Flagship project
Exploratory Data Analysis Project
Build a complete EDA project with data cleaning, statistical analysis, and visualizations.
- EDA
- Statistics
- Visualization
Data ScienceHigh-Growth Skill
Complete Data Science Career Track Course in Amritsar – Zero to Job Ready with Real Projects & Placement Support
Complete Data Science Career Track Course course in AmritsarJob-oriented Data Science training built on real industry scenarios — small batches, daily hands-on labs and 100% placement assistance.
6 / 9 MonthsBeginner to advancedHybrid
6 / 9 Months
Duration
Weekday/Weekend
Mode
12th Pass/Any Grad
Eligibility
10-12 Students
Batch Size
Admissions open
Complete Data Science Career Track Course · 6 / 9 Months
Batch timings
- Morning
- Evening
- Weekend
- Live online
About the programme
Course overview
Course overviewtechcadd's Complete Data Science Career Track Course in Amritsar is a practical, project-first introduction to data science for beginners and career switchers.
It opens with Python programming, statistics, data wrangling, and exploratory data analysis, then moves into SQL, machine learning, deep learning, natural language processing, model deployment, and data visualization on real datasets.
You finish with a documented data science portfolio, a capstone project, CV preparation and interview coaching.
Every business in Amritsar — from retail shops near Hall Bazaar to IT companies on Airport Road, from local startups to D2C brands scaling across India — is drowning in data. And data, today, is the new currency. Companies are collecting more information than ever before, but most of them don't know what to do with it. They need people who can turn messy spreadsheets into decisions, build models that predict customer behaviour, and tell stories with numbers that actually drive revenue. Data Science has become the single most in-demand skill across industries, powering everything from recommendation engines on e-commerce platforms to fraud detection in fintech. But the field has matured. The old "learn Python and you're done" approach no longer works. Employers now expect analysts who can clean data, engineers who can deploy models, and scientists who understand business context — all in one profile.
techcadd's Complete Data Science Career Track Course in Amritsar is built on the projects you actually see in job descriptions and client briefs — Python programming, statistics, SQL, machine learning, deep learning, NLP, model deployment, and data visualization — not abstract theory. You work with real datasets from the very first module and finish with a documented data science portfolio, not just a certificate. The programme runs in a 6-month foundation track, with an optional 9-month extended track for deeper mastery. Batches are capped at 10–12 students, and every project you build is reviewed weekly by mentors who still work on live data problems.
Placement assistanceLive data labsInternship certificate
- Duration
- 6 / 9 Months
- Level
- Beginner to advanced
- Mode
- Hybrid
- Batches
- Morning, evening & weekend
Module by module
The curriculum, module by module
Module by module curriculumThe Complete Data Science Career Track is a nested ladder — the longer track includes every module from the shorter track and adds more depth. No module is repeated; you continue building on the skills you have already learned.
- Module 01 of 12
Python Programming & Data Science Fundamentals
The foundational programming knowledge every data scientist assumes you already know — how to write clean Python code and work with data structures.
Topics covered
- Python syntax
- data types
- control flow
- functions
- file handling
- NumPy
- Pandas
- data manipulation
- Jupyter Notebooks
- Git basics
Tools in this module
PythonJupyterNumPyPandasGitVS CodeYou finish withA documented Python project with data manipulation exercises
- Module 02 of 12
Statistics, Probability & Exploratory Data Analysis
Core statistical thinking — understanding data distributions, testing hypotheses, and exploring datasets that drive decisions.
Topics covered
- Descriptive statistics
- inferential statistics
- probability distributions
- hypothesis testing
- correlation
- regression basics
- EDA
- data cleaning
- outlier detection
- visualization
Tools in this module
PythonPandasMatplotlibSeabornSciPyStatsmodelsYou finish withA complete EDA project with statistical analysis and visualizations
- Module 03 of 12
SQL, Databases & Data Wrangling
Database skills and data wrangling — pulling, cleaning, and preparing data for analysis and modelling.
