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Program overview
Online
On-site
Hybrid
Data Science with Python
Build a strong foundation in Python for data analysis and machine learning by learning core programming concepts and visualization techniques. Gain hands on experience with real world examples and popular Python frameworks to build and apply machine learning models effectively.
Duration:
5 Days
Level:
Advanced
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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
- Python Foundations for Data Science
- Use Python syntax, types, functions, and control flow
- Organize scripts and notebooks for analysis work
- Manage environments and dependencies for reproducibility
- Hands-on: Build a small data processing script
- NumPy for Numerical Computing
- Work with arrays and vectorized operations
- Apply numerical routines common in data science
- Understand broadcasting and performance basics
- Prepare numeric data for downstream libraries
- Data Manipulation with Pandas
- Load, filter, join, and aggregate tabular data
- Reshape data for analysis and modeling
- Handle missing values and dtypes deliberately
- Build reusable pandas transformation steps
- Visualization with Matplotlib
- Create core chart types for exploratory analysis
- Customize plots for clarity and stakeholder use
- Combine multiple views for comparison
- Export visuals for reports and notebooks
- Data Cleaning and Preprocessing
- Apply cleaning patterns for ML-ready datasets
- Encode, scale, and split data correctly
- Detect outliers and data quality issues
- Document preprocessing assumptions
- Machine Learning Fundamentals
- Frame supervised and unsupervised problem types
- Understand train/validation/test discipline
- Choose baseline models before complexity
- Connect ML outputs to business decisions
- Models with Scikit-learn
- Build classic ML models with scikit-learn
- Compose preprocessing + model pipelines
- Compare algorithms on the same evaluation setup
- Hands-on: Train and score a baseline pipeline
- Model Evaluation and Selection
- Select metrics aligned to the problem type
- Use cross-validation and model selection patterns
- Diagnose bias/variance issues
- Pick a model with a clear trade-off rationale
- Deep Learning with TensorFlow
- Introduce neural network building blocks
- Train a simple TensorFlow model end to end
- Monitor training behavior and overfitting
- Know when deep learning is and is not warranted
- Text Processing and Web Scraping
- Process text with NLTK-style workflows
- Scrape and parse web data responsibly
- Structure scraped data for analysis
- Respect rate limits, robots, and legal boundaries
- Model Deployment and Project Customization
- Package models for scoring environments
- Choose simple deployment patterns for first releases
- Customize project structure for team workflows
- Define monitoring and retraining next steps
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
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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
- Faster Insights: Quickly analyze large volumes of data, identify trends, and make data-driven decisions.
- Cost-effective: Python, being an open-source programming language, provides a cost-effective solution for you to utilise data analytics without significant expenditures.
- Scalable: Manage large datasets and adapt to the evolving requirements of a growing business, create tailored data science solutions that seamlessly integrate with other tools and systems.
Training objectives
- Gain a solid understanding of the basics of Python programming
- Gain an understanding of data analysis and visualisation
- Get introduced to machine learning and discover popular Python frameworks for building machine learning models
- Acquire hands-on experience with real-world examples and projects
Who will benefit
- Data Analysts
- Data Scientists
- Business analysts
- IT professionals
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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.