; LET for readable formulas
  • Pivot tables: rows, columns, values, filters and calculated fields
  • Grouping dates, slicers and timelines across several pivots
  • What-if analysis: Goal Seek, Scenario Manager, Data Tables
  • Basic statistics: mean, median, mode, standard deviation, percentiles
Hands-on Lab
Merge Sheets with VLOOKUP, INDEX-MATCH & XLOOKUP Commission & Grading Calculator Live Top-10 Customer List with FILTER & SORT Pivot Table Analysis on 10,000 Rows Break-Even & Scenario Analysis
Tools Used
XLOOKUP Dynamic Arrays PivotTables & PivotCharts Slicers & Timelines Goal Seek & Scenario Manager Analysis ToolPak
  • ETL in plain words: extract, transform, load
  • Power Query: applied steps that repeat on every refresh
  • Append versus merge queries; unpivoting wide data
  • The data model and relationships (Power Pivot introduction)
  • Choosing the chart: comparison, trend, share, distribution
  • Dashboard layout: KPIs on top, trends in the middle, detail below
  • Combo charts, sparklines, KPI cards and connected slicers
  • Macros: what recording does, and when not to trust it
Hands-on Lab
Combine 12 Monthly Files with Power Query One-Click Refresh Test Project A: Excel Sales Performance Dashboard
Tools Used
Power Query Power Pivot Excel Charts & Sparklines Slicers Macro Recorder
  • Why databases instead of spreadsheets: size, integrity, many users at once
  • RDBMS concepts: tables, rows, columns, schemas
  • Primary keys, foreign keys and relationships (one-to-many, many-to-many)
  • ER diagrams and normalisation (1NF to 3NF) in plain words
  • MySQL data types: INT, DECIMAL, VARCHAR, DATE, DATETIME
  • SQL families: DDL, DML and DQL; NULL and why it is not zero or empty
  • SELECT, DISTINCT, aliases, ORDER BY, LIMIT; WHERE with AND, OR, IN, BETWEEN, LIKE, IS NULL
  • COUNT, SUM, AVG, MIN, MAX with GROUP BY and HAVING
  • The order SQL runs in versus the order you write it
Hands-on Lab
Design a Retail Database as an ER Diagram Build It in MySQL with Keys & Constraints Import CSV Data & Answer 10 Filtering Questions Business Summaries with GROUP BY & HAVING Safe UPDATE & DELETE Practice
Tools Used
MySQL Server MySQL Workbench ER Diagrams
  • INNER, LEFT and RIGHT joins; full outer joins with UNION; self joins
  • Join pitfalls: duplicated rows and silently missing matches
  • UNION versus UNION ALL
  • Subqueries in WHERE, FROM and SELECT; correlated subqueries
  • CTEs (WITH) for step-by-step, readable queries
  • CASE WHEN for segmenting and bucketing
  • Window functions: ROW_NUMBER, RANK, DENSE_RANK, LAG, LEAD, SUM() OVER
  • Views, indexes, EXPLAIN basics and stored procedures (introduction)
  • Checking AI-written SQL against the actual result
Hands-on Lab
Join Practice with Row-Count Checks Rewrite Subqueries as CTEs Customer Segmentation with CASE WHEN Ranking, Month-over-Month Growth & Running Totals Project B: SQL Business Case Study
Tools Used
MySQL MySQL Workbench Views & Indexes AI Assistants
  • Why Python for analysts: repeatable, shareable, no row limit
  • Jupyter notebooks versus scripts, and when to use each
  • Variables and data types: int, float, str, bool
  • Strings and f-strings; lists, tuples, dictionaries, sets
  • Conditionals, loops and list comprehensions
  • Functions: packaging steps so they can be reused
  • Reading a traceback: the errors beginners actually hit
  • Modules, pip, virtual environments; files and try / except; datetime
  • Git and GitHub: init, add, commit, push, README
Hands-on Lab
Salary & Grade Calculator Sales Records with Loops & Comprehensions Read, Clean & Summarise a CSV Push Your Project to GitHub
Tools Used
Python Anaconda Jupyter Notebook VS Code Git & GitHub
  • Why arrays beat lists: speed and vectorisation
  • ndarray, shape, dimensions and dtype
  • Creating arrays: array, arange, linspace, zeros, ones, random
  • Indexing, slicing and boolean masks; np.where
  • Broadcasting in plain words
  • Aggregations along an axis: sum, mean, median, std, percentile
  • Reshaping, stacking and splitting
  • Missing values: NaN and NaN-safe functions
Hands-on Lab
List vs. Array Speed Test with %timeit Marks Statistics by Subject & Student Simulate 1,000 Days of Store Sales Flag Outlier Days with Percentiles
Tools Used
NumPy Jupyter Notebook np.random (seeded)
  • Series, DataFrame and the index
  • Reading CSV, Excel, JSON and SQL tables
  • Selecting data: loc, iloc, boolean filters, query
  • The four data problems: missing values, duplicates, wrong types, inconsistent text
