Flagship project
Sales Forecasting with Machine Learning
Build a machine learning model that predicts sales revenue using Python and Scikit-learn.
- Python
- Scikit-learn
- Machine Learning
Data Science Immersive TrainingFrom Fundamentals
Data Science Immersive Training – Master Predictive Modeling and Build a Job-Ready AI Portfolio
Data Science Immersive Training course in AmritsarIntensive, hands-on data science training focused on real business data — small batches, daily labs, and placement assistance to get you job-ready.
8 / 12 WeeksAdvancedHybrid
8 / 12 Weeks
Duration
Weekday/Weekend
Mode
12th Pass/Any Grad
Eligibility
8-10 Students
Batch Size
Admissions open
Data Science Immersive Training · 8 / 12 Weeks
Batch timings
- Morning
- Evening
- Weekend
- Live online
About the programme
Course overview
Course overviewtechcadd's Data Science Immersive Training is a practical, project-based program for beginners and professionals who need to acquire core data science skills and advance to expert-level proficiency.
It focuses on the essentials: Python for programming, Statistics for analytics, and SQL for data extraction. You then move into Machine Learning and Artificial Intelligence, learning to build predictive models and deploy them on real datasets.
You finish with a portfolio of 3-4 completed projects, a capstone project, and the interview-readiness to land your first Data Scientist role or advance your current career.
In an era defined by artificial intelligence, data is the new oil, but machine learning is the engine that refines it. Companies are no longer just looking for analysts who can describe what happened; they are desperate for Data Scientists who can predict what will happen next. The challenge isn't gathering data; it's using predictive modeling to drive strategy. Data Science is the discipline that blends statistics, programming, and business acumen to build systems that learn from data—recommendation engines, churn prediction models, and automated decision-making tools. Organizations urgently need professionals who can navigate unstructured data, build AI solutions, and communicate complex findings to stakeholders. The most valuable skill isn't just coding—it's the ability to build machine learning solutions that deliver immediate business ROI.
techcadd's Data Science Immersive Training is a comprehensive, structured program built on the essential tools you see in every Data Scientist job description—Python, SQL, Machine Learning, and AI. This is not a theory-heavy course; it's a hands-on journey where you work with real datasets and solve real business problems from day one. You'll finish with a portfolio of real projects, not just a certificate. This course is designed for deep upskilling, delivered in a focused 12-week format with small batches of 8-10 students and daily mentor-led labs.
Placement assistanceLive data science labsInternship certificate
- Duration
- 8 / 12 Weeks
- Level
- Advanced
- Mode
- Hybrid
- Batches
- Morning, evening & weekend
Module by module
The curriculum, module by module
Module by module curriculumThe Data Science Immersive Training is a nested program. The 12-week Advanced track includes every module from the 8-week Foundation track and adds more depth. No module is repeated; you continue building on the skills you have already learned.
- Module 01 of 04
Python, Statistics & SQL Fundamentals
The foundational knowledge every Data Scientist needs — how to query, clean, and prepare raw data for machine learning.
Topics covered
- Python programming
- Pandas
- NumPy
- descriptive statistics
- hypothesis testing
- SQL queries
- joins
- aggregations
Tools in this module
PythonPandasNumPySQLJupyterGoogle ColabYou finish withA documented SQL and Python analysis on a real business dataset
- Module 02 of 04
Exploratory Data Analysis & Visualization
Building visual narratives and preparing data for machine learning models.
Topics covered
- Matplotlib
- Seaborn
- Plotly
- data visualization
- feature engineering
- outlier detection
- problem framing
Tools in this module
PythonMatplotlibSeabornPlotlyGitHubYou finish withA complete EDA project with visualizations and a report
- Module 03 of 04
Machine Learning & Predictive Modeling
Core machine learning algorithms — building predictive models that solve real business problems.
Topics covered
- Scikit-learn
- regression
- classification
- clustering
- model evaluation
- hyperparameter tuning
- cross-validation
Tools in this module
PythonScikit-learnPandasNumPyJupyterYou finish withAn end-to-end machine learning pipeline deployed on a real dataset
- Module 04 of 04
AI, Deployment & Career Readiness
Advanced AI deployment and comprehensive career readiness for data science roles.
Topics covered
- Model deployment
- Flask/FastAPI
- cloud basics
- portfolio development
- resume
- LinkedIn profile
- interview readiness
- AI certification exam preparation
Tools in this module
PythonFlaskAWS/GCPGitHubPortfolioResumeMock interviewsYou finish withA fully deployed AI model for a real business scenario and an interview-ready data science portfolio and resume
What you build
Projects that answer the interview question for you
Portfolio projectsEach project is reviewed individually, and the review notes go into your portfolio documentation.
Customer Churn Prediction
Analyze a real business dataset using classification algorithms to predict customer churn and extract actionable insights.
- Machine Learning
- Classification
- Business Intelligence
AI Data Pipeline Project
Build an end-to-end AI pipeline that cleans, analyses, and visualises a real dataset.
- Python
- Pandas
- AI
- Data Visualization
Expert Capstone Project
Complete an end-to-end data science project with data cleaning, machine learning, AI deployment, dashboard creation, and a business presentation.
- Multi-Domain
- Portfolio
- Data Science Strategy
The working loop
Learn it. Build it. Make it yours.
Learn it. Build it. Make it yours.Every project moves through the same loop: understand the brief, build it with guidance, then explain the decisions behind your work. The certificate is the receipt — the portfolio is the point.
