Introduction au Data Analytics
Introduction au Data Analytics
Walid Bergaoui
Introduction to Data Analytics
Data is everywhere — in your sales, your customers, your surveys, the spreadsheets sitting idle on a hard drive. Yet as long as you can't read it, it stays silent. This training teaches you to make it speak. You start from scratch, with no technical background: you don't need to be a mathematician or a developer. By the end, you'll know how to take a raw dataset and draw a clear answer, a trend, a decision from it. You'll no longer be at the mercy of the numbers — you'll question them.
The approach is firmly hands-on. You begin by understanding what data analytics really is and how the cycle of an analysis unfolds, from the initial question through to the decision. You then learn to ask the right question, to collect and clean your data — the most overlooked and most decisive step — and to explore it in a spreadsheet to draw out the first insights. You discover the statistics that are useful day to day (mean, median, spread, correlation) without needless jargon, before learning to turn your results into accurate charts and readable dashboards. Each session draws on real-world examples and a mini case that you work through yourself.
By the end of the program, you walk away with a complete method and an analysis project carried out from start to finish: a question, cleaned data, an exploration, visualizations and a reasoned recommendation. You'll know how to spot a misleading chart, avoid the classic interpretation pitfalls and present your conclusions convincingly. It's the solid foundation on which to build more advanced skills in Business Intelligence, SQL or data science later on. But starting right now, you'll be the person able to answer, numbers in hand: "what does our data really say?"
What you walk away with
This introduction doesn't stop at concepts: it leaves you with tangible takeaways, ready to put to use.
A complete analysis project — a case carried out from the initial question to the final dashboard, which you can show and reuse.
A reusable method — the data analysis cycle, applicable to any dataset, in any line of business.
The vocabulary to go further — the right terms and the right instincts to move on smoothly to BI, SQL or data science.
Program — 12 one-hour sessions
A progressive, 100% hands-on journey: we start from the question, prepare and clean the data, explore it, measure it, visualize it, then tell the story of what it says. Each session ends with a mini case that you work through yourself.
Module 1 — Understanding data (sessions 1-3)
1. What is data analytics? — what data analysis is for, the major types of analysis (descriptive, diagnostic, predictive) and concrete examples in retail, finance and marketing.
2. The analysis cycle — the stages of a project from A to Z: ask the question, collect, clean, explore, visualize, decide. The common thread running through the whole training.
3. Asking the right question — turning a vague business need into a measurable question, and identifying the indicators (KPIs) that answer it.
Module 2 — Preparing the data (sessions 4-6)
4. Data sources and types — where to find data (spreadsheets, CSV files, exports), quantitative and qualitative data, and how to structure it properly.
5. Data cleaning — the step that changes everything: duplicates, missing values, entry errors, inconsistent formats. An analysis is only as good as the quality of its data.
6. Organizing and filtering in the spreadsheet — sorting, filtering and formatting a dataset in Excel or Google Sheets to make it usable.
Module 3 — Exploring and measuring (sessions 7-9)
7. Data exploration — getting to know a dataset: pivot tables, groupings and first findings.
8. Statistics that are useful day to day — mean, median, minimum, maximum, standard deviation: what each measure really tells you, and when to use it.
9. Trends and correlations — spotting a change over time, comparing groups, understanding correlation and the "correlation is not causation" trap.
Module 4 — Visualizing and storytelling (sessions 10-12)
10. Choosing the right chart — line, bar, pie, scatter plot: matching each message to the right visual and avoiding misleading charts.
11. Building a dashboard — bringing your key indicators together on a single clear, readable page, designed for the decision-maker.
12. Telling a story with data — data storytelling: presenting your results, framing a recommendation and wrapping up your complete analysis project.
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