Flagship project
Sales Forecasting with Machine Learning
Build a predictive model that forecasts future sales using Python and Scikit-learn.
- Python
- Scikit-learn
- Machine Learning
- Forecasting
Data Science Launchpad CourseFrom Fundamentals
Data Science Launchpad Course – Master the Core Skills and Build a Predictive Portfolio
Data Science Launchpad Course 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 / 16 WeeksBeginner to advancedHybrid
8 / 16 Weeks
Duration
Weekday/Weekend
Mode
Any Grad/Post Grad
Eligibility
8-10 Students
Batch Size
Admissions open
Data Science Launchpad Course · 8 / 16 Weeks
Batch timings
- Morning
- Evening
- Weekend
- Live online
About the programme
Course overview
Course overviewtechcadd's Data Science Launchpad Course 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 data analysis, SQL for data extraction, and Statistics for informed decision-making. You then move into Machine Learning, learning to build, evaluate, and deploy predictive models 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 a world driven by data, the ability to predict the future is a superpower. Companies are sitting on mountains of data, from customer behaviour to market trends, but they are starving for the foresight to act on it. The challenge isn't having data; it's asking the right questions and building models that provide answers. Data Science is the engine that transforms raw data into predictive insights and strategic foresight—forecasting sales, identifying at-risk customers, and uncovering hidden opportunities. Organisations desperately need professionals who can not only analyse data but also build machine learning models that solve complex business problems. The most valuable skill isn't just coding—it's the ability to build data science solutions that deliver tangible business impact.
techcadd's Data Science Launchpad Course is a comprehensive, structured program built on the core pillars of the Data Science profession—Python, Statistics, SQL, and Machine Learning. This is not a theory-heavy lecture series; 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-world projects, not just a certificate. This course is designed for deep upskilling, delivered in a focused 16-week format with small batches of 8-10 students and daily mentor-led labs.
Placement assistanceLive data science labsInternship certificate
- Duration
- 8 / 16 Weeks
- Level
- Beginner to advanced
- Mode
- Hybrid
- Batches
- Morning, evening & weekend
Module by module
The curriculum, module by module
Module by module curriculumThe Data Science Launchpad Course is a nested program. The 16-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, SQL & Statistical Foundations
The foundational knowledge every Data Scientist needs — how to extract, clean, and prepare raw data for analysis and how to apply statistical methods to understand it.
Topics covered
- Python programming
- Pandas
- NumPy
- SQL queries
- joins
- aggregations
- descriptive statistics
- hypothesis testing
- data cleaning
Tools in this module
PythonPandasNumPySQLMySQLJupyterGoogle SheetsYou finish withA documented SQL and Python analysis on a real business dataset with a statistical summary.
- Module 02 of 04
Data Visualization & Machine Learning Capstone
Building compelling visualizations that tell a story and creating a complete machine learning project from scratch, plus career readiness for data science roles.
Topics covered
- Matplotlib
- Seaborn
- data visualization
- basic machine learning
- problem framing
- portfolio development
- resume preparation
- interview readiness
Tools in this module
PythonMatplotlibSeabornScikit-learnGitHubPortfolioResumeYou finish withA complete capstone project with a predictive model and an interview-ready portfolio.
- Module 03 of 04
Machine Learning & Advanced SQL
Core machine learning programming — building, training, and evaluating predictive models on real business data, and taking your SQL skills to an advanced level.
Topics covered
- Scikit-learn
- regression
- classification
- clustering
- model evaluation
- feature engineering
- advanced SQL
- window functions
- CTEs
Tools in this module
PythonScikit-learnPandasJupyterSQLYou finish withAn end-to-end machine learning pipeline with a deployed model on a real dataset and a complex SQL analysis.
- Module 04 of 04
Model Deployment, Data Storytelling & Career Readiness
Deploying machine learning models into production and comprehensive career readiness for data science roles.
Topics covered
- Model deployment (Flask/Streamlit)
- data storytelling
- portfolio development
- resume
- LinkedIn profile
- interview readiness
- data science certification exam preparation
Tools in this module
Flask/StreamlitGitHubPortfolioResumeMock interviewsYou finish withA fully deployed machine learning 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.
SQL Data Analysis Project
Analyse a real business dataset using advanced SQL queries, joins, and window functions to extract actionable insights.
- SQL
- Data Analysis
- Business Intelligence
Customer Churn Prediction Model
Build an end-to-end Python data pipeline that cleans, analyses, and builds a classification model to predict customer churn.
- Python
- Pandas
- Scikit-learn
- Classification
Expert Capstone Project
Complete an end-to-end data science project with data cleaning, statistical analysis, machine learning model, 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.
SQL Data Analysis Project
- 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.
Customer Churn Prediction Model
- 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 Launchpad Course
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 02 or 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 Launchpad Course course
Who can join the Data Science Launchpad Course 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 beginner level and builds from there across 8 / 16 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 / 16 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 Launchpad 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.
13tools covered
Programming language
- Python
Query language
- SQL
Data manipulation
- Pandas
Machine learning
- Scikit-learn
Data visualization
- Matplotlib
Web framework
- Flask
Version control
- Git
Code hosting
- GitHub
Certification
Get certified in Data Science Launchpad Course
Get certified in Data Science Launchpad 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 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, SQL, machine learning, and data storytelling.
- MLOps and Generative AI are the new layersTeams expect data scientists to integrate predictive models, forecasting, 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, Scikit-learn, Matplotlib | Usually limited to basic spreadsheets |
| Batch size | 8–10 students per batch | Often 25–40 students per batch |
| Model Deployment | Live integration, real predictive models | Rarely covered |
| Advanced topics | Advanced SQL, Machine Learning, Model Deployment | Often only theory |
| Automation & Scripts | Automated data pipelines, model deployment | 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 beginner 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.
