For most beginners, reaching the point where you can load a messy file, clean it in pandas and answer real questions from it takes a few months of steady practice. The honest answer depends on your hours per week, not on how smart you are. Someone who practises two hours every day will get there much faster than someone who studies hard one weekend a month.
A rough guide by hours per week
These are rough ranges, not promises. They assume you start with no coding and aim for the basic level described above, not expert level.
| Hours per week | What that looks like | Rough time to basic data work |
|---|---|---|
| About 3 to 5 | An evening or two after work | Often 6 months or more, with gaps that cost you |
| About 8 to 12 | An hour or more most days | Roughly 3 to 5 months |
| About 20 | A class plus daily practice | Roughly 1 to 2 months for the Python part |
Why such wide ranges? Because people start in different places. If you already use Excel formulas every day, you know what a filter, a lookup and a pivot table do. You only need to learn how to write them in code. If you have never used a spreadsheet, you are learning the ideas and the code together.
The order that works
Many beginners jump straight to machine learning tutorials. They copy code they do not understand and get stuck the first time something breaks. A slower order is actually faster:
- Plain Python first. Variables, if and else, loops, functions, lists and dictionaries. Then reading files and handling errors. Do not skip this.
- Jupyter notebooks. A notebook lets you run code one block at a time and see the result. Learn to keep it tidy so it runs from top to bottom.
- NumPy. A library for fast maths on whole columns of numbers. You only need the basics before moving on.
- pandas. The library for tables. Filtering, grouping, joining tables, and fixing missing values, duplicates, text and dates. This is where most of the real work happens.
- SQL. The language for asking questions of a database. Most company data lives in one, so analysts use SQL and Python together.
- Charts and basic statistics. Averages, spread, outliers, and choosing the right chart.
Machine learning comes after all of this. You cannot build a useful model on data you cannot clean.
What slows people down
- Watching instead of typing. Videos feel productive, but you learn by writing code that breaks and fixing it.
- Long gaps. A two-week break often means relearning the last two weeks.
- Tidy practice data only. Real files are messy. Practise on data with missing values and odd spellings.
- Letting AI run code you have not read. ChatGPT and Copilot are useful for explaining an error. If you paste their code without understanding it, you learn nothing and miss their mistakes.
How to know you are ready
A simple test: take a CSV file you have never seen. Load it in pandas, find and fix the missing values and wrong types, join it with a second table, and answer three questions a manager might ask. If you can do that without copying a tutorial step by step, your Python is good enough to move on to statistics and dashboards.
A simple weekly plan if you study alone
If you have about 8 to 10 hours a week, split them like this:
- Four short sessions of new material, about an hour each, on weekdays.
- One longer session at the weekend to work on a real dataset from start to finish.
- A few minutes after each session writing down what broke and how you fixed it.
Keep your notebooks in a GitHub repository from the first week. It shows your progress, and later it becomes something you can show an employer.
How we pace it in our course
In our Data Science and Machine Learning course, you have a 2-hour class Monday to Friday, plus about 2 hours of practice at home each day. Plain Python takes Week 1 and the first half of Week 2. NumPy comes next, then pandas and data cleaning in Week 3, and SQL with Python in Week 4. Statistics, Power BI and machine learning follow over the remaining weeks. You can read exactly what each week covers on our Python for data science page.
Whichever way you learn, the rule is the same. Steady hours every week beat long bursts with gaps in between.
