Become a job-ready Data Analyst with an automotive customer intelligence project
Learn Excel, SQL, Python, Power BI, Tableau, statistics, customer segmentation, churn prediction and business storytelling through a practical automotive analytics project based on EV readiness, hybrid demand, brand loyalty, connected car features and service trust.
What you will build
- Automotive customer segmentation dashboard
- EV readiness and hybrid preference analysis
- Brand loyalty and churn risk analysis
- Connected car feature demand dashboard
- Service trust and customer satisfaction insights
- Final portfolio project with interview explanation
Learn data analytics through a real business-style automotive project
Data Analyst interviews are not only about tools. Companies want candidates who can understand business problems, clean data, write SQL, build dashboards, find insights and explain recommendations. This course trains you using one complete automotive customer intelligence project from problem to presentation.
Customer Problem
Automotive customers are confused about EVs, hybrids, affordability, brand trust, connected features, service quality and long-term vehicle value. Companies need data analysts who can convert customer behavior into business decisions.
Project Scope
You will learn to analyze customer survey data, vehicle preference, EV readiness, brand switching, service trust, connected car feature demand and customer retention opportunities.
Business Outcome
The project helps automotive companies recommend the right vehicle, improve customer conversion, reduce churn, personalize offers, improve service transparency and build executive dashboards.
Skills covered in this Data Analyst Course
The skill flow starts from business understanding and moves into data cleaning, SQL, Excel, Python, visualization, dashboards, statistics, ML basics, reporting and interview storytelling.
Research Skills
Study automotive consumer trends, EV adoption barriers, hybrid demand, connected vehicle concerns and service expectations.
Business Metrics Definition
Define KPIs such as EV readiness score, lead conversion, brand loyalty, churn risk, service satisfaction and connected feature adoption.
Analytical Skills
Break customer behavior into measurable patterns around affordability, trust, vehicle choice, financing and service quality.
Critical Thinking
Interpret whether a customer should be targeted for EV, hybrid, petrol, diesel, used vehicle or service-retention campaigns.
Problem Solving
Solve automotive business problems such as low EV conversion, weak brand loyalty, poor service trust and dealer inefficiency.
Communication Skills
Explain analytical findings clearly to business teams, product teams, dealerships, finance teams and leadership.
Data Storytelling
Convert charts and numbers into a business story that explains what customers want and what the company should do next.
Stakeholder Presentation
Present insights to non-technical audiences using simple language, business logic and clear recommendations.
Data Handling
Work with customer survey data, CRM data, dealer records, service history, vehicle catalog data and connected vehicle signals.
Data Cleaning
Fix missing values, duplicates, inconsistent vehicle names, wrong customer categories and invalid survey responses.
Data Validation
Check whether customer, vehicle, service and pricing data are accurate before analysis and reporting.
Data Aggregation
Combine customer data from dealerships, website leads, service centers, surveys and connected car platforms.
Data Transformation
Convert raw data into features such as EV readiness, price sensitivity, service satisfaction and loyalty score.
Exploratory Data Analysis
Find early patterns in customer behavior, vehicle preferences, financing interest and service expectations.
Trend and Pattern Identification
Identify which customer groups prefer EVs, hybrids, connected features, safety features and trusted service networks.
Data Interpretation
Explain why a customer may switch brands, avoid EVs, delay purchase, reject connected features or trust a service provider.
Basic Statistics
Use averages, percentages, distributions, correlation and comparison to summarize consumer behavior.
Statistical Analysis
Analyze relationships between income, vehicle preference, service trust, price sensitivity and purchase intent.
Forecasting
Predict future demand for EVs, hybrids, connected services, service revenue and customer retention campaigns.
SQL
Query customer, vehicle, sales, service, dealer and finance data from databases.
Advanced SQL
Write joins, window functions, aggregations, customer segmentation queries and dashboard-ready datasets.
Relational Database Management
Understand how customer, vehicle, transaction, service and dealership tables connect with each other.
