Flagship project
Sales Forecasting with Machine Learning
Build a predictive ML model that forecasts sales, tracks KPIs, and provides actionable insights using Python and Scikit-learn.
- Python
- Machine Learning
- Forecasting
- Scikit-learn
Data Science Crash CourseFrom Zero to Advance
Data Science Crash Course – Master Python, Machine Learning, and AI with Real-World Projects in Just 8 Weeks
Data Science Crash Course course in AmritsarIntensive, accelerated data science training designed for speed without compromise. Small batches, daily hands-on labs, and placement assistance to get you job-ready fast.
8 / 10 WeeksAdvancedHybrid
8 / 10 Weeks
Duration
Weekday/Weekend
Mode
12th Pass/Any Grad
Eligibility
6-8 Students
Batch Size
Admissions open
Data Science Crash Course · 8 / 10 Weeks
Batch timings
- Morning
- Evening
- Weekend
- Live online
About the programme
Course overview
Course overviewtechcadd's Data Science Crash Course is an accelerated, project-driven program for individuals who need to acquire core data science skills and become job-ready in the shortest possible time. This is high-intensity upskilling for people who don't have months to spare but refuse to compromise on quality.
It focuses on the essentials that matter: Python programming, Statistics, and SQL for data manipulation and querying. You then rapidly advance to Machine Learning, Predictive Modeling, and Data Visualization using Matplotlib, Seaborn, and Plotly, building models on real business datasets from day one.
You finish with a portfolio of 4 completed projects, a production-grade capstone, and the interview confidence to land your first Data Scientist role or pivot into an AI-focused career. No fluff, no unnecessary theory—just the high-impact skills employers actually hire for.
The data revolution isn't coming—it's already here. Every industry, from healthcare to finance to e-commerce, is drowning in data but starving for insights. Companies don't just need analysts who can create charts; they need data scientists who can build predictive models, deploy AI systems, and translate complex findings into business decisions that drive revenue.
The problem? Traditional courses take months, sometimes years, and leave you with theoretical knowledge but zero practical experience. You learn algorithms in isolation, never understanding how they work in real business contexts. Meanwhile, job postings demand Python proficiency, SQL mastery, machine learning expertise, and MLOps deployment skills—all backed by a portfolio of real projects that prove you can deliver results.
techcadd's Data Science Crash Course is the solution for professionals who need to move fast without cutting corners. This isn't a watered-down introduction or a theoretical overview—it's an intensive, immersive sprint that takes you from fundamentals to production-grade AI systems in just 8 weeks. You'll work on real business datasets, build deployable ML models, and finish with a portfolio that speaks louder than any certificate.
Designed for career switchers, working professionals, and ambitious graduates, this crash course delivers maximum value in minimum time. Small batches of 6-8 students, daily mentor-led labs, and a curriculum engineered for rapid skill acquisition ensure you don't just learn data science—you become a practitioner.
Placement assistanceLive data science labsInternship certificate
- Duration
- 8 / 10 Weeks
- Level
- Advanced
- Mode
- Hybrid
- Batches
- Morning, evening & weekend
Module by module
The curriculum, module by module
Module by module curriculumThe Data Science Crash Course is a nested program. The 10-week Extended track includes every module from the 8-week Intensive track and adds more depth. No module is repeated; you continue building on the skills you've already learned at an accelerated pace.
- Module 01 of 04
Python, Statistics & SQL Fundamentals
The foundational knowledge every Data Scientist needs—how to query, clean, and prepare raw data for advanced analysis and modeling.
Topics covered
- Python programming
- Pandas
- NumPy
- Statistics
- Hypothesis Testing
- SQL queries
- joins
- aggregations
Tools in this module
PythonPandasNumPySciPySQLMySQLJupyterYou finish withA documented Python and SQL analysis on a real business dataset with statistical insights
- Module 02 of 04
Machine Learning & Data Visualization Capstone
Building predictive models and interactive visualizations that drive business decisions, plus career readiness for data science roles.
