Digital Industry Analysis: how to become a Data Analyst?
We’ve been getting a lot of inquiries lately about data analysis and job search directions
Ask yourself if your background and experience are suitable for data analysis
How to change careers and become a Data Analyst
To this end, we sorted out your problems
This article will focus on the following
On the Data of Analytics, career planning, and industry prospects
How to become a data analyst’s relevant learning list these three points
I hope you found this helpful：
1. industry prospect
IBM forecasts that demand for Digital jobs will jump 28% over the next three years.
By 2020, the number of all U.S. Digital jobs will increase by 360,000, bringing the total to 2.7 million.
In addition, McKinsey predicts that by 2018, the United States may face a gap of 140,000 to 190,000 “people with in-depth analytical skills” and 1.5 million “managers and analysts who can use big data analysis to make effective decisions.”
In finance, Internet technology, media, and retail companies, data can be used for a wide range of purposes, not only to analyze customer behavior but also to maximize customer value.
2. Career planning
About the career planning of job-hunting in the direction of data analysis, we will help you to understand from different fields and different routes:
Different areas
“Domain” is often ignored by many newcomers to data analysis, in fact, data analysis will not be separated from the existence of a business. The industry you enter will largely determine your initial skill tree and skill points.
For example, the risk control model/anti-fraud model in the financial field, the life cycle in the marketing field, the estimated click-through rate in the advertising field, etc., and the recommendation system and user portrait system in the e-commerce industry have their own characteristics.
Different routes
The career path of data analysis can be roughly divided into four directions:
- The data analysis
- Data mining
- Data products
- Data engineering,
1. Data analysis/data operation/business analysis
This is a business-oriented data analyst. Most people start their own data path from this position, which is also the position with the largest base.
- Responsible for and support related reports of each department;
- Establish and optimize the index system;
- Monitor data fluctuations and anomalies to identify problems;
- Optimize and drive business, and drive digital operation;
- Identify growth markets or product optimizations;
- Output thematic analysis report;
2. Data mining/algorithm expert
These are technical data posts. Some are classified in the r&d department, while others are set up as separate data departments. Data mining engineers are expected to have strong statistical and mathematical skills as well as programming skills.
The closed-loop of common data mining projects is as follows:
- Define the problem
- Data extraction
- Data cleaning
- Feature selection/feature engineering
- The data model
- Data validation
- The iterative optimization
3. Data product manager
This position is relatively new, and it has two understandings: one is the Product Manager with strong data analysis ability, the other is the planner of the company’s data products.
The former, data-oriented optimization and product improvement. In companies with strong products, data analysis falls into the product division, and even operations fall into the product division. This type of product manager has more opportunities to get in touch with the business, which is typical of doing the analyst’s job by the way. They will use different data sources to analyze and mine the user’s behavior characteristics to improve the product. The most typical scenario is the AB test. Everything from page layout to path planning to the color and style of buttons can be evaluated using data metrics.
The latter is a true data product manager. As the company grows larger and stronger, the amount of data increases day by day. At this time, there will be many data-related product projects, including big data platform, embedded point acquisition system, Business Intelligence, recommendation system, advertising platform, etc. These are, of course, products, which naturally require refining requirements, design, planning, project scheduling, and even landing.
4. Data engineer
Data engineers are actually more technical, which is a broader career path for programmers. In many small and medium-sized companies, on the one hand, the data is disordered, missing and original, on the other hand, various business reports need to be processed. At this point, the analyst can only do various tasks by himself, as well as data cleaning +ETL(extract conversion loading)+ business intelligence
How to become a data analyst?
When data analysis and data mining technology become an important driving force in the business field, the requirements for practitioners are also relatively increased.
1. Data analysis basis
Aside from the basic understanding of business, the first thing to learn data analysis well is to understand statistics. Statistical analysis is the basis and soul of data analysis. Several core contents of statistical analysis:
Descriptive statistics, statistical inference, probability theory;
Sampling, distribution, estimation, confidence interval, hypothesis testing;
Linear regression, time series;
2. Data analysis tools
According to the article published by the author Data Castle in CSDN, the skills required by enterprises for data analysis can be summarized as follows:
- The basic operation of SQL database, basic data management
- Basic data extraction, analysis and presentation using Excel/SQL
- Able to perform data analysis in a scripting language, Python or R
- Additional ability to access external data, such as crawlers or familiarity with public data sets
- Basic data visualization skills and ability to write data reports
- Familiar with common data mining algorithms: regression analysis, decision tree, classification, clustering
1. Excel
As a great spreadsheet tool for Microsoft, Excel is also something that data analysts need to know. Because many non-technical people in other parts of the company don’t use programming tools, they use relatively simple Excel to process reports. At this time, you may need to do some data analysis and feedback in Excel, but you don’t need to go too far, just master the core functions, such as:
- Add and delete
- The use of various common functions
- The creation of various basic ICONS
- Pivot tables, etc
2. R/Python
R language is designed for statistics, is one of the mainstream data application software, is very suitable for data analysis and data mining. As a popular language, Python is compatible with most of the new data science tools, and it is also the best choice for many people due to its excellent scientific computing library.
3. The SQL
As the most classic database tool, SQL provides the possibility for the storage and management of massive data and greatly improves the efficiency of data extraction. You need to master the following skills:
1) extract data under specific circumstances: the data in the enterprise database must be large and complex, and you need to extract the part you need. For example, you can extract all the sales data for 2018 according to your needs, the data of the top 50 products sold this year, and the consumption data of users in the region… , SQL can do this for you with simple commands.
2) database add, delete, check, change: these are the most basic operation of the database, but as long as the simple command can be achieved, so you only need to remember the command.
3) data grouping and aggregation, how to establish the relationship between multiple tables: this part is the advanced operation of SQL, the relationship between multiple tables, which is very useful when you are dealing with multi-dimensions and multiple data sets, which also allows you to deal with more complex data.
