SPSS is the earliest statistical software that uses graphical menu-driven interface in the world. It displays almost all functions in a unified and standardized interface. SPSS uses a form similar to EXCEL to input and manage data, and its data interface is universal, so it can easily read data from other databases. Its statistical process includes commonly used and mature statistical processes, which can fully meet most work needs.
MATLAB is a commercial mathematical software produced by MathWorks Company in the United States, which is used for algorithm development, data visualization, data analysis and numerical calculation in advanced technical computing language and interactive environment.
Its advantages are as follows:
1, efficient numerical calculation and symbolic calculation function, which can liberate users from complicated mathematical operation analysis;
2. It has complete graphics processing function, realizing visualization of calculation results and program design;
3. Friendly user interface and naturalized language close to mathematical expressions are convenient for scholars to learn and master;
4. Functional application toolboxes (such as signal processing toolboxes and communication toolboxes) provide users with a large number of convenient and practical processing tools.
But this software is not easy to use and is not recommended by non-professionals.
SAS organically combines data access, management, analysis and display. Its function is very powerful, and the statistical method is neat, complete and novel. It consists of dozens of special modules, and its functions include data access, data storage and management, application development, graphic processing, data analysis, report preparation, operational research methods, econometrics and forecasting. SAS system can be basically divided into four parts: SAS database part; SAS analysis core; SAS development demonstration tools; SAS support for distributed processing mode and its data warehouse design. However, the use of this software requires certain professional knowledge, which is not recommended by non-professionals.
Finereport-like EXCEL design mode, EXCEL+ bound data rows "form holds multi-table and cross-table calculations, which is perfectly compatible with EXCEL formulas. Users can design arbitrary complex tables with what you see is what you get, and easily realize Chinese-style complex reports. Its functions are also very rich, such as data support and integration, summary report, data map, Flash printing, interactive analysis and so on.
What's the difference between python and R in data analysis? I have been using python for three years, and I am deeply impressed by its simple, easy-to-read and powerful library. I have fallen in love with Python. Its pythonic language features are extremely friendly to people. It can be said that it is not difficult for a person who doesn't know programming language at all to understand python language.
What are the good textbooks or tutorials for data analysis in Python? Learning Python programming language is the best choice for everyone to enter the programming world. It is very important to learn any language, basic knowledge or basic skills. Finding a teacher or senior with rich programming experience will make you take fewer detours and make much faster progress. No matter what the purpose of our study is, we have to say that Python is really an excellent programming language, which is worth your time to learn. When choosing training, we must compare teaching, teachers, projects and employment. Choose carefully in many ways.
Big Data Analysis: What software is suitable for this kind of analysis? Lingjiu Software believes that big data analysis implies several requirements:
1 big data storage and calculation, and the software in this area is open source hadoop+HBase. With this system, we can build a decentralized storage and computing system as small as tens of TB and as large as PB. The hardware uses a blade server and an onboard hard disk to store data.
2 main data query requirements. Queries about big data often appear in some log records. Traditional storage uses expensive commercial database systems. Therefore, the log information of large enterprises, such as customer records of banks, is stored offline, which is very troublesome to query. Using Hadoop/HBase, a Pb-level cluster query system can be built, and a better query experience can be obtained through the secondary index system.
For data mining, the Mahout mining algorithm library can be used, and if there is a mining algorithm, it can also be written directly by Mr. These mining programs are all implemented in the Hadoop system mentioned above to realize distributed analysis.
If there is demand, you may need to consider real-time analysis, which requires Spark, a memory computing framework.
What courses are there in data analysis training? The training courses are as follows:
First, the frontier knowledge of big data and hadoop introduction
Get started with zero foundation, understand the historical background and development direction of big data, and master two installation configurations of hadoop.
Second, Hadoop has advanced deployment.
Proficient in hadoop cluster construction; The distributed file system HDFS based on Hadoop architecture is deeply analyzed.
Third, the Java Foundation.
Understand the basic idea of java programming, skillfully use eclipse for simple java programming, skillfully use jar files, understand the principles of database management systems such as mysql, and understand the program development process based on web.
Fourthly, MapReduce theory and actual combat.
Familiar with the working principle and application of mapreduce, familiar with the basic programming of MapReduce, and master the design and writing of projects based on MapReduce according to the goal of big data analysis.
Verb (abbreviation of verb) hadoop+Mahout big data analysis
Master the usage scenarios of big data analysis methods based on hadoop+mahout, and skillfully use mahout's mature algorithms to analyze big data in specific scenarios.