Topics covered
- Scikit-learn
- Regression
- Classification
- Clustering
- Model Evaluation
- Matplotlib
- Seaborn
- Plotly
- Data Storytelling
- Portfolio Development
- Resume Preparation
- Interview Readiness
Tools in this module
Scikit-learnMatplotlibSeabornPlotlyGitHubPortfolioResumeYou finish withA complete capstone project with a predictive ML model and interactive visualizations, and an interview-ready portfolio
- Module 03 of 04
Advanced Machine Learning & MLOps
Core data science engineering—building production-grade ML pipelines that train, evaluate, and deploy models into real business environments.
Topics covered
- Ensemble Methods
- XGBoost
- Hyperparameter Tuning
- Feature Engineering
- Model Deployment
- Docker
- Flask
- API Development
- CI/CD for ML
Tools in this module
Scikit-learnXGBoostLightGBMDockerFlaskFastAPIGitHub ActionsMLflowYou finish withA deployed ML model as an API with documentation and a complex feature engineering pipeline
- Module 04 of 04
AI Strategy & Career Readiness
Advanced AI techniques plus comprehensive career readiness for data science roles.
Topics covered
- Deep Learning (TensorFlow/Keras)
- NLP
- AI Strategy
- Portfolio Development
- Resume
- LinkedIn Profile
- Interview Readiness
- Data Science Certification Exam Preparation
Tools in this module
TensorFlowKerasNLTKGitHubPortfolioResumeMock interviewsYou finish withA deployed deep learning model or NLP project 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 Segmentation & Predictive Analytics
Analyze a real business dataset using clustering and classification algorithms to extract actionable insights and predict customer behavior.
- Python
- Clustering
- Classification
- Business Intelligence
End-to-End ML Pipeline with MLOps
Build an end-to-end ML pipeline that cleans, trains, evaluates, and deploys a model as an API using Docker and Flask.
- Python
- MLOps
- Docker
- Flask
- Deployment
Deep Learning & NLP Capstone
Complete an end-to-end deep learning project with NLP, model deployment, dashboard creation, and a business presentation.
- Deep Learning
- NLP
- TensorFlow
- Portfolio
- AI 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 only the most in-demand data science tools and techniques—no time wasted on outdated methods or theoretical fluff.
Sales Forecasting with Machine Learning
- 02
Real datasets and projects
Every lab runs on a real business dataset with real questions and real outcomes, not simplified demos or toy problems.
Customer Segmentation & Predictive Analytics
- 03
Mentor-reviewed deliverables
Each project ends with a working ML model or analysis reviewed by a mentor who works daily on live data science problems.
End-to-End ML Pipeline with MLOps
- 04
Business-first mindset from the start
Every model is built on a real-world business brief and measured through accuracy, business impact, and stakeholder communication.
Deep Learning & NLP Capstone
The honest pitch
Your weeks should build a portfolio, not just a certificate.
Your weeks should build a portfolio, not just a certificate.An accelerated course is worth the intensity only if you finish with real data science projects you can demonstrate and AI-powered business insights you can defend in an interview.
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 Crash Course
Why students choose techcadd
Why students choose techcaddWhat makes this crash course at techcadd different is its intense focus on building a real data science project portfolio in record time, 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 02 or 04.
- 03
Small batch sizes
6-8 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 Crash Course course
Who can join the Data Science Crash Course courseThis crash course is designed for ambitious individuals who need a rapid skill upgrade to achieve their career goals without putting their lives on hold.
- 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 / 10 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 / 10 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 Crash Course
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.
19tools covered
Programming language
- Python
Query language
- SQL
Data manipulation
- Pandas
Machine learning
- Scikit-learn
Deep learning
- TensorFlow
Containerization
- Docker
Version control
- Git
Code hosting
- GitHub
Certification
Get certified in Data Science Crash Course
Get certified in Data Science Crash CourseIssued 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 doesn't stand still, and neither does what it's 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 data scientists to integrate predictive models, 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 crash 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, Scikit-learn, TensorFlow | Usually limited to basic spreadsheets |
| Batch size | 6-8 students per batch | Often 25-40 students per batch |
| MLOps & Deployment | Live integration, real API deployment | Rarely covered |
| Advanced topics | Advanced ML, Deep Learning, NLP | Often only theory |
| Automation & Scripts | Automated ML pipelines, CI/CD for ML | Rarely covered |
| Portfolio | 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 |
| Time to job-ready | 8-10 weeks | 16-24 weeks or more |
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.
