AI for Data Analysis
A 4-week, 20-hour applied course for Indian learners who already use spreadsheets. You work like a junior analyst for fictional Surat businesses: inspect a raw sales file, clean it with an audit trail, define KPIs, ask ChatGPT and Julius AI to analyse it, and check every number before you trust it. You then choose honest charts, build a KPI dashboard, classify customer reviews, read campaign metrics, design a churn prediction, evaluate a model with a confusion matrix, forecast next month's sales with what-if scenarios and audit a report for privacy and quality. MonkeyLearn and Obviously AI are taught as legacy concepts (now part of Medallia and Zams); Akkio and DataRobot are taught at concept level with clearly labelled simulations. Twelve graded tasks, four weekly assignments and a 30-mark capstone BI dashboard; pass at 70/100 with at least 18/30 in the capstone. It is a practical entry point to junior analyst and MIS work; Power BI, Excel, SQL and statistics are the recommended next steps. No job, salary or placement is promised.
- Duration: 4 weeks
- Modules: 14
- Fee: Rs 15,999 (about Rs 2,666 a month on EMI)
- Field: AI & Automation
Roles this track prepares you for
- Junior Data Analyst
- MIS Executive
- Data Analyst
- Reporting Analyst
- Business Analyst Trainee
- Sales Analyst
- Data Quality Analyst
- Marketing Analyst
- Operations Analyst
- Business Intelligence Analyst
Curriculum
- Business Data and KPIs - Read a business table like an analyst: records, fields, data types, the four kinds of analytics and the KPIs a dataset can and cannot support. Week 1.
- Business data: rows, columns and data types
- Descriptive, diagnostic, predictive and prescriptive analytics
- KPIs and business questions from a sales report
- Data Cleaning and Preparation - Find and fix missing values, duplicates, invalid entries, mixed dates and suspicious discounts while keeping the raw file and a change log that reconciles totals. Week 1.
- Spot missing values, duplicates and invalid entries
- Clean a sales file and keep an audit trail
- Outliers, suspicious discounts and validation checks
- ChatGPT Data Analysis with Files - Upload a CSV or Excel file to ChatGPT, explore it with precise prompts, group and chart it, then verify every AI number against a manual check before drafting a short report. Week 1.
- Upload a CSV to ChatGPT and explore it
- Group, calculate KPIs and chart with ChatGPT
- Verify AI calculations and draft the report
- Charts, Dashboard Basics and Assignment 1 - Choose the chart that answers the question, spot misleading scales, design a readable KPI dashboard, then assemble the Week 1 starter kit. Week 1.
- Choose the right chart for the question
- Misleading scales, annotations and readable dashboards
- Assignment 1: Business Data Analysis Starter Kit
- Julius AI Conversational Analysis - Upload spreadsheets to Julius AI, ask clear questions, transform and group data, build charts and turn the results into a verified branch performance report. Week 2.
- Upload data to Julius AI and ask clear questions
- Transform, group and visualise data in Julius AI
- Build and verify a branch performance report
- KPI Analysis and Customer Segmentation - Separate revenue from profit, calculate AOV and percentage change, segment customers and rank products and regions to recommend actions the data supports. Week 2.
- Revenue, profit, AOV and percentage change
- Customer segments, repeat buyers and concentration
- Rank products and regions, then recommend actions
- Customer Feedback Analytics, Storytelling and Assignment 2 - Classify reviews by sentiment and topic (MonkeyLearn ideas, now in Medallia text analytics), check AI labels against human ones, turn findings into a manager-ready story and complete Assignment 2. Week 2.
- Sentiment and topic classification of customer reviews
- Check AI labels, fix misclassifications and find themes
- Turn numbers into a recommendation for managers
- Assignment 2: Business Insights and Customer Feedback Report
- Campaign Analytics with Akkio Concepts - Calculate CTR, conversion rate, CPL, CPA and ROAS, see how Akkio is used today by agencies for campaign data, and recommend a budget shift with stated assumptions. Week 3.
- Campaign funnel KPIs: CTR, conversion rate, CPL, CPA and ROAS
- Akkio today: AI for agency campaign data
- Compare campaigns and recommend a budget shift
- No-Code Prediction and Churn Blueprint - Learn targets, features, classification and regression, what no-code AutoML tools like the legacy Obviously AI did (now Zams), and design a churn prediction that avoids data leakage. Week 3.
- Prediction basics: target, features, classification and regression
- No-code AutoML: what Obviously AI taught and where it went
- Design a churn prediction and avoid data leakage
- Model Evaluation, Risk Dashboards and Assignment 3 - Split train and test data, compare with a baseline, build a confusion matrix, calculate accuracy, precision and recall, set thresholds, show risk groups on a dashboard and complete Assignment 3. Week 3.
- Train and test data, baselines and overfitting
- Confusion matrix, accuracy, precision and recall
- Thresholds, risk groups and a prediction dashboard
- Assignment 3: Predictive Analytics Kit
- DataRobot Basics: AutoML Experiments - Follow DataRobot's predictive workflow from data to leaderboard, compare models against a baseline with the right metric, and read feature impact, explainability, governance and monitoring. Week 4.
- DataRobot's AutoML workflow from data to leaderboard
- Compare models with a baseline and the right metric
- Feature impact, explainability and monitoring
- Sales Forecasting and What-If Analysis - Build simple forecast baselines, account for seasonality, calculate growth rates, compare best, base and worst scenarios and turn the forecast into an inventory decision. Week 4.
- Forecast baselines: naive, average and seasonality
- Growth rates and best, base and worst scenarios
- From forecast to an inventory decision
- Data Quality, Privacy, Freelance Pricing and Assignment 4 - Protect personal data under India's DPDP framework before uploading to AI tools, audit a flawed report for wrong totals, bad charts and false causality, price a freelance analytics project and complete Assignment 4. Week 4.
- Personal data, DPDP basics and safe AI uploads
- Audit a report: wrong totals, bad charts and false causality
- Freelance analytics pricing and Assignment 4 plan
- Capstone: AI-Powered BI Dashboard and Decision Support - Bring the course together for Navrang Textiles: requirements, data inventory, cleaning report, KPI definitions, an interactive dashboard, a predictive or feedback insight, recommendations, limitations, privacy checklist and a client presentation. Week 4.
- Capstone brief, data inventory and KPI plan
- Build the dashboard and the predictive or feedback insight
- Recommendations, limitations and client presentation
Frequently asked questions
How long is the AI for Data Analysis course?
It runs for 4 weeks, with 14 modules. It is self-paced online learning from EasySkill Career Academy, Surat.
How much does the AI for Data Analysis course cost?
The fee is Rs 15,999, or about Rs 2,666 a month on EMI.
What will I build in AI for Data Analysis?
Every module ends in a graded practical task that AI checks in about 30 seconds, so you finish with a portfolio you can show to employers or clients.
Which roles does AI for Data Analysis prepare me for?
It prepares you to apply for roles such as Junior Data Analyst, MIS Executive, Data Analyst, Reporting Analyst, Business Analyst Trainee. It does not guarantee a job or income.
Can I learn online, or do I have to visit a centre?
You can learn fully online. EasySkill also has centres in Adajan, Vesu and Bhestan, Surat, if you prefer to visit.
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