Excel
Perform quick analysis, pivot tables, lookup logic, business calculations and management-friendly reports.
Advanced Excel
Build advanced dashboards, customer scoring models, scenario analysis and finance comparison sheets.
Power Query
Clean, merge and transform business data before loading it into Power BI.
Power BI
Build executive dashboards for EV adoption, customer segments, brand loyalty, churn risk and service trust.
DAX
Create measures for conversion rate, EV readiness, loyalty score, service satisfaction and revenue opportunity.
Tableau
Create interactive visual dashboards for customer preference, geography, vehicle type and service behavior.
Data Visualization
Create charts that explain customer demand, buying barriers, pricing sensitivity and service expectations.
Reporting
Prepare weekly and monthly insight reports for automotive leadership and dealership managers.
Dashboard Development
Create business dashboards for OEMs, dealerships, service providers and finance teams.
Python
Use Python for data cleaning, analysis, visualization, feature engineering and model preparation.
Pandas and NumPy
Clean, transform, aggregate and analyze large customer and vehicle datasets.
Matplotlib
Create custom charts for customer analysis, trend reporting and model explanation.
Machine Learning Basics
Understand how predictive models are built for churn, purchase intent and EV readiness scoring.
Scikit-learn
Build baseline models for customer segmentation, churn prediction and vehicle recommendation.
XGBoost
Build stronger predictive models for customer conversion and brand switching risk.
Customer Segmentation
Group customers into EV-ready, hybrid-ready, price-sensitive, service-focused and brand-loyal segments.
Churn Prediction
Predict which customers may switch to another automotive brand or service provider.
Recommendation Logic
Recommend vehicle type, service package, finance option or connected feature based on customer profile.
Natural Language Processing
Analyze customer reviews, complaints, survey comments, service notes and dealer feedback.
spaCy
Extract vehicle names, complaint categories, service issues and customer sentiment from text data.
API Integration
Understand how dashboards and analytics systems connect with CRM, dealer systems and service platforms.
FastAPI Basics
Expose analytics outputs such as EV readiness score or churn risk through simple APIs.
AWS Glue Basics
Understand how cloud ETL pipelines move automotive data from source systems to analytics layers.
AWS Lambda Basics
Understand event-based scoring and automated alerts for customer actions and service triggers.
Git
Track project code, notebooks, dashboard files and analytics documentation.
Documentation
Document datasets, assumptions, formulas, dashboard definitions, model logic and business recommendations.
Collaboration
Work with product, marketing, dealership, finance, service and technology teams.
Time Management
Complete project phases on time: research, data cleaning, SQL, Python, dashboarding, ML and presentation.
Course roadmap
The roadmap is designed to take you from raw data to business-ready dashboards, portfolio proof and interview confidence.
Program focus areas
By the end of the course you should be able to
- Explain an automotive customer intelligence project from business problem to dashboard and recommendation.
- Clean and analyze customer, vehicle, dealer, survey and service datasets using Excel, SQL and Python.
- Build Power BI and Tableau dashboards for EV readiness, brand loyalty, churn risk and service trust.
- Use statistics and EDA to find customer behavior patterns and market opportunities.
- Build basic ML models for customer segmentation, churn prediction and recommendation logic.
- Convert the project into resume bullets, portfolio proof, interview answers and presentation stories.
Automotive Customer Problem and Analytics Thinking
Understand EV adoption barriers, hybrid demand, brand loyalty, connected car concerns, service trust and how data analysts convert these problems into measurable business questions.
Excel, SQL, Data Cleaning and Data Validation
Learn how to clean customer, vehicle, dealer, survey and service datasets. Build SQL queries, joins, aggregations and business-ready datasets.
EDA, Statistics, Customer Segmentation and Insight Discovery
Use Python, Excel and statistics to identify patterns in vehicle preference, affordability, EV readiness, connected feature demand and service satisfaction.
Power BI, Tableau, DAX and Executive Dashboarding
Build dashboards for leadership showing EV readiness, customer segments, brand switching, service trust, churn risk and revenue opportunities.
