The rapid development of big data makes more and more people want to join the industry, after all, data analysis can explore the potential role of big data. The industry’s huge demand and lucrative rewards attract a large number of people who want to become data analysts. If you want to become a data analyst, or a senior data analyst in a business, you technically need to understand some of the learning paths of becoming a data analyst. There are mainly 6 learning paths as follows:
Statistics, data, machine learning
You’ll learn a little bit about math in college classes, and you’ll learn more if you’re majoring in math and science. But these are not enough, you need to go to the system of learning, xiaobian recommend you to khan Academy and MIT online platform, there are open courses. In addition, it is recommended to systematically learn Udacity Openintro for statistical knowledge.
2, code,
From programming basics to end-to-end development, some technical languages of commercial software such as R, Python, SAS, and SPSS, as well as in-depth interactive learning, of which you need to be proficient at least some, and preferably Python, if you want to become a professional.
3. Understand the database
Data analysis is mostly practical. Enterprise data is often stored in MySQL, Oracle, Postgres, MonogoDB, Cassandra, etc., so it takes some time and effort to get to know and understand these databases.
4. Data management, data visualization and data reporting
(1) Data management includes grid processing of data, CLEANING of ETL, etc., to make data more accurate and clear before analysis, such as DataWrangler.
(2) Data visualization is the front-end representation of data analysis, such as Tableau and Spotfire, to display data in a clearer and more intuitive way.
(3) Data reports are presented by different tools, which can be combined with data visualization, but in practical applications, most of them are presented by PPT.
Many companies in the market are using business intelligence tools, such as Smart Software Smartbi. BI tools are more business-friendly data connectivity, data processing, and visualization tools than the pure tools above.
5. Big Data
Big data is an inevitable trend of future development. There are many kinds of big data technologies, such as Hadoop, MapReduce, Spark and Smart software Smartbi. If you learn one more, your level will be different from others.
Accumulate experience and learn from your peers
All the above are theoretical knowledge and tools, but practice is the only criterion for testing truth. No matter you are a novice in data analysis or a student majoring in data mining and analysis, we all hope that you can participate in more competitions while learning the above content, and accumulate more experience in this field like professional masters in the same field. Of course, proficiency in Excel and PPT is also a must.