1. A Curriculum Built Around What Data Science Roles Actually Require
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Data Science Foundations: The data science lifecycle, problem framing, hypothesis formation, and how data drives product and business decisions. Build the strategic layer before diving into code.
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Python for Data Science: pandas, NumPy, matplotlib, seaborn, and scikit-learn. Python is the primary language for every certified data science professional role on the market today.
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Statistics and Probability: Distributions, hypothesis testing, Bayesian thinking, correlation, and regression theory. These are the foundations behind every model you will build.
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SQL for Data Science: Query, join, aggregate, and clean data inside relational databases. SQL is a non-negotiable baseline requirement alongside Python in every data science course.
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Machine Learning: Supervised and unsupervised learning — linear regression, logistic regression, decision trees, random forests, SVMs, K-means, and more. Understand which model fits which problem and why.
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Feature Engineering and Data Preparation: Handle missing values, encode categorical variables, scale features, and build pipelines that make models more accurate and production-ready.
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Model Evaluation and Optimisation: Cross-validation, confusion matrices, ROC curves, hyperparameter tuning, and regularisation techniques. Learn to improve models, not just build them.
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Deep Learning Fundamentals: Neural networks, backpropagation, TensorFlow, and Keras basics. Understand where deep learning fits relative to classical ML methods.
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Data Visualisation and Communication: Power BI, matplotlib, and seaborn for visual reporting. Present model outputs and insights clearly enough that non-technical stakeholders can act on them.
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Capstone ML Project: Apply everything end-to-end — from raw data to a trained, evaluated, and presented model — using real datasets and a structured problem statement.
2. Tools and Platforms You Will Work On
At Skillfloor, you don't just study these tools in theory. You use them, build real outputs inside them, and leave the data science course with hands-on experience that employers can verify and test.
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Programming: Python (pandas, NumPy, matplotlib, seaborn, scikit-learn)
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Deep Learning: TensorFlow, Keras, PyTorch basics
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SQL: MySQL, PostgreSQL
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Notebooks: Jupyter Notebook, Google Colab
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Visualisation: Power BI, Tableau, Plotly
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ML Frameworks: scikit-learn, XGBoost, LightGBM
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Version Control: Git and GitHub for project management
3. Course Details: Duration and Fees
This data science certificate program is priced to be accessible without reducing the depth or quality of what you learn.
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Course Name: Certified Data Science Professional
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Duration: 6 weeks of training + internship component
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Mode: Online Live / Classroom / Hybrid
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Fees: Starts at ₹14,900
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EMI: Available — No Cost EMI options
4. Flexible Classes for Students and Working Professionals
Whether you are a student or in a full-time role, there is a batch designed to fit your availability. This data science online course runs across multiple formats and cities in India.
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Weekday Batch: Monday to Friday ● 1.5 hours per day ● Morning and evening slots available
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Weekend Batch: Saturday and Sunday ● 4–5 hours per session ● Ideal for working professionals
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Fast-Track Batch: Intensive format ● Completed in 4–6 weeks ● Full curriculum, accelerated pace
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Online Live Batch: Instructor-led, fully interactive ● Session recordings available for lifetime
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Classroom Batch: Available in Bangalore, Hyderabad, Chennai, Pune, Ahmedabad, and more cities
5. Certifications You Receive That Add Weight to Your Resume
Skillfloor's data science professional certificate gives you a stack of verifiable credentials that employers evaluate when shortlisting candidates.
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Skillfloor Certified Data Science Professional: Issued on completion of coursework, ML project, and final assessment. The primary credential from this program.
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Internship Certificate: A formal completion certificate that adds practical credibility alongside your data science certificate.
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NASSCOM · Futureskills Prime: Five recognised industry body certifications included. NASSCOM and Futureskills Prime are government-backed credentials.
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10+ Tool and Skill Certifications: Python, scikit-learn, TensorFlow, SQL, Power BI, and more — each adding a verifiable, platform-specific credential.
6. Placement Support
Skillfloor's placement support is structured and ongoing. Here is what every certified data science professional graduate receives.
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Resume Building: Data science-specific templates reviewed by practitioners and hiring leads.
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LinkedIn Profile Optimisation: Profile audit, keyword alignment, and visibility strategies for technical recruiters.
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Mock Interviews: Domain-specific rounds with practising data scientists and ML engineers.
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Job Referrals: Active tie-ups with data teams at startups, product companies, consulting firms, and BFSI organisations.
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Portfolio Review: Your ML project is packaged and reviewed to make a strong impression in technical interviews.
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Interview Prep: Python coding questions, statistics rounds, ML theory, case study walkthroughs, and salary negotiation guidance.
7. Designed for Beginners, Professionals, and Career Switchers
This data science course for beginners and experienced professionals requires no prior machine learning background — just motivation to learn and apply.
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Fresh Graduates: You want a data science career and need a data science certificate that employers respect from day one.
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Working Professionals: You're in IT, analytics, finance, or engineering and want to move into data science with structured, credentialed training.
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Software Developers: You already write code and want to formalise your ML skills with a recognised certified data science professional credential.
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Business Analysts: You work with data daily and want to upgrade from reporting to predictive modelling and machine learning.
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Freelancers: You want a verifiable data science certificate to position yourself for higher-value projects.
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Career Switchers: Coming from non-technical domains and want to enter one of the highest-growth professional fields with a solid foundation.
8. Work on a Real End-to-End Machine Learning Project
Every learner completes one dedicated ML project built on a real business problem. Instead of only absorbing theory, you will source data, clean it, engineer features, train and evaluate models, and present your results in a structured report.
What You Will Build:
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✔ A complete machine learning pipeline from raw data to a trained model
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✔ Feature engineering and data preparation notebooks
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✔ Model evaluation with accuracy, precision, recall, and ROC metrics
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✔ A visual presentation of results and business impact
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✔ A portfolio-ready project with code hosted on GitHub
9. Present Your Project to Mentors and Build Real Interview Confidence
At the end of the program, you present your completed ML project to data science practitioners in a structured review session. This is where technical competency becomes communication confidence.
During the Final Presentation, You Will:
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✔ Receive expert feedback on your model choices and methodology
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✔ Improve your data storytelling and technical communication
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✔ Learn how to explain ML outputs to business stakeholders
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✔ Build a stronger portfolio with mentor-guided refinements
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✔ Develop confidence for technical and panel interview rounds
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✔ Gain real-world exposure beyond what any classroom alone can provide
10. Career Paths You Can Step Into After This Program
Graduates move into roles across product companies, consulting firms, BFSI organisations, e-commerce platforms, and research teams. The demand for certified data science professionals continues to expand across India and globally.
Data Scientist ● ML Engineer ● Data Analyst (ML-focused) ● AI/ML Researcher ● NLP Engineer ● Computer Vision Engineer ● Business Intelligence Engineer ● Data Science Consultant ● Product Data Scientist ● Freelance ML Consultant
Enroll Now in the Certified Data Science Professional Program — Seats Are Limited Per Batch
