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Data Essentials: master data and AI fundamentals
To contribute to an AI project, you need to distinguish genuine capabilities from hype. Connect data, models and use cases to understand choices and limitations. Develop a framework that makes discussions with technical teams more precise and decisions better supported.
- Duration
- 9 days 63 hours
- Code
- DATA010FR Code
Presentation
Data is the new fuel of business, but knowing how to use it remains rare. This Data Essentials course develops your understanding of data and artificial intelligence. In 9 days, move from an Excel file to actionable predictions without writing complex code.
Through learning by doing, with 70% practical work, you will learn through action. Use SQL to extract data and build a dashboard to make it visual and understandable. Then use Python, with ready-to-use Pandas and Scikit-learn recipes, to create your own machine learning models for churn, scoring and recommendations.
By the end of the course, you will have a portfolio of 3 real projects, deployed on GitHub or through Tableau. This provides tangible evidence for employers or recruiters that you can turn raw data into strategic decisions and work confidently with data.
Objectives
By the end of this course, you will be able to:
- extract and manipulate data with SQL and understand cloud infrastructure;
- create interactive visual dashboards with Tableau Public;
- analyse datasets and conduct statistical tests, including A/B testing, with Python;
- create and train regression and classification machine learning models to predict trends;
- present analytical results clearly to guide decision-making.
Program
Days 1 and 2: Mastering data visualisation with Tableau Public
- Exploring Tableau's interface and connecting to different data sources.
- Preparing data for visual analysis.
- Creating interactive dashboards to manage business activity.
- Advanced data exploration through data mining to uncover hidden trends.
Days 3 and 4: Using databases, SQL and the cloud
- Understanding data infrastructure, including data warehouses, data lakes and ETL.
- Writing basic SQL queries: filtering with WHERE, sorting with ORDER BY and removing duplicates with DISTINCT.
- Building complex SQL queries: JOIN operations, SUM and AVG aggregations, and segmentation with GROUP BY and HAVING.
- Introduction to cloud computing services through Google Cloud Platform.
Days 5 and 6: Analysing data with Python and statistics
- Learning Python algorithmic fundamentals: variables, conditions and loops.
- Manipulating and cleaning datasets with Pandas.
- Mastering statistical fundamentals: mean, median, standard deviation and Z-score.
- Designing and analysing A/B tests to optimise websites or applications.
Days 7 and 8: Deploying machine learning models
- Understanding key concepts and the machine learning project lifecycle.
- Developing linear regression models with Scikit-learn.
- Implementing classification algorithms, decision trees and random forests.
- Visualising model performance with Matplotlib.
Day 9: Final project and presentation: hackathon
- Select a real dataset, such as Boston Marathon, Spotify or recruitment data.
- Independently conduct exploratory analysis and clean the data.
- Train a predictive model to address a business problem.
- Present results through visual storytelling to the assessment panel.
Audience
This course offers an ideal starting point for entering the technology sector, including for:
- career changers seeking to acquire data skills quickly without a lengthy programme;
- analysts and research officers seeking to modernise their tools by moving from Excel to SQL/Python to process larger data volumes;
- managers and project managers seeking to understand technical teams' language and lead data projects.
Prerequisites
No specific technical knowledge or professional experience is required.
Teaching and assessment methods
- Initial skills assessment
- Training materials provided to participants
- Continuous assessment throughout the course
- End-of-course feedback questionnaire
- Combination of theory and practical application
- Attendance records
- Post-course follow-up evaluation
- Practical exercises
Course highlights
- Practical learning: 70%: spend most of your time practising in small groups on real cases, rather than attending theoretical lectures.
- Modern technical stack: learn tools used by data science experts, including Python, SQL, Tableau and Scikit-learn.
- Professional portfolio: leave with completed projects to present in interviews and demonstrate your capabilities immediately.
- Fully accessible: a programme designed to make data accessible without prior programming knowledge.
Dates and sessions
Choose the date and delivery format that suit you.
No upcoming sessions are currently available.
Tableau® is a registered trademark of Tableau Software, LLC (www.tableau.com).
Python™ is a registered trademark of the Python Software Foundation (www.python.org).
Google Cloud Platform™ is a trademark of Google LLC (cloud.google.com).
Other product and company names mentioned belong to their respective owners.