[PYTHON] What skills should I study as a data analyst from inexperienced?
Original article: https://www.octoparse.jp/blog/what-skills-should-you-study-for-a-data-analyst-from-inexperience/
"I'm thinking of becoming a data analyst, but I'm worried about its future. Also, are you suitable for a data analyst? What skills do you need? I want to do my best, but where should I start? Okay ... if you know, please let me know! "
This article answers these questions. From now on, I will explain what you need to know before studying a data analyst by yourself, the skills required for it, and the study method.
By reading this article, you will be able to imagine ** "the work content of a data analyst, its future and necessary skills, learning resources" **.
Let's take a look.
1. 1. What is a data analyst?
Simply put, a data analyst is someone who specializes in analyzing data for a company's challenges.
Data analysts place more importance on "utilization of data" than data scientists, and data analysis proposes future forecasts and solutions to problems that can be seen from there.
1.1 Job description
As I said before, specifically, the main job of a data analyst is to analyze the huge amount of data, find out consumer behavior and market trends from it, make hypotheses, and solve problems. It is to propose means and to help improve services. Of course, there are differences in each analysis method depending on the industry.
1.2 types
The data analysts posted on the recruitment site are mainly classified into "consulting type" and "engineer type".
A consulting data analyst's job is mainly to formulate hypotheses, analyze them through data commentary, and propose commentary measures for corporate issues. I mainly belong to marketing companies and management consulting companies.
On the other hand, the job of an engineer-type data analyst is to analyze customer behavior patterns using techniques such as machine learning and data mining, and to provide data that can be used to develop and improve products and services. ..
2. Current status and future potential of data analysts
An era in which digital information is flooding the world and the Internet is exploding. Furthermore, data exists everywhere, and it is no exaggeration to say that it is an "information explosion." In such an era, data science, which handles a large amount of data and finds business value, is gaining increasing attention year by year.
Furthermore, in the future, as AI permeates society, it is easy to be deprived of it by occupations that are mostly devoted to routine work. On the other hand, professions that are difficult to deprive tend to require complex intelligence and complex judgments. Obviously, data analysts are one of those hard-to-find professions.
Currently, the demand for data analysts in a wide range of industries such as IT, WEB, and finance has greatly exceeded the supply. There is a so-called shortage of job offers. As a result, some companies may even hire inexperienced but highly motivated job seekers with a background in data analysis. So now I have a chance to study data analysts and change jobs to success.
3. 3. What kind of person is suitable for a data analyst?
Now, you may be wondering who the data analysts are for.
The images below are my own opinions, so please refer to them.
Four. Skills required for data analysts
4.1. Statistical analysis
Statistical analysis is an indispensable basic skill for work related to data analysis. You can also study using statistical analysis software such as SPSS or SAS. 4.2. SQL
For anyone who wants to be an engineered data analyst, the SQL language will have to be learned. Data analysts, web professionals, product managers, especially the Internet industry, need to have knowledge of SQL.
4.3．Python
Python mainly requires you to learn basic syntax, pandas operations, numpy operations, sklearn modeling, how to crawl data with a web crawler in Python, and more.
In addition, instead of Python, ** scraping tool ** that can easily acquire data has also appeared. A scraping tool called ** Octoparse ** is a tool that makes it easier to acquire data. If you master ** Octoparse **, you can get the same effect as data acquisition in Python.
4.4. R language
It is no exaggeration to say that the R language exists for statistics. You need to master the basic syntax, data management, data mining modeling, and evaluation of the R language.
4.5. Data visualization
If you are a beginner in data analysis, the more important thing is to "touch the data yourself". BI tools are generally used for data analysis, and the analysis is generally performed through data visualization. Once you have the data, try analyzing it with the tools that are best for you from the BI tools recommended for 2020.
Five. Learning resources to become a data analyst
5.1. Statistical analysis
5.2. SQL
https://www.classcentral.com/search?q=sql
https://employment.en-japan.com/engineerhub/entry/2019/11/05/103000
5.3．Python
https://www.classcentral.com/search?q=python
5.4. R language
https://udemy.benesse.co.jp/ai/r-language.html
https://www.classcentral.com/search?q=r-programming
5.5. Data visualization
https://www.classcentral.com/subject/data-visualization
What do you think. Can you imagine what you should do before you self-taught a data analyst?
Recommended Posts