Topics covered
- SQL queries
- joins
- subqueries
- window functions
- database design
- data extraction
- data transformation
- ETL basics
- working with APIs
Tools in this module
MySQLPostgreSQLSQLitePythonPandasSQLAlchemyYou finish withA SQL project with complex queries and a data pipeline exercise
- Module 04 of 12
Machine Learning Fundamentals
Core machine learning — building, training, and evaluating predictive models on real datasets.
Topics covered
- Supervised learning
- unsupervised learning
- linear regression
- logistic regression
- decision trees
- random forests
- k-means clustering
- model evaluation
- cross-validation
- feature engineering
Tools in this module
Scikit-learnPythonPandasNumPyMatplotlibSeabornYou finish withA machine learning project with multiple models and performance metrics
- Module 05 of 12
Data Visualization & Business Intelligence
Data visualization and BI tools — turning analysis into dashboards that drive business decisions.
Topics covered
- Data storytelling
- dashboard design
- KPI tracking
- interactive visualizations
- business reporting
- Tableau basics
- Power BI basics
- Matplotlib
- Plotly
Tools in this module
TableauPower BIPlotlyMatplotlibSeabornLooker StudioYou finish withAn interactive dashboard with business KPIs and data storytelling
- Module 06 of 12
Capstone Project, Career & Interview Readiness
A capstone project combining data wrangling, machine learning, visualization, and business insights, plus career readiness for data science roles.
Topics covered
- End-to-end data science project
- problem framing
- data collection
- model building
- deployment basics
- portfolio development
- resume
- LinkedIn profile
- interview readiness
- data science certification exam preparation
Tools in this module
PythonScikit-learnGitHubPortfolioResumeLinkedInYou finish withA complete capstone project with documentation, and an interview-ready data science portfolio and resume
- Module 07 of 12
Deep Learning & Neural Networks
Deep learning — building and training neural networks for image, text, and sequential data.
Topics covered
- Neural networks
- backpropagation
- CNNs
- RNNs
- LSTMs
- transfer learning
- TensorFlow
- Keras
- PyTorch
- model tuning
Tools in this module
TensorFlowKerasPyTorchPythonGoogle ColabYou finish withA deep learning project with CNN or RNN implementation
- Module 08 of 12
Natural Language Processing & Computer Vision
NLP and computer vision — applying machine learning to text and image data.
Topics covered
- Text preprocessing
- tokenization
- word embeddings
- sentiment analysis
- topic modelling
- transformers
- BERT basics
- image classification
- object detection
- OpenCV
Tools in this module
NLTKSpaCyHugging FaceOpenCVTensorFlowPyTorchYou finish withAn NLP project with sentiment analysis and a computer vision project with image classification
- Module 09 of 12
Model Deployment, MLOps & Cloud Basics
Deployment and MLOps — taking models from notebook to production.
Topics covered
- Model deployment
- Flask
- FastAPI
- Docker
- ML pipelines
- model monitoring
- CI/CD for ML
- AWS basics
- GCP basics
- serverless deployment
Tools in this module
FlaskFastAPIDockerAWSGCPMLflowGitHub ActionsYou finish withA deployed machine learning model with API endpoint and monitoring
- Module 10 of 12
Big Data, Spark & Advanced Analytics
Big data and distributed computing — processing large-scale datasets for analytics and ML.
Topics covered
- Big data concepts
- Hadoop basics
- Spark
- PySpark
- distributed computing
- data lakes
- ETL at scale
- real-time analytics
Tools in this module
Apache SparkPySparkHadoopDatabricksKafka basicsYou finish withA PySpark project with large-scale data processing and analytics
- Module 11 of 12
Advanced Machine Learning & Model Optimization
Advanced machine learning — optimizing models for accuracy, speed, and interpretability.
Topics covered
- Ensemble methods
- XGBoost
- LightGBM
- hyperparameter tuning
- feature selection
- model interpretability
- SHAP
- LIME
- AutoML
- experiment tracking
Tools in this module
XGBoostLightGBMOptunaSHAPLIMEMLflowAutoMLYou finish withAn optimized ML project with ensemble models and explainability reports
- Module 12 of 12
Advanced Capstone, Career & Interview Readiness
An advanced capstone project combining machine learning, deep learning, deployment, and business impact, plus career readiness for senior data science roles.
