Roadmap Materials: github.com/krishnaik06/Data-A...
#datanalystroadmap #krishnaik
Becoming a data analyst can be a rewarding and challenging career path, but the process of becoming one can be different for everyone and depend on where you are starting from, your level of education and experience, and what specific areas of data analysis you want to specialize in. However, here is a general roadmap that could be helpful for someone looking to become a data analyst in 2023:
Learn the basics of statistics and data analysis: It's important to have a good understanding of statistics and data analysis concepts such as probability, correlation, and regression. You can find many introductory statistics and data analysis resources available online, or through books or classes
Learn to use Excel and other data analysis tools: Excel is a commonly used tool for data analysis, and it's important to be proficient in it. You should also become familiar with other tools that are commonly used in data analysis, such as R, Python, and SQL
Learn data visualization: Data visualization is a key aspect of data analysis, and it's important to be able to effectively communicate your findings through visual means. Familiarize yourself with popular data visualization tools like Tableau, Power BI, or ggplot
Learn machine learning: Machine learning is an increasingly important aspect of data analysis and can be used to find patterns and insights in data. Learn the basics of machine learning, including supervised and unsupervised learning, as well as common libraries such as scikit-learn, TensorFlow, and Keras
Get real-world experience: It's important to get some hands-on experience working with data. You can do this by working on personal projects, participating in hackathons or Data Science competition or through internships or entry-level positions in the field.
Get a relevant educational degree or certification: Depending on the job and company you are interested in, an educational degree or certification can be helpful, such as a degree in statistics, computer science, engineering, economics or any other related field, or Data Analysis or Data Science certification.
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