About Course
Join SkilBrill's Data Science Training — a comprehensive programme covering the tools, concepts and real-world projects that employers look for. The master course is delivered through live online sessions with weekday, weekend and evening batch options.
Course Overview
Data Science combines statistics, programming, and domain expertise to extract insights and build predictive models. This programme covers Python, machine learning, deep learning, and MLOps.
Why Learn Data Science
Data scientists are among the most sought-after professionals. This programme prepares you for analyst, scientist, and ML engineer roles.
Who Should Join This Course
- Aspiring data scientists
- Analysts upgrading skills
- Engineers moving to ML
- Professionals seeking data-driven roles
Prerequisites
- Basic mathematics and statistics
- Programming basics in Python
- SQL fundamentals
Learning Objectives
- Clean and analyse data with Python
- Build predictive models
- Create visualisations and dashboards
- Apply ML algorithms end-to-end
- Communicate insights to stakeholders
96 hrs across 12 modules (90 topics)
Tools and Technologies
Python, Pandas, NumPy, Scikit-learn, TensorFlow, Keras, SQL, Tableau, Power BI, Jupyter, Git
Career Opportunities After This Programme
- Data Scientist
- Data Analyst
- ML Engineer
- Business Analyst
Hands-on Labs
- Hands-on lab on Python for Data Science — build and run a working example covering NumPy and Pandas, Data cleaning, Exploratory data analysis.
- Hands-on lab on Statistics and Probability — build and run a working example covering Descriptive statistics, Hypothesis testing, Probability distributions.
- Hands-on lab on Supervised Machine Learning — build and run a working example covering Regression, Classification, Model evaluation.
- Hands-on lab on Unsupervised Learning — build and run a working example covering Clustering, Dimensionality reduction, Association rules.
- Hands-on lab on Data Visualisation — build and run a working example covering Matplotlib and Seaborn, Tableau / Power BI, Storytelling with data.
- Hands-on lab on Advanced Topics and Deployment — build and run a working example covering NLP basics, Model deployment, MLOps overview.
Career Roadmap
Role Progression
- Data Scientist
- Data Analyst
- ML Engineer
- Business Analyst
Step-by-Step Learning Path
- Master Python for Data Science
- Master Statistics and Probability
- Master Supervised Machine Learning
- Master Unsupervised Learning
- Master Data Visualisation
- Master Advanced Topics and Deployment
- Prepare for technical interviews and update your resume and portfolio
Course Duration, Mode and Certification
The course runs for 12 weeks with 3 sessions per week (2 hours per session), totalling 72 classroom hours. Classroom, online, and hybrid modes are available where supported. You will receive a SkilBrill completion certificate and work on real-world projects, with dedicated interview preparation support at the end of the programme.
Why Train at SkilBrill
SkilBrill Training Institute focuses on making you employable. Trainers are working professionals, labs mirror real production environments, and the placement cell connects eligible candidates with hiring companies. Enquire today for the next batch start date and fee details.
Career and Job Support
SkilBrill provides practical career support to help you move from training into relevant roles. Services include:
- Resume assistance
- LinkedIn profile guidance
- Portfolio guidance
- Technical interview preparation
- Mock interviews
- Project explanation preparation
- Job-search guidance
- Placement assistance
Resume Building Guidance
- Keep your resume to one page if you have less than five years of experience.
- Use a clean format with clear sections: contact, objective, skills, projects, education and certifications.
- Tailor your skills section to match the job description.
- Quantify achievements where possible, for example "reduced regression time by 30%".
- Include links to your GitHub, LinkedIn and portfolio if available.
- Proofread carefully; spelling and grammar errors create a poor impression.
LinkedIn Profile Optimisation
- Use a professional headshot and a headline that includes your target role.
- Write a summary that highlights your skills, projects and career goals.
- List relevant tools, frameworks and certifications in the Skills section.
- Share posts or articles about what you are learning to show activity.
- Connect with trainers, classmates and professionals in your target domain.
- Request recommendations from mentors or project reviewers.
GitHub and Portfolio Guidance
- Create well-named repositories for each major project.
- Add a README file explaining the project, technologies, setup steps and screenshots.
- Use meaningful commit messages and keep code organised.
- Include a portfolio website or GitHub profile readme that links to your best work.
- Keep sensitive data like passwords and API keys out of public repositories.
- Regularly update repositories with improvements and new projects.
Job Search Strategy
- Update your resume and LinkedIn profile before applying.
- Apply to roles on LinkedIn, Naukri, Indeed and company career pages.
- Customise each application to match the job description.
- Prepare a short elevator pitch for phone screenings.
- Practise technical and behavioural questions daily.
- Follow up politely after interviews and ask for feedback.
- Attend meetups, webinars and networking events in your domain.
Frequently Asked Questions
What is Data Science?
Data science combines statistics, programming, and domain knowledge to turn raw data into predictions and decisions — using Python, SQL, machine learning, and visualisation to solve business problems end to end.
Who should learn Data Science?
Analytical graduates, engineers, and professionals from maths, statistics, or economics backgrounds targeting analyst and data scientist roles across every industry.
What are the prerequisites?
Basic mathematics and statistics. Python fundamentals help but are taught in the programme.
What topics are covered?
Statistics and hypothesis testing, Python with NumPy/Pandas, machine learning algorithms (regression, trees, ensembles, SVM, clustering), deep learning, NLP and LLMs, MLOps and deployment, Spark for big data, time series, A/B testing, and portfolio projects.
Is training online or classroom?
The master programme is live online. City pages offer classroom and hybrid options where supported.
What career support is available?
Resume building, GitHub and Kaggle portfolio guidance, data science interview preparation, case-study practice, and placement assistance.