  • Handling missing values: drop, fill or flag, and why it matters
  • Dates and time series: parsing, resampling, rolling windows
  • groupby, pivot_table, crosstab and melt
  • merge, join and concat as the pandas version of SQL joins
  • Connecting to MySQL with read_sql; using an AI pair programmer and reading what it writes
Hands-on Lab
Clean a Messy Dataset with a Cleaning Log Reproduce SQL Results in pandas Monthly Resampling & Rolling Averages Pull MySQL Tables into pandas & Export to Excel
Tools Used
pandas Jupyter Notebook MySQL Excel
  • The EDA workflow: question, distribution, relationships, outliers, conclusion
  • Choosing the chart: comparison, trend, share, distribution, relationship
  • Anatomy of a good chart: title, axes, labels, legend, annotation
  • Misleading charts: truncated axes, cherry-picked ranges, dual axes, 3D
  • Correlation is not causation
  • Data storytelling: headline, evidence, so-what
  • Matplotlib, Seaborn and quick pandas charts; exporting at the right size for reports
Hands-on Lab
Find & Fix Two Misleading Charts Project C: Python EDA Report on GitHub
Tools Used
Matplotlib Seaborn pandas .plot() Jupyter Notebook GitHub
  • The Power BI ecosystem: Desktop, Service and Mobile
  • The workflow: get data, transform, model, visualise, publish
  • Power Query Editor inside Power BI; connecting to Excel, CSV, a folder and MySQL
  • Star schema: fact and dimension tables, relationships, cardinality, filter direction
  • Calculated columns versus measures
  • DAX: SUM, AVERAGE, COUNTROWS, DISTINCTCOUNT, DIVIDE; CALCULATE and filter context
  • Time intelligence: date tables, TOTALYTD, SAMEPERIODLASTYEAR, DATEADD
  • Report design: KPI cards, drill-down, drill-through, bookmarks, accessibility; row-level security concept
Hands-on Lab
Connect & Clean Three Sources Including MySQL Build a Star Schema with a Date Table 12 DAX Measures Tested Against Excel Project D: Power BI Sales & Operations Dashboard
Tools Used
Power BI Desktop Power BI Service Power Query DAX MySQL
  • Tableau Public versus Tableau Desktop
  • Dimensions and measures; discrete (blue) and continuous (green)
  • The Marks card: colour, size, label, detail, tooltip
  • Joins, relationships and blends
  • Calculated fields and IF / CASE logic
  • Table calculations: running total, percent of total, rank
  • LOD expressions (FIXED) in plain words; parameters for what-if views
  • Dashboards, actions and Stories; Power BI or Tableau: when each one wins
Hands-on Lab
Ten Core Charts Including Map & Dual-Axis Calculated Fields, LOD & Parameters Project E: Tableau Story on Tableau Public
Tools Used
Tableau Public Calculated Fields LOD Expressions Dashboard Actions
  • What recruiters scan for in the first 30 seconds
  • Role map: data analyst intern, MIS executive, BI analyst, junior SQL analyst, business analyst trainee
  • Resume: one page, impact verbs, numbers, ATS keywords — every line traced to something you built
  • Portfolio: GitHub, Tableau Public, Power BI screenshots, LinkedIn Featured
  • Explaining a project in two minutes: problem, approach, result, limits
  • Technical rounds: Excel tests, live SQL, pandas, DAX, case questions
  • HR rounds, stipend discussion, and finding internships honestly
Hands-on Lab
Resume Tailored to Two Real Postings GitHub & Tableau Public Portfolio Polish 75-Question Interview Bank Timed Excel & SQL Tests Technical & HR Mock Interviews Internship Sprint: 10 Tailored Applications
Tools Used
GitHub LinkedIn Tableau Public Profile STAR Story Bank Excel Application Tracker
  • Problem first: who has it, and how will we know it is answered
  • Scoping: the minimum viable analysis versus nice-to-have
  • Sprint planning with GitHub Projects or Trello; daily stand-ups
  • Peer review: checking someone else's numbers kindly and usefully
  • Demo craft: problem, data, insight, recommendation, limitation in three minutes
  • Capstone options:
    • End-to-end sales analytics pipeline (MySQL to pandas to Power BI)
    • HR attrition analysis
    • E-commerce customer segmentation and cohort study
    • Clinic operations dashboard on synthetic data
    • Public-markets dashboard from open data, or a trainer-approved project
Hands-on Lab
Sprint Board & GitHub Issues Peer-Reviewed Pull Request Reproduce on a Fresh Machine from the README Two-Minute Demo Video One-Page Business Case Study Capstone Demo Day
Tools Used
GitHub Issues & Projects Pull Requests Excel · SQL · Python Power BI / Tableau
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

