Data Analyst & Business Intelligence with AI Training in Ahmedabad
Learn to collect, clean, analyze and visualize business data using Excel, SQL, Python and Power BI. Develop interactive dashboards, automate reporting, identify business trends and use AI assistants to accelerate data analysis. Gain practical experience in data modeling, KPI tracking, business forecasting, analytical storytelling and AI-assisted decision support through real-world projects.
Skills & Technologies Covered
- Microsoft Excel
- SQL
- Power BI
- Python
- Pandas
- NumPy
- PostgreSQL
- Power Query
- DAX
- Tableau
Live Production Project & Internship Experience
Live Client Project:
Project: AI-Powered Business Intelligence & Sales Analytics Platform
Students will develop an end-to-end analytics solution for a simulated IT services company using realistic sales, customer, marketing, revenue and operational datasets.
Data preparation: Import datasets from Excel, CSV and PostgreSQL. Clean missing values, identify duplicates, validate records and transform data using Excel Power Query and Python Pandas.
Data analysis: Use SQL to analyze customer behavior, sales performance, revenue trends, lead conversion, customer retention and service profitability.
Business intelligence: Build interactive Power BI dashboards with data models, relationships, DAX measures, filters and drill-through reports.
AI integration: Use AI assistants to generate and validate SQL queries, explain trends, draft executive summaries, identify potential anomalies and suggest follow-up analysis. Verify every important finding against the underlying data.
Business forecasting: Create basic sales forecasts, compare actual performance with targets and analyze business scenarios.
Final deliverables: Cleaned datasets, SQL query portfolio, Python notebooks, Power BI dashboards, KPI dictionary, analytical report, AI-assisted executive summary and a final business presentation with actionable recommendations.
Detailed Course Curriculum & Modules
Module 1: Data Analytics & Business Intelligence Fundamentals
- • What data analytics and business intelligence mean.
- • Data analyst, BI analyst and data scientist career paths.
- • Structured, semi-structured and unstructured data.
- • Descriptive, diagnostic, predictive and prescriptive analytics.
- • Business questions, KPIs, metrics and performance targets.
- • Data sources, reporting workflows and decision-making.
- • Introduction to AI-assisted analytics and its limitations.
- • Defining business problems before selecting charts or tools.
Module 2: Advanced Microsoft Excel for Data Analysis
- • Data formatting, sorting, filtering and validation.
- • IF, SUMIFS, COUNTIFS and lookup functions.
- • XLOOKUP, INDEX/MATCH and dynamic arrays.
- • Text, date and logical functions.
- • PivotTables, PivotCharts and slicers.
- • Conditional formatting and KPI reports.
- • What-if analysis and basic forecasting.
- • AI-assisted formulas and verification of results.
Module 3: SQL for Data Analysts
- • Relational database concepts and table structures.
- • SELECT, WHERE, ORDER BY and DISTINCT.
- • Aggregate functions, GROUP BY and HAVING.
- • INNER, LEFT, RIGHT and other relevant joins.
- • Subqueries, common table expressions and CASE expressions.
- • Date calculations, string functions and NULL handling.
- • Window functions, ranking and running totals.
- • Views, indexes and query performance fundamentals.
- • Business analysis using sales, customer and revenue datasets.
- • AI-assisted SQL drafting, debugging and result validation.
Module 4: Data Cleaning & Transformation
- • Data quality, accuracy, completeness and consistency.
- • Identifying missing values and duplicate records.
- • Standardizing dates, categories and text fields.
- • Data type conversion and validation rules.
- • Excel Power Query fundamentals.
- • Combining files, merging tables and appending datasets.
- • ETL concepts: extract, transform and load.
- • Data reconciliation and maintaining transformation documentation.
- • AI-assisted data profiling and anomaly identification.
Module 5: Power BI Fundamentals & Data Modeling
- • Power BI Desktop and the reporting workflow.
- • Importing Excel, CSV and SQL data sources.
- • Power Query transformations.
- • Relationships, cardinality and filter direction.
- • Fact tables, dimension tables and star schemas.
- • Date tables and time intelligence foundations.
- • Data modeling best practices.
- • Choosing the right chart for a business question.
Module 6: DAX & Advanced Power BI Reporting
- • Calculated columns versus measures.
- • CALCULATE, FILTER and common aggregation functions.
- • Row context and filter context.
- • Time intelligence and period comparisons.
- • Year-over-year and month-over-month analysis.
- • KPI cards, drill-through, tooltips and bookmarks.
- • Interactive dashboards and report navigation.
- • Report performance, measure organization and validation.
- • Introduction to Power BI Service, publishing and sharing permissions.
Module 7: Data Visualization & Business Storytelling
- • Selecting charts based on analytical objectives.
- • Trend, comparison, distribution and relationship analysis.
- • Dashboard layout, hierarchy and visual consistency.
- • Avoiding misleading charts and inappropriate scales.
- • Designing executive dashboards.
- • Turning analytical findings into recommendations.
- • Communicating uncertainty and limitations.
- • Presenting business insights to non-technical stakeholders.
Module 8: Python for Data Analytics
- • Python syntax, variables, collections and functions.
- • Jupyter notebooks and development environments.
- • Reading CSV, Excel and JSON datasets.
- • Pandas Series and DataFrames.
- • Filtering, sorting, grouping and aggregating data.
- • NumPy arrays and numerical operations.
- • Merging datasets and handling missing values.
- • Reusable analytical scripts and visualizations.
- • AI-assisted Python coding and debugging.
Module 9: Statistics for Business Analytics
- • Mean, median, mode, variance and standard deviation.