Topics covered
- Full data science lifecycle
- problem framing
- data strategy
- model deployment
- portfolio development
- resume
- LinkedIn profile
- interview readiness
- data science certification exam preparation
Tools in this module
GitHubPortfolioResumeLinkedInMock interviewsCase studiesYou finish withA complete advanced capstone project with documentation, 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.
Machine Learning Prediction Model
Deploy a machine learning model with feature engineering and performance metrics.
- Machine Learning
- Scikit-learn
- Prediction
NLP & Deep Learning Project
Build an NLP or deep learning project with sentiment analysis or image classification.
- NLP
- Deep Learning
- TensorFlow
Expert Capstone Project
Complete an end-to-end data science project with deployment, monitoring, and business impact.
- Multi-Domain
- Portfolio
- Deployment
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
Industry-focused curriculum
You learn it the way professional data science teams use it, with the workflow and standards a data scientist expects.
Exploratory Data Analysis Project
- 02
Real datasets and projects
Every module runs on real datasets with real business questions and real outcomes, not a slide or a screenshot.
Machine Learning Prediction Model
- 03
Mentor-reviewed deliverables
Each module ends with a working project reviewed weekly by a mentor who still works on live data problems.
NLP & Deep Learning Project
- 04
Real projects and scenarios from the start
Every model is built on real-world data briefs and measured through accuracy, precision, and business impact, the way data teams work.
Expert Capstone Project
The honest pitch
Your months should build more than a certificate.
Your months should build more than a certificate.A course is worth the time you give it only if you finish with real projects you can show and 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 Complete Data Science Career Track Course
Why students choose techcadd
Why students choose techcaddWhat makes this course at techcadd different from a typical alternative is that every module is built on a real data science project and documented to professional standards, so you finish with an end-to-end data science portfolio rather than disconnected certificates.
- 01
Mentors who still work on live data problems
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 06 or 12.
- 03
Small batch sizes
10–12 students per batch ensures every student gets personalised feedback on every deliverable.
- 04
Nested, no-repeat curriculum
Longer programs include everything from shorter tracks and add 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 Complete Data Science Career Track Course course
Who can join the Complete Data Science Career Track Course courseThis course works for a specific starting point — here's who gets the most out of it.
- 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 6 / 9 months.
- 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 6 / 9 months, 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 Complete Data Science Career Track 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
Notebook environment
- Jupyter
Numerical computing
- NumPy
Data manipulation
- Pandas
Visualization
- Matplotlib
Statistical visualization
- Seaborn
Machine learning
- Scikit-learn
Deep learning
- TensorFlow
- PyTorch
Deep learning API
- Keras
NLP
- NLTK
- SpaCy
Transformers
- Hugging Face
Computer vision
- OpenCV
Database querying
- SQL
Database
- MySQL
- PostgreSQL
BI & visualization
- Tableau
- Power BI
Interactive visualization
- Plotly
Web framework
- Flask
API framework
- FastAPI
Containerization
- Docker
Cloud platform
- AWS
- GCP
ML lifecycle
- MLflow
Big data
- Apache Spark
Big data with Python
- PySpark
Version control
- Git
Code hosting
- GitHub
Code editor
- VS Code
Cloud notebooks
- Google Colab
Reporting
- Looker Studio
Certification
Get certified in Complete Data Science Career Track Course
Get certified in Complete Data Science Career Track 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 analytical knowledge will extend to machine learning and AI-driven decision making.
- Automation and AI are the new layersTeams expect data scientists to integrate MLOps, automation, and privacy-first data practices, not just build models.
The objective is practical 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, TensorFlow, PyTorch, SQL, Tableau | Usually limited to basic Python |
| Batch size | 10–12 students per batch | Often 25–40 students per batch |
| Deep Learning & NLP | Live integration, real projects | Rarely covered |
| Advanced topics | MLOps, deployment, Big Data, Spark | Often only theory |
| Automation & Scripts | ML pipelines, Docker, CI/CD | Rarely covered |
| Portfolio | 10 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 | Often only a completion certificate |
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.
