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 for Beginners CourseZero to Data Pro
Data Science for Beginners Course – Learn Python, Statistics, and Machine Learning from Scratch to Build Your First Data Science Portfolio
Data Science for Beginners Course course in AmritsarA beginner-friendly, hands-on data science course focused on real business data — small batches, daily labs, and placement assistance to help you land your first Data Science role.
16 / 20 WeeksBeginnerHybrid
16 / 20 Weeks
Duration
Weekday/Weekend
Mode
12th Pass/Any Grad
Eligibility
8-10 Students
Batch Size
Admissions open
Data Science for Beginners Course · 16 / 20 Weeks
Batch timings
- Morning
- Evening
- Weekend
- Live online
About the programme
Course overview
Course overviewtechcadd's Data Science for Beginners Course is a practical, project-based program designed specifically for individuals with no prior data science or programming experience. Whether you're a student, a working professional looking to switch careers, or someone simply curious about data, this course provides a structured, supportive path to mastering the fundamentals.
You begin with Python programming, statistics, and SQL — the three pillars every data scientist needs. From there, you progress to machine learning, predictive modeling, and data visualization using tools like Pandas, Scikit-learn, Matplotlib, Seaborn, and Plotly. Every concept is reinforced through hands-on labs on real business datasets, ensuring you learn by doing.
You finish with a portfolio of 4-5 completed projects, a capstone project that demonstrates your end-to-end data science skills, and the interview-readiness to confidently apply for your first Data Scientist role or advance your current career.
Every expert was once a beginner. The difference? They started. In today's data-driven world, the demand for professionals who can transform raw data into meaningful insights has never been higher. Companies across every industry are searching for individuals who can analyze trends, build predictive models, and communicate data stories that drive real business decisions. The Data Science for Beginners Course at techcadd is your gateway into this exciting field—no prior coding experience required. We believe that anyone with curiosity and dedication can learn data science. That's why we've designed this program specifically for absolute beginners, breaking down complex concepts into digestible, practical lessons. You'll start with the basics of Python programming and gradually progress to building machine learning models and deploying them into real-world applications. This isn't just another online tutorial—it's a comprehensive, mentor-led journey with small batch sizes, daily hands-on labs, and a supportive learning environment. By the end, you'll have a portfolio of 4-5 real projects, the confidence to tackle data science challenges, and the skills employers are actively looking for. Your journey from beginner to job-ready Data Scientist starts here.
techcadd's Data Science for Beginners Course is built on the core pillars every beginner needs—Python, SQL, Statistics, Machine Learning, and Data Visualization. This is not a theory-heavy course; it's a hands-on, project-driven experience where you work with real datasets and solve real business problems from day one. You'll finish with a portfolio of real AI projects, not just a certificate. Delivered in a focused 16-20 week format with small batches of 8-10 students and daily mentor-led labs, this course is designed for deep, immersive upskilling.
Placement assistanceLive data science labsInternship certificate
- Duration
- 16 / 20 Weeks
- Level
- Beginner
- Mode
- Hybrid
- Batches
- Morning, evening & weekend
Module by module
The curriculum, module by module
Module by module curriculumThe Data Science for Beginners Course is a nested program. The 20-week Advanced track includes every module from the 16-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 & Data Fundamentals
The foundational knowledge every Data Scientist needs — how to query, clean, and prepare raw data for analysis and modeling. Designed for absolute beginners, this module assumes no prior coding experience.
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
Big Data, AI Strategy & Career Readiness
Advanced AI and Big Data techniques plus comprehensive career readiness for data science roles.
Topics covered
- Big Data (PySpark)
- Deep Learning (TensorFlow/Keras)
- NLP
- AI Strategy
- Portfolio Development
- Resume
- LinkedIn Profile
- Interview Readiness
- Data Science Certification Exam Preparation
Tools in this module
PySparkTensorFlowKerasNLTKGitHubPortfolioResumeMock interviewsYou finish withA fully deployed deep learning model or big data pipeline 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
Beginner-focused curriculum
You learn the most in-demand data science tools and techniques, with every concept explained from scratch — no jargon, no assumed knowledge.
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 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
Real-world business briefs 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.A beginner's course is worth the time only if you finish with real data science projects you can showcase and the confidence to solve real business problems with data.
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 for Beginners Course
Why students choose techcadd
Why students choose techcaddWhat makes this beginner's 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 for Beginners Course course
Who can join the Data Science for Beginners Course courseThis course is designed for individuals who are new to data science and want a practical, structured path to build their skills and career.
- 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 16 / 20 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 16 / 20 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 for Beginners 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.
20tools covered
Programming language
- Python
Query language
- SQL
Data manipulation
- Pandas
Machine learning
- Scikit-learn
Deep learning
- TensorFlow
Big data processing
- PySpark
Containerization
- Docker
Version control
- Git
Code hosting
- GitHub
Certification
Get certified in Data Science for Beginners Course
Get certified in Data Science for Beginners 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, 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 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, PySpark | Usually limited to basic spreadsheets |
| Batch size | 8–10 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 | 4-5 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.
