1. Knowledge reserve
Both the fields of data analysis and data science are becoming richer and more specialized. Therefore, if you want to enter any of these two career fields, you need to be fully prepared to meet the needs of various actual jobs. Among them：
Data analysis
Related work in the field of data analysis often requires a prior understanding of RDBMS (relational database management system, relational database management system) and SQL for operating data structure patterns (structural patterns), as well as query development. Isotope, the location to choose from. Machine learning (machine learning, ML), artificial intelligence (artificial intelligence, AI), the development of custom algorithms, and data management knowledge around information collection and storage will also allow you to achieve a multiplier effect with half the effort. So you need to have relevant applied knowledge and skills in IT, computer science, mathematics, and statistics.
Data science
Data scientists often have an educational background in computer science, information technology, applied mathematics, or statistics.These core knowledge can prepare you to provide, collect, organize, process, and model business data.You can also understand and master professional knowledge about data visualization, API-based data collection and preparation, etc.These are very beneficial for you to carry out exploratory data analysis, follow and build models, and customize the design and testing of models.When you are in AI-based predictive modeling, the related skills in the ML and AI fields mentioned earlier are always unskilled.
2. Job roles and responsibilities
Whether it is a data analyst or a data scientist, their daily roles and job functions will vary depending on their professional level and the nature of the enterprise. Mainly reflected in：
Data analysis
Data analysts often focus on analyzing, visualizing, and mining business-specific data.Its roles and functions include：
Process, process, process, process and process (example) data
Exploratory data analysis of large data sets
Understand (contract, conversion, short-term care) management, and adopt a power model.
Using logical regression, KNN (K nearest neighbor, K-nearestne neighbor classification algorithm), that is, and decision tree, for
At the same time, at the same time, at the same time, at the same time, at the same time, at the same time, at the same time, at the same time, at the same time, at the same time, at the same time
Data model
Identify data models to make informed, data-supported predictions
Data science
Data science mainly involves gaining insights and drawing inferences from the contextual data of the business.Its roles and functions include：
- Collect and interpret data
- Identify relevant patterns in the data set
- Explain the data model.
- Use SQL, Python, SAS (statistics)
- Proficient in various predictive, normative, descriptive and diagnostic analysis tools
- [Medical] Homology analysis.
3. Basic skill set
Since both roles belong to professional fields, they require specific skill sets to be competent for basic daily tasks. Specifically：
Data analysis
In addition to intermediate statistical (intermediate statistical data) knowledge and advanced analytical skills with problem-solving skills, the skills of data analysts also include：
Power conveyor, power conveyor.
Use business intelligence tools to generate relevant reports
Compared with IT engineering experience, data analysts need to master the skills of learning statistics, database management, data modeling, and predictive analysis.
Data science
As a data scientist, in addition to being proficient in mathematics, advanced statistics, predictive modeling, and machine learning, you also need to master software knowledge in the following fields：
Hardup and Spark are a complete data model.
Related content of Sql, Nosql, and postgresql data models
Learn about the detailed data model.
4. Tool spectrum
As the saying goes: “If you want to do good, you must first use your tools.”Good tools are essential for both data analysis and data science.In order to facilitate download and selection, I have classified the tools currently on the market in a targeted manner.：
Data analysis
Data types: Splunk, QlikView, Power BI, and
Literature and Art: Talend
Data model: Spark
Literature and art: Microsoft Excel
Data science
Data models: SAS, KNIME, RapidMiner, PowerBI,
ETL Literature and Art category: Apache Kafka
Data model: Apache Hadoop, Spark
Data categories: Tableau, BigML, Trifacta, QlikView, MicroStrategy, and analysis
Literature and art: Microsoft Excel, Apache Flink, SAP Hana, MongoDB, MiniTab, and
Literature and art: R, Julia, and
General flow category: Can be used to calculate the surface flow of the model model
5. Career opportunities
With the previous information, you must want to know what popular careers in the field of data analysis and data science are available in the enterprise?
- Data analysis
- Business intelligence analyst
- Data analyst
- Quantitative analyst
- Data analysis consultant
- Operations analyst
- Marketing analyst
- Project manager
- It started.
- Transportation logistics expert
- Data science
- Data analyst
- Data engineer
- Database administrator
- Machine learning engineer
- Data scientist
- Data architect
- Statistician
- Business analyst
- Data and Analysis Manager
Summary
Based on the above comparison, it is not difficult to find that the requirements of enterprises for data scientists themselves and their skills are higher than those of ordinary data analysts. However, in the workplace, people usually start their careers as data analysts, and then gradually turn to the field of data science. For beginners, you can also start with related positions in data architecture and data engineering, continue to polish and experience yourself, and eventually become an expert in this field.