Excel Basic to Advanced

Clean data, use SUMIFS and XLOOKUP, summarise with pivot tables, model with what-if and automate with Power Query.

MySQL & Advanced SQL

Design relational databases and answer business questions with joins, CTEs, window functions and views.

Python, NumPy & pandas

Write Python, compute fast statistics with NumPy, and clean, merge and analyse messy data in pandas.

EDA & Honest Charts

Explore datasets visually with Matplotlib and Seaborn and tell a clear story with headline, evidence and so-what.

Power BI & DAX

Build star-schema models, write tested DAX with time intelligence, and publish interactive reports.

Tableau & Storytelling

Create interactive Tableau dashboards and stories with LOD expressions and parameters on Tableau Public.

Careers

Target Roles

Data Analyst Intern / Junior Data Analyst

Excel, SQL, Python and pandas, Power BI or Tableau, and written insights.

MIS Executive / Reporting Analyst

Advanced Excel, pivot tables, Power Query and Excel dashboards.

Junior SQL / Database Analyst

Database design, MySQL, joins, CTEs, window functions and views.

Power BI Developer Trainee / BI Analyst

Power Query, data modelling, DAX, interactive reports and publishing.

Data Visualisation Analyst (Tableau)

Chart choice, honest visuals, Tableau dashboards and stories.

Python Data Analyst Intern

Python, NumPy, pandas, Matplotlib, Seaborn and EDA.

Business Analyst Trainee

KPIs, what-if analysis, SQL, dashboards and data storytelling.

Role titles vary by employer. These are the kinds of entry-level and internship roles the projects support, not guarantees of a job.

Portfolio

Portfolio at Graduation

Project A: Excel Sales Performance Dashboard

One-screen dashboard with KPI cards, slicers and a refresh-ready Power Query source.

Project B: SQL Business Case Study

15 business questions in a commented .sql file, two reporting views and a findings summary.

Project C: Python EDA Report

A cleaned public dataset, six honest charts and five insights, published on GitHub.

Project D: Power BI Sales & Operations Dashboard

Two-page report with DAX measures, drill-through and bookmarks, published or exported.

Project E: Tableau Story

An interactive dashboard and four-point story published to Tableau Public.

Capstone Project

An end-to-end analytics project using at least three tools, peer-reviewed and presented on Demo Day.

Internship Track 1 Capstone

Full analytics engagement on real business data with real deadlines and stakeholders.

Internship Track 2 Independent Project

Domain of your choice, end to end, presented to a business panel.

Stack

Complete Tools & Technology Stack

Spreadsheets

Microsoft Excel (Microsoft 365), Power Query, Power Pivot, Analysis ToolPak, Google Sheets

SQL & Databases

MySQL Server, MySQL Workbench, ER diagrams, views and indexes

Python — Core

Python, Anaconda, Jupyter Notebook, VS Code, NumPy, pandas

Python — Visualisation

Matplotlib, Seaborn, pandas .plot()

Power BI

Power BI Desktop, Power BI Service, Power Query, DAX

Tableau

Tableau Public — calculated fields, LOD expressions, parameters, dashboards and stories

AI Assistants

AI pair programmers for formulas, SQL and Python — always tested against the actual result

Datasets

Public, synthetic or anonymised datasets only — never real personal data

Portfolio & Career

Git, GitHub, GitHub Projects or Trello, LinkedIn, Tableau Public profile

Certification

Certification Details

  • Certification: ACDAE — Avanteia Certified Data Analytics Expert
  • Total Duration: 6 Months — 3 months classroom (120 hours) + 3 months internship
  • Classroom Format: 12 weeks, Monday to Friday, 2 hours a day — Friday is Build Day
  • 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. Laptop with 8 GB RAM or more (Windows recommended for Power BI)
  • Next Level: ACDAE Advanced (120 hrs) — ML for analysts, advanced Python, cloud analytics, dbt, Snowflake
Promise

Program Promise

Every ACDAE graduate leaves with six course portfolio projects — an Excel dashboard, a SQL case study, a Python EDA report, a Power BI report, a Tableau story and a capstone — plus two internship projects, five or more public GitHub repositories, a Tableau Public profile, a tailored resume, two scored mock interviews and ACDAE certification. Hiring outcomes depend on effort, the market and the employer.

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