Turn a messy export
into a number a manager trusts.

It is Monday morning at Bluewick Studio, a small marketing agency with nine clients. A requester named Imani sends you a spreadsheet export and one line: the client wants to know why last month's ad spend did not turn into sales. You open the file. The dates are text, two rows are duplicated, and the total at the bottom does not match the invoice. You do not guess. You clean the export in a copy, keep the raw file untouched, and build a pivot table that reconciles to the invoice total. Then you write one sentence at the top of a chart: spend rose while sales fell. That sentence is the finding. That is the work. You are the person who turns a messy export into a number a manager can act on. I am an experienced analyst, and in this course I will show you how the job is done on a real queue, with real requesters, at a fictional agency called Bluewick Studio. Nine clients, six requesters, the same names all the way through. You will learn spreadsheets, SQL and a dashboard tool, and you will practise until the number you send is the number that reconciles. Training and certification do not guarantee employment. What they can do is teach you the habits that make a junior analyst useful on day one. Let me give you the size of this work, and I will be careful, because the government does not count data analyst as its own occupation. The closest measures are operations research analysts and data scientists, and the course says so plainly. The US Bureau of Labor Statistics puts the median pay for operations research analysts at eighty-eight thousand nine hundred and forty dollars a year in the latest handbook, with about seven thousand five hundred openings a year on average over the decade. For data scientists, the same source puts the median at one hundred and twenty thousand two hundred and thirty dollars a year, with about twenty-four thousand eight hundred openings a year on average, and the handbook lists data analyst as a related title. That is the pay and the demand from the government's own numbers. A demand check run before this track was built found sixty thousand five hundred monthly US searches for data analyst jobs, fourteen thousand eight hundred for remote data analyst jobs, and nine thousand nine hundred for entry-level data analyst roles. Those are searches, not jobs, and I will not pretend otherwise. What they show is a steady appetite for people who can turn an export into a number a manager can act on. Training and certification do not guarantee employment. What they can do is give you the habits and the proof of work that make you worth interviewing. So what is the job, really? Imani sends a request. You read it twice before you touch anything, because the request is not the task. The task is the decision behind it. She needs to know why ad spend did not turn into sales, and she needs it before the Friday freeze. You find the metric, the dimension and the KPI, and you say them back to her in one line so you are both talking about the same number. Then you work. You clean the export in a copy, never the raw file. You build the pivot, you check the total against the invoice, you write the chart title as the finding. You hand back a one-page report with the number and the sentence that explains it. That is the whole job: a request, a number, a decision it serves. Now the line the role never crosses. You do not decide. You do not tell Imani to cut the campaign or move the budget. You give her the number, the caveat, and where it came from, and she decides. You also never invent a figure to fill a gap, and you never send a number you cannot reconcile to its source. If it does not tie out, you say so before someone else finds it. That restraint is what makes a junior analyst trusted with real client data. Training and certification do not guarantee employment. What they can do is build the habit of stopping at the line. This course is for a particular kind of beginner, and I want to be honest about who it is not for. It is for you if you are starting out or changing careers and you want to turn messy exports into numbers a manager can act on. You may be aiming at a remote analyst role, or at an employer here in West Africa. You do not need a maths degree. You need patience with a spreadsheet and the willingness to check your own total before you send it. It is not for you if you want to skip the cleaning and jump straight to the dashboard. The cleaning is the job. It is not for you if you want to be told what the number means and never defend it, because every figure you send on the Bluewick queue is checked against its source. It is not for you if you want a certificate that stands in for practice. The practicum is timed, and passing needs zero critical errors. And it is not for you if you are looking for a guarantee. Training and certification do not guarantee employment. What this course can do is put you in front of the same kind of request, from the same requesters, week after week, until the habits are yours. If that sounds like work you would enjoy doing carefully, stay with me. If you want a shortcut, this is not it, and I would rather tell you now. Here is how the ten weeks actually run, because the shape matters more than the promise. Weeks one to eight are lessons and quizzes. Week one, what a data analyst actually does, the request and the decision it serves. Week two, cleaning data without breaking it, raw, staging, final. Week three, averages, rates, pivots and trends. Week four, SQL, reading the database behind the CRM. Week five, charts and dashboards. Week six, the weekly report and the honest summary. Week seven, data you must protect and requests you must push back on. Week eight, working with requesters and the capstone brief. Alongside those eight weeks you get one live class a week, eight in total, where you bring your own work and we go through it together. All of it happens inside Bluewick Studio, the fictional agency, nine clients and six requesters