A step-by-step plan from Excel to SQL and from Python to a portfolio for someone with no software background, with starting salary expectations.
Data analysis has become one of the fields career changers ask about most in recent years. And you do not have to be a programmer; what you need is analytical thinking, regular study and the right tools. For someone who can set aside 8-10 hours a week, a realistic six-month roadmap looks like this.
Months 1 and 2: Core tools
In the first two months concentrate on Excel/Google Sheets and SQL. Being comfortable with pivot tables, XLOOKUP and basic chart creation makes a quick difference in your first job. In SQL learn SELECT, JOIN and GROUP BY statements. The goal at this stage is to be able to ask a question of a dataset and answer it with a table.
Months 3 and 4: Python and visualisation
The basics of Python, data cleaning with Pandas and visualisation with Matplotlib are the focus of this period. Do one small analysis a week on a real dataset (for example from open data portals). First portfolio project: customer segmentation on an e-commerce dataset.
Month 5: Statistics and storytelling
Besides mean, median and standard deviation you should know hypothesis testing and correlation. More important, though, is being able to explain results to a non-technical manager. Practise explaining one finding on a single slide.
Month 6: Portfolio, CV and applications
Prepare a portfolio with 2-3 projects (GitHub or a personal site), a clear headline on LinkedIn and an ATS-friendly CV. Make 5-10 applications a week and prepare for interviews.
Starting salary and expectations
Starting salaries for data analyst roles vary with company size and city; the first job is usually a junior analyst or reporting specialist role. Because salary figures change constantly, follow current job postings and industry salary reports. Kıvılcım Academy does not give a job guarantee; however, 64% of our graduates moved to a new position within six months.
Most common mistakes
- Watching too many courses and not doing projects
- Learning only tools and not understanding the business question
- Postponing the portfolio
Where to start?
The Data Analyst Path structures this plan with Python, SQL and Machine Learning courses, live lessons and mentoring support. You can see the curriculum and student projects at the free introduction webinar.
Sample weekly study plan
Setting aside one weekday evening for an SQL or Python lesson, one evening for a practice assignment and 3 hours at the weekend for project work creates a consistent pace of about 9 hours in total. What matters is producing one concrete output every week: a query, a chart, a short report. These outputs make up the core of your portfolio by month six.
Conclusion
Data analysis is not a quick switch but a regular investment. With the right plan, live support and real projects, you can build the foundation needed for a first position within six months.
