The data analytics roadmap
from beginner to expert

Stage 1: spreadsheets, SQL, Tableau and R, about six months part-time. Google's professional certificate teaches spreadsheets, SQL, Tableau and R through case studies and ends with a portfolio capstone. It's designed for beginners, takes about six months at ten hours a week and offers financial aid on Coursera.

Stage 2: proves Power BI, the BI tool many analyst posts name. Microsoft's Power BI Data Analyst exam proves you can prepare, model and visualise data and share reports securely. It's 100 minutes, priced by country, with free Microsoft Learn training and free online renewal every 12 months.

Stage 2: the alternative if your employers use Tableau. Tableau's analyst certification proves you can connect, prepare, analyse and visualise data in Tableau. It's most valuable where Tableau or Salesforce is the reporting standard; check Tableau's page for the current fee.

Stage 3: statistics, regression and Python, about six months part-time. Google's advanced certificate takes analysts into statistics, regression, machine learning and Python. It takes about six months at ten hours a week and is the natural next step after Google Data Analytics.

Stage 4: Python, SQL and ML for analysts moving into data science. IBM's professional certificate covers Python, SQL, data visualisation and machine learning basics with hands-on labs and a capstone. It takes about four months at ten hours a week, and financial aid is available on Coursera.

Stage 4: training and deploying models on Azure. Microsoft's associate exam proves you can train and deploy machine learning models with Azure Machine Learning. It's priced by country, needs Python and ML basics and renews free online each year.

Stage 4: Andrew Ng's ML course. Andrew Ng's specialization with Stanford teaches supervised and unsupervised learning, model evaluation and practical machine learning in Python. It takes about two months at ten hours a week and is widely regarded as the clearest structured start in ML.