ML Basics, Churn Prediction, Recommendation and NLP
Learn how predictive analytics can support customer retention, vehicle recommendation, service alerts and review analysis.
Project Presentation, Resume Proof and Mock Interviews
Convert the full project into resume bullets, dashboard walkthroughs, business case explanation and interview-ready answers.
Portfolio project: Automotive Consumer Intelligence Dashboard
This is the central project you will use for learning, portfolio building, resume proof and interviews.
Dataset and Analysis
Customer preferences, vehicle type interest, EV readiness, price sensitivity, brand switching, connected feature demand, service trust and satisfaction patterns.
Dashboard Output
Power BI or Tableau dashboard showing customer segments, vehicle preference, loyalty risk, EV adoption opportunity and service improvement areas.
Business Recommendation
Final presentation explaining which customers to target, which offers to personalize, how to reduce churn and how to improve service trust.
Target roles after preparation
This course is designed for candidates targeting analytics roles where business thinking, dashboarding, SQL, Excel, Python and storytelling are important.
Support included
Tool Training
Learn Excel, SQL, Python, Power BI, Tableau, statistics and dashboarding through practical business tasks.
Portfolio Project
Build an automotive customer intelligence project that can be shown in resume, LinkedIn and interviews.
Interview Preparation
Practice Data Analyst interview questions around SQL, Excel, dashboards, statistics, business insights and project explanation.
Resume Positioning
Convert your project work into strong resume bullets, LinkedIn project description and interview stories.
Data Analyst Course with portfolio project
Fee per candidate
- EMI facility available
- Excel, SQL, Python, Power BI and Tableau training
- Automotive customer intelligence portfolio project
- Dashboard building and business storytelling
- Resume and interview preparation support
- Mock interview and project explanation practice
What is included in this fee
- Live practical learning on Data Analyst tools
- Automotive customer intelligence project
- Excel, SQL, Python, Power BI and Tableau practice
- Statistics, EDA, segmentation and dashboarding
- Resume, LinkedIn and portfolio positioning
- Data Analyst interview questions and mock interviews
Mentor profile
Devanshu Shukla
Founder, The Talent Grid | Enterprise AI and Data Analytics Mentor
The focus of this course is to help candidates learn data analytics through a real business-style project and convert that learning into portfolio proof, interview confidence and job-ready communication.
Core guidance
- Business problem framing for data analysts
- SQL, Excel, Python and dashboarding practice
- Portfolio project development
- Interview answer structuring
Candidate outcome
- Stronger Data Analyst project explanation
- Better resume and LinkedIn positioning
- Confidence in SQL, dashboards and analytics interviews
- Portfolio-ready automotive analytics project
Ready to become job-ready for Data Analyst roles?
Join the program and build a complete automotive customer intelligence project using Excel, SQL, Python, Power BI, Tableau, statistics and business storytelling.
Frequently asked questions
Who should join this program?
Students, freshers and working professionals who want to become job-ready for Data Analyst, Business Analyst, Power BI Analyst, BI Analyst, Customer Analytics Analyst or Product Data Analyst roles.
What is the project used in this course?
The course uses an automotive consumer intelligence project where you analyze customer preferences around EVs, hybrids, brand loyalty, connected car features, service trust and purchase behavior.
What is the program fee?
The total fee is ₹39,999/- per candidate. EMI facility is available.
Do I need coding experience?
No advanced coding experience is required. You will learn practical Python, SQL, Excel and dashboarding step by step.
Will I build a portfolio project?
Yes. You will build an end-to-end automotive analytics project with datasets, SQL analysis, Python notebooks, Power BI or Tableau dashboard and final business presentation.
Will this help in interviews?
Yes. The program includes resume positioning, project explanation, data analyst interview questions, dashboard walkthrough practice and mock interviews.
Talk to the team
For admission help, role fit questions or course clarity, connect directly with The Talent Grid team.
- +91-9315027516
- support@thetalentgrid.in
- Admission link: https://rzp.io/rzp/uB3dtCsn