- • Percentages, ratios, distributions and outliers.
- • Probability and sampling fundamentals.
- • Correlation versus causation.
- • Confidence intervals and hypothesis-testing concepts.
- • Business experiments and A/B testing fundamentals.
- • Interpreting statistical results responsibly.
- • Distinguishing evidence from assumptions in AI-generated explanations.
Module 10: AI Tools for Data Analysts
- • Using ChatGPT, Gemini, Claude and Microsoft Copilot for analysis.
- • Prompting for formulas, SQL, Python scripts and analytical plans.
- • Summarizing datasets and generating hypotheses.
- • AI-assisted data documentation and KPI definitions.
- • Drafting executive summaries and management reports.
- • AI-assisted chart planning and insight explanations.
- • Validating calculations against source data.
- • Protecting sensitive information and avoiding unsupported claims.
- • Reproducibility and documenting AI-assisted work.
Module 11: AI-Powered BI & Natural-Language Analytics
- • Asking business questions in natural language.
- • AI-assisted dashboard exploration and report summaries.
- • Power BI Copilot concepts and licensing requirements.
- • Tableau AI capabilities and supported workflows.
- • Natural-language-to-SQL concepts.
- • Grounding AI responses in approved business data.
- • Testing AI-generated queries and analytical answers.
- • Row-level security, permissions and controlled access.
- • Building a prototype business-data assistant.
Module 12: Forecasting & Predictive Analytics Fundamentals
- • Trend, seasonality and time-series concepts.
- • Moving averages and baseline forecasts.
- • Sales and demand forecasting exercises.
- • Regression fundamentals and feature relationships.
- • Training and testing data concepts.
- • Forecast accuracy measures such as MAE and RMSE.
- • Comparing forecasts with simple baselines.
- • Scenario analysis and uncertainty communication.
- • Knowing when a problem requires a specialist data scientist.
Module 13: Reporting Automation & Analytics Workflows
- • Refreshing reports and managing recurring datasets.
- • Automating repeatable tasks with Power Query and Python.
- • Scheduled refresh concepts and gateway requirements.
- • Generating recurring reports and executive summaries.
- • Data validation checks and exception reporting.
- • Connecting APIs and data sources where appropriate.
- • Version control for SQL scripts and Python notebooks.
- • Monitoring failed refreshes and documenting dependencies.
- • AI-assisted reporting workflows with human approval.
Module 14: Data Governance, Security & Analytics Quality
- • Data privacy, confidentiality and access control.
- • Data quality rules and validation.
- • Data dictionaries, KPI definitions and source documentation.
- • Personally identifiable information and responsible data handling.
- • Row-level security and report sharing permissions.
- • Bias, misleading visualizations and analytical errors.
- • AI-generated code and output verification.
- • Auditability, reproducibility and version control.
Who Should Join & Prerequisites
Prerequisites: Basic computer knowledge, Basic mathematics and logical reasoning, Basic Excel knowledge (helpful), Basic English reading comprehension, Willingness to learn data analysis and AI tools
Recommended For: Anyone Interested in Building AI-Powered Applications
Career Roles & Job Opportunities
- 💼 Data Analyst
- 💼 Business Intelligence Analyst
- 💼 Power BI Developer
- 💼 Reporting Analyst
- 💼 Junior Business Data Analyst
100% Placement Support & Practical IT Training in Ahmedabad
ExcelPTP Solutions (Amar Technolabs Pvt. Ltd.) provides practical 1-to-1 IT training until you secure your job in an IT company. We provide complete resume building, technical mock interview practice, technical viva preparation, and direct job interviews with hiring partner companies in Ahmedabad, Gandhinagar, and across Gujarat.
🏢 Campus Address: 4th Floor, Sunrise Avenue, Opp. Drive-In Cinema, Thaltej, Ahmedabad, Gujarat 380054
📞 Inquiries & Counseling: +91 94093 78394 / +91 91922 00000
✉️ Email: info@excelptp.com | 🌐 Website: https://www.excelptp.com
Still Have Questions?
Get clarity on course details, hands-on training, real-world projects, eligibility, fees, and career opportunities before you get started.
Why should I learn Data Analytics & Business Intelligence with AI?
Businesses use data to understand customers, measure performance and make better decisions. Learning data analytics, business intelligence and AI tools helps you turn raw data into meaningful insights, reports and interactive dashboards that support real business decisions.
Do I need advanced mathematics or programming knowledge to learn Data Analytics?
No advanced mathematics or programming knowledge is required to begin. You can start with Excel, basic statistics, data visualisation and SQL, then gradually learn Python and AI-assisted analytics as your skills develop.
Is Data Analytics a good career option for IT freshers?
Yes, data analytics can be a career path for freshers who enjoy working with numbers, solving problems and understanding business trends. Developing practical skills in Excel, SQL, Power BI and Python can help you prepare for entry-level data and reporting roles.
What career opportunities are available after completing this course?
You can prepare for roles such as Junior Data Analyst, Business Intelligence Analyst Trainee, Reporting Analyst, MIS Executive, Power BI Developer Trainee or Business Data Analyst. Opportunities depend on your practical skills, portfolio, communication ability and employer requirements.
Will AI replace Data Analyst and Business Intelligence jobs in the future?
AI is changing data analytics by automating repetitive tasks such as report generation, basic data cleaning and query writing. However, businesses still need people who can validate data, understand business requirements, interpret results and communicate useful insights. Learning AI tools alongside SQL, Power BI, data analysis and critical thinking can help you adapt to these changes.