Free courses in the order an analyst actually picks the tools up: spreadsheets first, because that is where the data is, then SQL to get it out of a database, then Python with NumPy and Pandas to do what a spreadsheet cannot. Every lesson runs in your browser and the Excel, SQL, Python, Pandas and R courses end with a free certificate.
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A path certificate at the end, plus 10 along the wayEach step is an existing Coddy course. Take them in order, or jump into the one you are missing - your progress is saved per course either way.
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Double or Nothing
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Promotion zoneRead, listen, test yourself, ask the AI, or look up anything you've already covered. Every lesson meets you where you are.
A variable is a named container that stores a value you can reference later in your program.
In Python, you create one by writing the name, an equals sign, then the value you want to store.
The value can change over time. Reassigning the name simply points it to a new value.
Earn certificates for every course you complete. Add them to your LinkedIn profile and resume to showcase your coding expertise to employers.
Certificate of CompletionPython Fundamentals
Add to LinkedInLearn the three tools every analyst posting lists - spreadsheets, SQL and one of Python or R - in that order, then build two or three analyses of real public datasets that answer a question someone would actually ask. That is this path plus a small portfolio. Experience with data is easier to manufacture than most careers: every public dataset is a project waiting to be written up.
Spreadsheets for quick work and sharing, SQL for getting data out of a database, a programming language for anything a spreadsheet cannot do (cleaning, joining large tables, repeatable pipelines), enough statistics to know what a mean hides, and the writing skills to explain a chart to someone who will not read the numbers. This path covers the first three directly; the last two you practise on every analysis you write up.
Excel first, SQL second, Python third, which is the order of this path. Spreadsheets teach you to think in tables at no cost. SQL is the single most requested analyst skill and takes three weeks to become useful. Python only pays off once you have data big or messy enough that a spreadsheet struggles, so learning it first means learning it without a reason.
Python, for most analyst jobs, because it is asked for far more often and the same skill transfers into automation and engineering work. R remains the language of statistics departments and some research and pharma teams. This path teaches Python with NumPy and Pandas and keeps R as an optional final step, so you can add it when a job asks for it.
Around four to six months at an hour a day to reach a junior level. On this path Excel takes two to three weeks, SQL three to four, the Python basics six, and NumPy and Pandas two to three weeks together. The remaining time is what matters most: writing up analyses of real datasets until you can explain a result to someone who did not ask for the method.
No, and we would rather say so than pad the page. Dashboarding tools are a common job requirement and a short study once you can already shape the data underneath them, which is what this path teaches. When a visualization course ships on Coddy it joins this path as a new step.
On its own, little; attached to skills a hiring manager can test, quite a lot. The Excel, SQL, Python, Pandas and R courses here each issue a free certificate at its own verifiable URL, and each names one concrete skill. Those are the lines an applicant tracking system searches for, and the three questions an interviewer will ask you to prove. Finish every step and the path adds one certificate that lists them all.
Yes. Finish every step and you get one free certificate for the whole Data Analyst path. It lists each course you finished on the way, and links each course's own certificate, so anyone can check every skill it names. You can add it to your LinkedIn profile. It is a certificate of completion from Coddy, not an accredited qualification.
