Flagship project
Predictive Sales Forecasting with Machine Learning
Build a regression model that predicts future sales based on historical data using Python, scikit-learn, and SQL.
- Python
- scikit-learn
- Regression
- Forecasting
Applied Data Science CourseFrom Fundamentals
Applied Data Science Course – Master Machine Learning and Build a Job-Ready Portfolio
Applied Data Science Course course in AmritsarIntensive, hands-on data science training focused on building real-world applications — small batches, daily labs, and placement assistance to get you job-ready.
12 / 16 WeeksBeginner to advancedHybrid
12 / 16 Weeks
Duration
Weekday/Weekend
Mode
12th Pass/Any Grad
Eligibility
8-10 Students
Batch Size
Admissions open
Applied Data Science Course · 12 / 16 Weeks
Batch timings
- Morning
- Evening
- Weekend
- Live online
About the programme
Course overview
Course overviewtechcadd's Applied Data Science 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 programming, SQL for data manipulation, and statistics for foundational understanding. You then move into the core of machine learning, learning to build, evaluate, and deploy predictive models using Scikit-learn and cloud platforms.
You finish with a portfolio of 4-5 completed projects, a full-stack capstone project, and the interview-readiness to land your first Data Scientist or Machine Learning Engineer role.
In a world driven by data, the ability to predict outcomes, automate decisions, and uncover hidden patterns is nothing short of a superpower. Businesses are no longer just asking "what happened?"—they're demanding to know "what will happen next?" and "how can we automate our response?". The challenge isn't just analysing data; it's building intelligent systems that use Machine Learning and Artificial Intelligence to create tangible business value. Data Science is the engine that transforms raw data into predictive models, automated insights, and data-driven products. Organisations are desperate for professionals who can not only build these models but also deploy them into real-world applications.
techcadd's Applied Data Science Course is a comprehensive, project-driven program built on the core pillars of modern data science—Python, Machine Learning, SQL, and Model Deployment. This is not a theory-heavy course; it's a hands-on journey where you work on real-world datasets and solve complex business problems from day one.
You will finish with a portfolio of deployed machine learning projects, not just a certificate. This course is designed for deep upskilling, delivered in a focused 12-16 week format with small batches of 8-10 students and daily mentor-led labs.
Placement assistanceLive data science labsInternship certificate
- Duration
- 12 / 16 Weeks
- Level
- Beginner to advanced
- Mode
- Hybrid
- Batches
- Morning, evening & weekend
Module by module
The curriculum, module by module
Module by module curriculumThe Applied Data Science Course is a nested program. The 16-week Advanced track includes every module from the 12-week Foundation track and adds more depth. No module is repeated; you continue building on the skills you have already learned.
- Module 01 of 05
Python, Statistics & Data Fundamentals
The foundational knowledge every Data Scientist needs — how to program, query, clean, and statistically analyze raw data for modeling.
Topics covered
- Python programming
- Pandas
- NumPy
- statistical analysis
- hypothesis testing
- data cleaning
- 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 05
Machine Learning Fundamentals & Capstone
Building predictive machine learning models and understanding how to evaluate and optimize them, plus career readiness for data science roles.
Topics covered
- scikit-learn
- regression
- classification
- clustering
- model evaluation
- feature engineering
- problem framing
- portfolio development
- resume preparation
- interview readiness
Tools in this module
Scikit-learnPythonPandasMatplotlibSeabornGitHubPortfolioResumeYou finish withA complete capstone project with a trained and evaluated machine learning model, and an interview-ready portfolio
- Module 03 of 05
Advanced SQL, Data Visualization & Capstone Part 2
Taking your SQL skills to an advanced level and building compelling visualizations that communicate data science insights effectively.
Topics covered
- Advanced SQL
- window functions
- CTEs
- Matplotlib
- Seaborn
- Plotly
- exploratory data analysis
- data storytelling
- dashboard creation
Tools in this module
SQLPythonMatplotlibSeabornPlotlyTableauPower BIYou finish withA complex SQL analysis and an interactive dashboard deployed on a real dataset
- Module 04 of 05
Advanced Machine Learning & Model Deployment
Advanced machine learning techniques and deploying models to production — building APIs and web apps that serve predictions.
Topics covered
- Ensemble methods
- gradient boosting
- XGBoost
- hyperparameter tuning
- model deployment with Flask/Streamlit
- API development
- MLOps fundamentals
Tools in this module
Scikit-learnXGBoostFlaskStreamlitDockerGitHubAWSYou finish withAn end-to-end deployed machine learning application with a live API and web interface
- Module 05 of 05
Deep Learning, NLP & Career Readiness
Introduction to deep learning and natural language processing, plus comprehensive career readiness for data science roles.
Topics covered
- Neural networks
- TensorFlow/Keras
- NLP basics
- model optimization
- portfolio development
- resume
- LinkedIn profile
- interview readiness
- data science certification exam preparation
Tools in this module
TensorFlowKerasPythonGitHubPortfolioResumeMock interviewsYou finish withA deep learning project 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 Model
Analyze customer behavior data using classification algorithms to predict churn and provide actionable retention insights.
- Machine Learning
- Classification
- Business Intelligence
End-to-End ML Pipeline with Deployment
Build a complete machine learning pipeline that cleans, trains, evaluates, and deploys a model as a live web application using Flask/Streamlit and Docker.
- Python
- MLOps
- Deployment
- Flask
NLP Sentiment Analysis Project
Build a natural language processing model that analyzes customer reviews and classifies sentiment using TensorFlow and Keras.
- Deep Learning
- NLP
- TensorFlow
Expert Capstone Project
Complete an end-to-end data science project with data cleaning, SQL analysis, machine learning modeling, model deployment, 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.
Predictive Sales Forecasting with Machine Learning
- 02
Real datasets and projects
Every lab runs on a real-world dataset with real questions and outcomes, not a simplified demo.
Customer Churn Prediction Model
- 03
Mentor-reviewed deliverables
Each project ends with a working machine learning model or deployed application reviewed by a mentor who works daily on live data science problems.
End-to-End ML Pipeline with Deployment
- 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.
NLP Sentiment Analysis 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-world data science projects you can show and machine learning models 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 Applied Data Science 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 building and deploying machine learning models in the industry.
- 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 03 or 05.
- 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 Applied Data Science Course course
Who can join the Applied Data Science Course courseThis course is designed for individuals who need a practical skill upgrade to meet their career goals in the high-growth field of data science.
- 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 12 / 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 12 / 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 Applied Data Science 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
Numerical computing
- NumPy
Machine learning
- Scikit-learn
Gradient boosting
- XGBoost
Deep learning
- TensorFlow
Data visualization
- Matplotlib
- Tableau
Statistical visualization
- Seaborn
Business intelligence
- Power BI
Web framework
- Flask
App framework
- Streamlit
Containerization
- Docker
Version control
- Git
Code hosting
- GitHub
Certification
Get certified in Applied Data Science Course
Get certified in Applied Data Science 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, machine learning, SQL, and data storytelling.
- AI and deep learning are the new layersTeams expect data scientists to integrate deep learning models, NLP, automation, and MLOps, not just build static reports.
The objective is practical, job-ready knowledge that you can apply to solve real-world problems, not just 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, scikit-learn, TensorFlow, SQL, Flask, Docker | Usually limited to basic Python |
| Batch size | 8–10 students per batch | Often 25–40 students per batch |
| Model Deployment | Live deployment with Flask/Streamlit, Docker | Rarely covered |
| Advanced topics | Advanced ML, Deep Learning, NLP, MLOps | Often only theory |
| Automation & Scripts | Automated ML pipelines, API deployment | 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.
