Data Science Course
Master python for data science, SQL, machine learning, and ai data science from scratch. Graduate with a production-grade portfolio of data science projects that proves your technical ability to land high-paying roles.
- 20+ students trained
Overview
Transform complex data into actionable intelligence and launch a high-growth technology career. The Data Science Course by Commit Career is an intensive, practical program engineered to transform ambitious learners into industry-ready data professionals.
Guided by a battle-tested curriculum and active industry mentors, you will gain practical mastery over modern python programming for data science, advanced predictive modeling, SQL database querying, and neural networks. Beyond raw statistics, you will build core skills including end-to-end machine learning pipelines, exploratory data analysis (EDA), natural language processing (NLP), and AI model deployment, learning the exact workflows top data engineering and analytics teams use.
Whether you are searching for hands-on online classes data science options or a structured data science bootcamp, this program delivers a production-ready portfolio, an official data science certificate, and the technical confidence to launch your journey into data science entry level careers.
Who This Course Is For
Complete beginners who have no prior experience in programming or statistics.
Career changers moving into tech, analytics, or AI from non-technical fields.
University students who want practical, job-ready skills that traditional data science online degree curricula often skip.
International students in Australia, Canada, the USA, and the UK seeking a industry-aligned portfolio for local job markets.
Working professionals upskilling around a full-time job to transition into data-driven decision-making.
Self-taught learners who have completed basic tutorials but struggle to build and deploy complex, end-to-end models.
Why Choose Commit Career?
Project-Based Learning: Master concepts by writing code and training models, not through passive video quizzes.
Industry-Aligned Curriculum: Designed directly around what engineering leads look for in data science entry level careers.
Deployed Portfolio: Graduate with live web applications and clean GitHub repositories that demonstrate actual proof of work.
Comprehensive Tooling: Gain practical experience across data science tools including Python, SQL, PyTorch, Scikit-Learn, and Docker.
1-on-1 Career Support: Work directly with a mentor on your resume, portfolio, and strategies for acquiring data science internships.
Realistic Workflows: Learn modern software engineering practices including version control, deployment, and AI-assisted workflows.
Flexible Schedule: Designed for students and working professionals, featuring live online evening sessions and complete recordings.
What you'll learn
- Master python programming for data science, environment setup, and foundational SQL database queries.
- Master Pandas, NumPy, and statistical plotting. Learn the clear analytical boundaries in data science vs data analytics.
- Implement regression, classification, clustering, and model tuning with Scikit-Learn.
- Dive into neural networks, PyTorch, natural language processing, and modern LLM application workflows.
- Wrap models into web APIs using FastAPI and Streamlit, containerize with Docker, and publish production URLs.
- Build, present, and deploy your custom capstone model. Receive direct support on data science interview questions, resume optimizations, and applications for data science internships and junior roles.
Curriculum
7 modules • 52 lessons • 16 weeks
Module 1: Data Science & Python Foundations
The technical foundation every ML engineer needs.
- Python 3 Environment Setup & Jupyter Notebooks
- Data Types, Control Flow, and Functions
- Object-Oriented Programming (OOP) for Data Pipelines
- NumPy Arrays & Matrix Computations
- Pandas DataFrames: Manipulation, Filtering, & Merging
- Git & GitHub Workflows for Data Scientists
- Basics of r for data science Data Structures
Module 2: Exploratory Data Analysis & Visual Analytics
- Data Cleaning: Handling Missing Values & Outliers
- Statistical Foundations: Mean, Variance, Probability Distributions, Hypothesis Testing
- Data Visualization with Matplotlib & Seaborn
- Interactive Data Apps with Plotly
- Data Visualization Ethics
- Feature Engineering & Scaling Techniques
- Handling Categorical & Imbalanced Data Sets
Module 3: Databases & Data Pipeline Engineering
- Relational Database Design Principles
- SQL Core Queries: SELECT, WHERE, GROUP BY, HAVING
- Advanced SQL: Subqueries, CTEs, and Window Functions
- Connecting Python to SQL Databases (SQLAlchemy, psycopg2)
- Building Automated ETL (Extract, Transform, Load) Scripts
- Data Wrangling at Scale
- Data Pipeline Error Handling & Logging
Module 4: Applied Machine Learning
Take a model from notebook to production.
