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Program overview
Online
On-site
Hybrid
Data Science with Machine Learning
Build a strong foundation in data science and machine learning by mastering core concepts, algorithms, and data analysis techniques. Learn how to build, evaluate, and scale production ready predictive models using modern tools and frameworks for real world business applications.
Duration:
4 Days
Level:
Intermediate
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1500+ users onboarded
Who will Benefit from this Training?
Training Objectives
Build a high-performing, job-ready tech team.
Personalise your team’s upskilling roadmap and design a befitting, hands-on training program with Uptut
Key training modules
Comprehensive, hands-on modules designed to take you from basics to advanced concepts
Download Curriculum
- Introduction to Data Science
- Define data science roles and delivery outcomes
- Map ML to business problem types
- Outline the lifecycle from data to deployed model
- Set project success criteria early
- Data Preprocessing and Cleaning
- Handle missing values, outliers, and inconsistencies
- Standardize data quality checks before modeling
- Prevent leakage during prep
- Create a reproducible cleaning checklist
- Data Exploration and Visualization
- Explore distributions and relationships visually
- Identify patterns that guide feature design
- Spot anomalies and data collection issues
- Summarize EDA findings for stakeholders
- Supervised Learning Algorithms
- Apply core regression and classification algorithms
- Match algorithm families to problem constraints
- Train baselines before advanced models
- Hands-on: Compare two supervised approaches
- Unsupervised Learning Algorithms
- Use clustering and dimensionality reduction
- Interpret unsupervised outputs carefully
- Apply unsupervised methods for discovery and features
- Validate clusters with practical business sense checks
- Model Evaluation and Selection
- Evaluate models with appropriate metrics
- Compare candidates with consistent validation design
- Select models for accuracy, cost, and explainability
- Document selection rationale
- Feature Engineering and Selection
- Engineer features that improve signal
- Select features to reduce noise and complexity
- Encode categoricals and time signals safely
- Maintain feature definitions for scoring parity
- Time Series Analysis
- Analyze time-dependent data for forecasting
- Use time-aware validation splits
- Apply ML approaches suited to temporal patterns
- Communicate forecast confidence and limits
- Model Deployment and Scalability
- Deploy models for batch or online scoring
- Plan for scale, latency, and reliability
- Version models and dependencies
- Define basic monitoring after go-live
- Case Studies and Real-World Projects
- Apply ML techniques to practical business cases
- Practice scoping, modeling, and presenting results
- Review current trends and best practices in applied ML
- Capture lessons learned for future projects
Hands-on Experience with Tools
Training Delivery Format
Flexible, comprehensive training designed to fit your schedule and learning preferences
Opt-in Certifications
AWS, Scrum.org, DASA & more
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100% Live
on-site/online training
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Hands-on
Labs and capstone projects
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Lifetime Access
to training material and sessions
Skill-Gap Assessment
Analysing skill gap and assessing business requirements to craft a unique program
1
Personalisation
Customising curriculum and projects to prepare your team for challenges within your industry
2
Implementation
Supplementing training with consulting support to ensure implementation in real projects
3
Why this course
- Data-Driven Decision Making: Data Science with Machine Learning enables businesses to make informed decisions based on data-driven insights.
- Improved Efficiency and Productivity: Machine Learning automates processes, enhancing efficiency and productivity. It automates repetitive tasks, performs complex calculations, and accurately analyses large datasets.
- Predictive Analytics and Forecasting: Machine learning models can be used to make accurate predictions and forecasts, empowering businesses to anticipate customer behaviour, market trends, and business outcomes.
- Cost Reduction: Machine Learning can lead to cost savings by automating processes and improving efficiency.
Training objectives
- Develop a solid understanding of the core concepts of Data Science
- Gain proficiency in applying a variety of supervised and unsupervised machine learning algorithms
- Learn how to analyse and visualize data effectively using popular libraries and tools
- Acquire practical skills in building accurate predictive models using machine learning algorithms
- Understand the process of evaluating and validating machine learning models using appropriate metrics, and techniques for model selection to ensure robust and reliable results
- Learn how to develop production-level Machine-Learning models and scale them effortlessly with state-of-the-art frameworks.
Who will benefit
- Software Engineers/Developers
- Business Analysts
- IT Professionals
- Data Scientists
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Frequently Asked Questions
2. Will my team get any practical experience with this training?
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With our focus on experiential learning, we have made the training as hands-on as possible with assignments, quizzes and capstone projects, and a lab where trainees will learn by doing tasks live.
3. What is your mode of delivery - online or on-site?
We conduct both online and on-site training sessions. You can choose any according to the convenience of your team.
4. Will trainees get certified?
Yes, all trainees will get certificates issued by Uptut under the guidance of industry experts.
5. What do we do if we need further support after the training?
We have an incredible team of mentors that are available for consultations in case your team needs further assistance. Our experienced team of mentors is ready to guide your team and resolve their queries to utilize the training in the best possible way. Just book a consultation to get support.