🎓 Data Science Practical Training Program

Turning Data into Decisions with Hands-on Projects and Modern Tools

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📌 Program Overview

In today’s data-driven world, organizations demand professionals who can not only interpret data but also derive actionable insights from it. This Data Science Practical Training Program by Stunited CIC is built to prepare UK-based students and graduates for real-world data challenges by training them on industry-relevant tools and frameworks. From data cleaning and visualization to machine learning basics, interns will work on practical projects using open-source and free platforms, gaining essential experience to be job-ready for entry-level data analyst or junior data scientist roles across sectors.

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🎯 Core Learning Objectives

• Master essential tools in data science such as Python, SQL, Power BI, and Excel

• Perform exploratory data analysis (EDA), data wrangling, and visual storytelling

• Understand structured workflows for data pipeline creation

• Apply machine learning concepts using real datasets

• Present data insights using dashboards, notebooks, and reports

• Practice teamwork, documentation, and code versioning

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🧩 Detailed Course Structure

1. Programming Foundations for Data

Tools: Python (Jupyter Notebook via Anaconda)

• Data types, loops, functions

• Libraries: NumPy, Pandas

• DataFrames and CSV handling

• Exploratory data analysis basics

• Working in Jupyter Notebook effectively

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2. Spreadsheet Analysis for Data Science

Tools: Excel / Google Sheets

• Data cleaning and filtering

• Pivot tables and data summaries

• Conditional formatting

• Charts for data visualization

• Financial or operational use case scenarios

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3. Data Analytics with Power BI & Tableau

Tools: Power BI (Desktop) / Tableau Public

• Build interactive dashboards

• Import and clean raw data

• Visual storytelling and KPI tracking

• Data filters, DAX basics, trend analysis

• Use cases: marketing data, EV performance, sales reports

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4. Working with Databases (SQL)

Tools: MySQL / PostgreSQL (via web or local tools)

• Create, update, and delete records

• Joins, filters, aggregations

• Subqueries and window functions

• Real dataset querying (e.g., sales or student database)

• Export SQL output to CSV for further analysis

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5. Version Control and Collaboration

Tools: GitHub / VS Code

• Setup GitHub repository

• Track versions and commit messages

• Work on Python or CSV files collaboratively

• Submit assignments using Git & GitHub

• Real-world team collaboration simulation

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6. Public Data Exploration & EDA

Tools: Kaggle / UCI Datasets

• Select dataset from public repositories

• Clean and analyze using Python

• Visualize trends with Seaborn/Matplotlib

• Summarize findings with markdown

• Create a case study notebook for review

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7. AI Tools for Data Analysis

Tools: ChatGPT, Bard, Claude

• Generate Python functions from prompts

• Get dataset summaries and project help

• Validate model outputs

• Use AI for documentation and presentation drafts

• Prompt Engineering for automation

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8. Machine Learning Fundamentals

Tools: Scikit-learn (via Jupyter)

• Supervised vs. unsupervised learning

• Build simple models: Linear Regression, KNN, Decision Tree

• Evaluate using accuracy, precision, recall

• Train/test split and cross-validation

• Case study: student performance or retail sales

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9. Data Storytelling & Reporting

Tools: PowerPoint, Canva, Markdown

• Summarize insights in a visual format

• Prepare project decks

• Design infographic-style data visuals

• Create stakeholder-ready presentations

• Document analysis in markdown

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10. Real-World Project Showcase

Tools: Combination of all tools

• Choose a theme: health, finance, education, retail

• Build an end-to-end mini project

• Include data cleaning, EDA, visualization, and ML (if applicable)

• Submit via GitHub, present findings via Zoom

• Get reviewed by peers and mentors

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🧪 Training Methodology

🔧 Practical Application

• Real-world public data projects (e.g., COVID trends, product sales, survey results)

• Tool-based assignments and submissions

• Guided notebooks and feedback sessions

• Independent and team-based learning tasks

• Use of prompt engineering for faster output

🏢 Industry Integration

• UK-specific datasets where possible

• Data privacy and GDPR-compliant practices

• Case studies: UK universities, local businesses, job market trends

• Presentation formats suited for real business teams

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✅ Expected Outcomes

📊 Technical Expertise

• Proficiency in Python, SQL, Power BI, and Tableau

• End-to-end data project delivery

• Dashboard building and storytelling

• Git-based portfolio creation

• Real-data experience with analysis and modeling

🚀 Professional Growth

• Analytical thinking and pattern recognition

• Code documentation and collaboration

• Confidence to discuss data findings

• Time-bound project planning

• Communication of technical insights to non-technical stakeholders

🎯 Career Enhancement

• Portfolio on GitHub or LinkedIn

• Real project case study for interviews

• Exposure to in-demand data tools

• Internship certification

• Guidance for entry-level job readiness

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📝 Assessment Framework

📍 Continuous Evaluation

• Weekly tasks on Python, SQL, EDA

• Code reviews and GitHub submissions

• Dashboards and insights presentation

• Mini group task with peer feedback

🏅 Final Certification

• Capstone project using real dataset

• Power BI/Tableau dashboard

• GitHub repo with notebooks and visuals

• Presentation via Zoom or recorded walkthrough

• Final feedback + optional LinkedIn recommendation

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💼 Industry Relevance

All tools and case studies are aligned with UK business needs, such as data reporting, marketing analysis, student performance, and sales forecasting. The training helps students stand out for roles like Junior Data Analyst, Business Intelligence Intern, or Reporting Executive.

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🎓 Career Opportunities

• Data Analyst Intern

• Business Intelligence Assistant

• Junior Python Data Developer

• Reporting Analyst

• Market Research Analyst

• Junior Data Scientist (with ML exposure)