- Supervised Learning Frameworks
- Linear & Polynomial Regression
- Logistic Regression & Binary/Multiclass Classification
- Decision Trees, Random Forests, and Ensemble Learning
- Gradient Boosting Frameworks: XGBoost, LightGBM
- Unsupervised Learning: K-Means & Hierarchical Clustering
- Dimensionality Reduction: Principal Component Analysis (PCA)
- Model Evaluation Metrics: Confusion Matrices, ROC-AUC, Precision/Recall, F1-Score
Module 5: Deep Learning & AI Data Science
- Introduction to Neural Networks & Perceptrons
- Deep Learning Concepts with PyTorch
- Convolutional Neural Networks (CNNs) for Visual Data
- Recurrent Neural Networks (RNNs) & LSTMs
- Natural Language Processing (NLP) Fundamentals & TF-IDF
- Transformer Architectures & Hugging Face Libraries
- Leveraging LLM APIs for Predictive Workflows
- Prompt Engineering for Data Analysis
Module 6: MLOps, APIs & Production Deployment
- Model Persistence (Joblib & Pickle)
- Building Data REST APIs with FastAPI
- Building Interactive ML Interfaces with Streamlit & Gradio
- Containerizing Applications with Docker Basics
- Version Control for Data & Models (DVC Fundamentals)
- Deploying ML Web Applications to Cloud Hosting Platforms
- Continuous Integration for Data Science Projects
Module 7: Industry-Level Data Science Projects
- Global Economic EDA & Market Report
- SQL Enterprise Business Intelligence Suite
- Housing Price Regression Engine
- Customer Churn Classifier
- E-commerce Customer Segmentation Model
- NLP Social Sentiment Dashboard
- Financial Time-Series Forecasting Platform
- Production AI Capstone Application
Meet Your Mentor
Dhiraj Bashyal
Machine Learning Expert, 10+ years
Dhiraj Bashyal is a Machine Learning Engineer with 5+ years of hands-on experience in applied AI and machine learning. He brings that industry experience directly into the classroom, teaching Data Science and Machine Learning at Commit Career, where he helps learners build a practical, project-ready foundation in Python, ML workflows, and real-world data problem-solving.
Projects & Case Studies
This course's project gallery is still in development — check back soon.
Requirements
- Basic programming experience (any language)
- Comfort with high-school level math is helpful, not required
- A laptop capable of running Python (Windows, macOS, or Linux)
Frequently asked questions
What does a Data Scientist do on a daily basis?
A Data Scientist collects, cleans, and analyzes complex structured and unstructured data to build predictive models and business solutions. They translate abstract corporate questions into technical algorithms using statistical analysis, machine learning, and programming.
What is the main difference in data analysis vs data science?
Data analysis vs data science comes down to focus and scope: Data Analysts typically focus on describing past historical trends to answer business questions using tools like SQL, Excel, and dashboards. Data Scientists use statistical algorithms, machine learning models, and automated Python pipelines to predict future outcomes and automate decisions.
What is a typical entry-level data science salary?
Average data science salary ranges vary by location and experience, but entry-level data scientists in the United States typically earn between $75,000 and $95,000 annually. Senior data scientists and AI specialists often exceed $130,000 to $180,000+ per year.
In data visualization, why are radar charts bad or discouraged?
Looking into data science, why are radar charts bad: They distort data perception because human eyes struggle to compare polygon areas across arbitrary angles. Small changes near the center appear visually understated, while outer changes look disproportionately large. Standard bar charts or parallel coordinate plots provide far more accurate visual comparisons.
Is a data science bootcamp better than a traditional data science online degree?
A data science bootcamp focuses heavily on practical coding, real-world portfolio projects, and rapid employment preparation over 3 to 6 months. A traditional data science online degree covers more theoretical mathematics over 2 to 4 years. For career changers looking to enter the market quickly with verifiable projects, a bootcamp is often the faster, more cost-effective path.
Do I need a math or computer science background to take this data science course?
No. This course starts with foundational concepts. We teach python programming for data science and math principles step-by-step, building up to machine learning and deep learning.
How does this program help me prepare for data science entry level careers?
We focus on building a production portfolio, preparing you for technical coding tests, and practicing common data science interview questions. You also work 1-on-1 with a career advisor on your resume, LinkedIn, and application strategies.
Will I receive a data science certificate upon completion?
Yes. Graduates who complete all projects and the final capstone receive an official data science certificate from Commit Career, which can be linked directly to your LinkedIn profile and resume.
Does this course cover both Python and R?
Yes. While the primary focus is python for data science (the dominant language in modern production machine learning), we introduce r for data science so you are comfortable working in hybrid data environments.
Can I balance this data science boot camp with a full-time job or university studies?
Yes. Our online classes data science program features live evening sessions with recorded replays, allowing you to build skills alongside existing work or academic commitments.