- 01
Hyper-focused curriculum
You learn the most in-demand data science tools and techniques, focusing on the skills that deliver results.
Sales Forecasting with Machine Learning
- 02
Real datasets and projects
Every lab runs on a real business dataset with real questions and outcomes, not a simplified demo.
Customer Churn Prediction
- 03
Mentor-reviewed deliverables
Each project ends with a working machine learning model or analysis reviewed by a mentor who works daily on live data science problems.
AI Data Pipeline Project
- 04
Real-world business briefs from the start
Every analysis is built on a real-world business brief and measured through accuracy, business impact, and stakeholder communication.
Expert Capstone Project
The honest pitch
Your weeks should build a portfolio, not just a certificate.
Your weeks should build a portfolio, not just a certificate.A comprehensive course is worth the time only if you finish with real data science projects you can show and predictive insights you can defend.
Project completion certificate
Issued for the project you build and defend
Course completion certificate
Carries a certificate ID and a scan-to-verify code
Why choose Data Science Immersive Training
Why students choose techcadd
Why students choose techcaddWhat makes this course at techcadd different is its intense focus on building a real data science project portfolio, ensuring you finish with demonstrable skills rather than just disconnected certificates.
- 01
Mentors who still work on live data science systems
Not full-time lecturers — trainers who are still doing the job they teach.
- 02
One project, extended through every module
You start the project in Module 01, extend it through every module, and complete the capstone by Module 04.
- 03
Small batch sizes
8–10 students per batch ensures every student gets personalised feedback on every deliverable.
- 04
Nested, no-repeat curriculum
The longer program includes everything from the shorter track and adds more depth — you never repeat a module.
- 05
Documented internship experience
Every student receives a documented internship certificate alongside their course certificate.
- 06
Placement support
Resume and LinkedIn preparation, interview coaching, and active placement assistance for graduates.
Eligibility
Who can join the Data Science Immersive Training course
Who can join the Data Science Immersive Training courseThis course is designed for individuals who need a practical skill upgrade to meet their career goals.
- Educational backgroundNo specific degree or stream required — school leavers, graduates and career switchers all qualify.
- Prior experienceNone required. The programme opens at advanced level and builds from there across 8 / 12 weeks.
- AgeNo upper age limit. What matters is being able to commit to the full course rather than dropping off midway.
- EquipmentA laptop or desktop with a stable internet connection for classroom or live-online batches.
- Time commitmentAble to attend regularly across 8 / 12 weeks, in a morning, evening or weekend batch.
Where it leads
The roles this portfolio opens
Job titles vary by company, but the underlying expectations do not. Each destination below maps to work you will have already done during the programme.
Tools you will actually use
The working stack for Data Science Immersive Training
Tools and technologies coveredEvery tool below is installed, configured and used by you during the course — not demonstrated on a slide. You leave able to set up your own environment from scratch.
14tools covered
Programming language
- Python
Query language
- SQL
Data manipulation
- Pandas
Machine learning
- Scikit-learn
Deep learning
- TensorFlow
Business intelligence
- Power BI
Data visualization
- Tableau
Version control
- Git
Code hosting
- GitHub
Certification
Get certified in Data Science Immersive Training
Get certified in Data Science Immersive TrainingIssued on completion against the modules you finished and the projects you submitted — plus an internship letter where the industrial training track applies. Shareable to LinkedIn, and verifiable by an employer who calls the Amritsar desk.
- Module-wise assessment, not attendance-based
- Project submissions logged against your certificate
- Internship letter on the industrial training track
- Verifiable directly with the campus office
Future scope
Where this takes you over the next five years
Where this takes you over the next five yearsThe skill does not stand still, and neither does what it is worth.
- Data Science is the new baselineEmployers increasingly assume business knowledge will extend to Python, ML, AI, and data storytelling.
- Generative AI and LLMs are the new layersTeams expect scientists to integrate Generative AI, LLMs, and automation, not just build static reports.
The objective is practical, job-ready knowledge rather than memorised concepts.
Due diligence
How techcadd compares
How techcadd comparesWhat actually differs between this course at techcadd and a typical alternative.
| What you should ask | techcadd Amritsar | Other institutes |
|---|---|---|
| Live project work | Every module, from week one | Varies, often only at the end |
| Learning format | One project extended through every module | Topics demonstrated separately, rarely combined |
| Tools used | Python, SQL, ML, AI, Power BI, Tableau | Usually limited to basic spreadsheets |
| Batch size | 8–10 students per batch | Often 25–40 students per batch |
| Data Science & Deployment | Live integration, real AI models | Rarely covered |
| Advanced topics | Advanced ML, AI, Dashboards, TensorFlow | Often only theory |
| Automation & Scripts | Automated ML pipelines, dashboard alerts | Rarely covered |
| Portfolio | 3-4 documented project deliverables | Often just assignments, not portfolio-ready work |
| Certificate | Completion certificate + documented internship | Only a completion certificate |
| Placement support | Resume, LinkedIn, interview coaching, active placement | Generic referrals, no dedicated coaching |
Every comparison here is about substance, not marketing language.
Before you enrol
Questions we get asked at the admissions desk
Frequently asked questionsIf something here is not covered, the Amritsar desk will answer it directly — no call-back queue.
No. The programme opens at advanced level and assumes no background beyond comfort with a computer. Students who already have some exposure move through the early modules faster and spend the saved time on project work.
