Data Analyst Roadmap for Beginners
Hello there, Hope you all are doing good. I have been asked this question alot on Linkedin and I thought why not write an article for everyone and post it out there. I have followed exact steps and it will be great if you follow same to become data analyst.
Roadmap to become Data Analyst
- Python
Python is a popular programming language used in data analysis because of its simplicity, flexibility, and powerful libraries that can be used for data manipulation, analysis, and visualization. you need to know NUMPY, PANDAS, MATPLOTLIB, SEABORN. These libraries will help you alot in strengthening your analysis skills.
2. SQL
SQL (Structured Query Language) is a programming language used to manage and manipulate relational databases, which makes it a valuable tool for data analysts. ofc when you are working with data you need some tool to deal with database and manipulate it accordingly.
3. Excel
Most underrated skill but Excel is a popular spreadsheet software used by data analysts for various data analysis tasks. I have written one article too, please refer once about the the imp functionalities that every Data Analyst should know.
4. Visualization
Familiarize yourself with data visualization tools like Tableau, Power BI. Many organizations just need Matplotlib for Visualization, but it is really smart thing to be well-versed with any one of the tool either Tableau or Power BI.
Some Points to remember which will help you in accelerating your Data Analyst Journey-
- Learn about data cleaning and preprocessing techniques.
- Learn basics of stats that will help you.
- Practice data analysis by working on projects or participating in Kaggle competitions.
- Develop communication skills to effectively communicate your findings to stakeholders.
- Keep up with industry trends and new technologies.
- Continue to learn and improve your skills.
It’s important to note that becoming a data analyst is not a linear process, and it’s important to continually iterate and improve upon your skills. Additionally, being able to work collaboratively with other team members and being able to work with different types of data sets and business domains can be valuable.