Sixth, Hbase theory and actual combat
Master the data storage and project actual combat of hbase, and master the installation, configuration and use scenarios of Spark and Hive.
Seven, Spark big data analysis
Installation, configuration and usage scenarios of Spark and Hive, skillfully using Spark's mature algorithm to analyze big data of specific scenarios.
Eight, big data learning comprehensive knowledge reserve
Statistics: multivariate statistical analysis, applied regression
Computer: R, python, SQL, data analysis, machine learning.
Matlab and mathematica also need to be mastered. The former has great advantages in practical engineering application and simulation analysis, while the latter is excellent in calculation function and mathematical model analysis. Mutual subsidies can complement each other.
What are the applications of business data analysis? Where there is information, there is information, and where there is demand, data analysis is needed. In this way, it is widely used. For example, I have seen a bank use FineBI to focus on finding target customers and maintaining and redeveloping existing customers. It mainly adopts differentiated sales strategies for various high-quality customer groups and provides personalized financial products and services. There are many such applications, and you can search related cases.
What are the commonly used methods for data analysis of online market research? Spss is generally used for frequency analysis, correlation analysis, factor analysis, cluster analysis and so on.
What are the common methods and theories of big data analysis? PEST analysis theory is mainly used for industry analysis. PEST analysis method is used to analyze the macro environment. Macro-environment, also known as macro-environment, refers to various macro-forces that affect all industries and enterprises.
When analyzing macro-environmental factors, because different industries and enterprises have their own characteristics and business needs, the specific content of the analysis will be different, but in general, we should analyze the four external environmental factors that affect enterprises: politics, economy, technology and society.
2. Logical tree analysis method
Logical tree analysis theory course is used for special analysis of business problems. Logical tree is also called problem tree, deduction tree or decomposition tree. Logical tree is one of the most commonly used tools to analyze problems. It lists all the sub-problems of the problem hierarchically, starting from the highest level and gradually expanding the suite downwards.
Think of a known problem as a tree trunk, and then start to think about which related problems this problem is related to.
(Disadvantages: Relevant issues involved in logical tree analysis may be omitted. )
For beginners, you can consider the above two points.
What are the bases and methods of data analysis in statistics? By using probability theory to establish a mathematical model, collect the data of the observed system, make quantitative analysis and summary, and then make inference and prediction. The research of statistical basic theory provides basis and reference for relevant decision-making, including: probability limit theory and its application in statistics, tree probability, Banach space probability, stochastic partial differential equation, Poisson approximation, stochastic network, Markov process and field theory, Markov convergence speed, Brownian motion and partial differential equation, limit of spatial branching population, large deviation and random mean value, cross-border problems in sequence analysis and time series analysis, One-to-one correspondence between Markov process and Dirichlet table, central limit theorem in function estimation, stability of limit theorem, causality and statistical inference, prediction inference, network inference, likelihood, M- estimator and maximum likelihood estimation, accurate approximation in independent variable model, adaptive method in independent variable estimation, new content in multivariate analysis, theory and application of time series, nonlinear time series, deterministic model and randomness in time series.
How to analyze IT operation and maintenance data? The so-called IT operation and maintenance management refers to the comprehensive management of IT such as hard execution environment (software environment, network environment, etc.). ), the IT business system and IT operation and maintenance personnel are in charge of the IT department of the unit.
IT operation and maintenance management mainly includes eight aspects:
1 equipment management.
Monitor and manage the execution status of network devices, server devices and operating systems.
2 application services.
Monitor and manage all kinds of application support software, such as database, middleware, groupware and all kinds of general or special services, such as email system, DNS, Web, etc.
3 data storage.
Unified storage, backup and recovery of system and business data.
4 business.
Including the monitoring and management of the enterprise's own core business system. For business management, we mainly focus on CSF (key success factor) and KPI (key performance indicator) of business system.
5 directory content.
This part is mainly about the content management and public information management that enterprises need to publish uniformly or customize for people.
6 resource assets.
Managing the resource assets of IT system in an enterprise can be physical or logical, and can interact with the financial department of the enterprise.
7 information security.
The international standard of information security management is ISO 17799, which covers ten control aspects, 36 control objectives and 127 control methods of information security management, such as enterprise security organization, asset classification and control, personnel safety, physical and environmental safety, communication and operation safety, access control and business continuity management.
8 daily work.
This part is mainly used to standardize and clarify the job responsibilities and work arrangements of operation and maintenance personnel, provide quantitative basis for performance appraisal, and provide a means to accumulate and enjoy experience and knowledge.