In an environment where digital technologies have profoundly redefined consumption and production patterns, data analysis is a formidable weapon for boosting business activity. Companies, regardless of their sector of activity, now have access to an enormous amount of data that must be interrogated in the right way to optimise decision-making. Data Analysis is the process, now highly computerised, which promotes the transformation and modelling of data to offer a better understanding of the latter. Jedha, the French leader in data analysis training, reveals here the methods and technologies of data analysis.
What is a data analysis method?
In Data Analysis, an analysis method is a statistical, computer or AI-derived process that extracts the maximum amount of actionable information from a group of available data. By cleaning, transforming and modelling the data, the analysis method seeks to establish meaningful statistical relationships between them. The aim is to describe the main statistical information conveyed by the variables involved to facilitate certain tasks. A successful analysis of the variables involved can be very useful for market research, new product development or customer profiling.
The appropriate choice of analysis method takes into account the quality of the data and the results expected from the data analysis. Today, there are many methods for analysing statistical variables from a sample of data. They fit all configurations for Big Data applications. Jedha provides the best training offers in Data Analysis to give you an excellent mastery of the methods and technologies of data analysis.
Among the widely used business tools for data analysis, AWS RDS ranks very high. Based on the Amazon cloud, AWS RDS supports on-premises deployment with engines such as Oracle, PostgreSQL, MariaDB, My SQL and SQL Server. The Data Analyst can use it to configure, operate and scale cloud databases. It provides a comprehensive, yet synthesised statistical view of database performance.
Performance Insights automates the analysis and visualisation of workloads at the SQL level. All metrics required for monitoring are easily accessible in tabular form. This eliminates the need to study complex statistical charts for performance variables. By indicating the nature and extent of SQL-related database performance issues, AWS RDS facilitates IT development, business database migration and application testing.
Principal component analysis (PCA)
Principal component analysis is one of the leading methods in multivariate statistics. It helps to synthesise and extract information in the best possible way by reducing the number of variables. By identifying the directions of maximum inertia for each variable and explaining the variance of the associated data, PCA offers the possibility of transforming statistically related variables.
The result is new, uncorrelated variables that better reflect the phenomenon to be studied. This facilitates statistical modelling methods such as discriminant analysis, linear regression, logistic regression, etc. PCA has many statistical applications. They can be used to visualise observations in a two- or three-dimensional space, to study the structure of a set of variables or to identify homogeneous groups of observations.
It is about using statistical methods, artificial intelligence and computer science to derive maximum information from models built from large amounts of data. This is the main component of Big Data. A data mining project is mainly developed according to the results set for the analysis of each variable. For efficient modelling, descriptive statistical techniques (multiple correspondence analysis, principal component analysis, independent component analysis) can be chosen. Predictive methods (decision trees, Bayesian networks, generalized additive model, k-nearest neighbour method) can also be chosen.
The data analysis project may focus on :
- Sequence analysis: search for patterns of causality between two non-simultaneous events,
- pattern analysis: reflects a non-causal link between variables,
- Clustering: identifies new patterns or groups of unknown facts without using already known structures,
- classification: concerns the generalisation of known exploration structures to the discovery of new data,
- prediction: allows for statistical evaluation of future possibilities based on continuous data patterns.
For website optimisation, marketing or fraud detection, the results of a data mining project are very useful. Jedha offers training in data analysis, including data mining, for all student profiles.
As part of the visual analytics tools, Data Exploration is the first phase of data analysis. It consists of exploring a vast set of data by identifying its points of interest and its main characteristics (size, typology, nature, transformation possibilities, etc.) in order to optimise subsequent analytical processing. All this statistical information is summarised in a simple and clear manner in the form of diagrams, dashboards or graphs.
By identifying trends, correlations, explanations of variance for each variable, it is easier for the Data Analyst to reduce a massive data set while making it more qualitative. Data mining is a key method to achieve any data mining project. Data mining is most often performed with the Python and R languages.
Exploratory Data Analysis
This variant of initial data mining focuses on the main features that it tries to investigate and summarise. It helps to identify patterns within the data. It helps to test hypotheses about the dataset and to identify obvious errors or anomalous events in relation to your study. This analysis allows you to examine the validity of some previously obtained results on certain variables.
It also leads to a better understanding of the variables and the relationships between them. This provides the opportunity to choose the best statistical techniques for processing the data to get the results you need. In Python or R, it is possible to use univariate or multivariate exploratory analysis (graphical or non-graphical) to better interrogate the data.
The user has access to several tools such as isolated statistics, cross tabulations, histograms, area diagrams, scatter plots, density maps. The statistical techniques associated with theExploratory Data Analysis The statistical techniques associated with Data Analysis include k-means clustering, linear regression, variable dimensionality reduction, visualisations (univariate, bivariate or multivariate).
It is a technology for managing large-scale dataset analytical workloads. Amazon Redshift is an online data warehouse that allows users to analyse data from their business intelligence tools. For Big Data analysis, Redshift's columnar data storage and massive parallel data processing architecture offers the best possibilities. Whether the data is structured or unstructured, SQL queries run through the tool very quickly.
Redshift addresses the needs of data analysis by providing comprehensive automation of relational database management tasks. These include data classification, master statistics generation, data integration and more. Amazon Redshift is definitely one of the online data analysis tools that every data scientist should master to improve their work.
Data analysis training by Jedha
Jedha offers a bootcamp course for anyone who wants to master the most efficient data analysis methods and technologies. Thanks to the support of highly qualified and experienced teachers, each student discovers all the issues related to data analysis at his or her own pace. The training modules to become a confirmed Data Analyst are :
- SQL & Cloud computing,
- Data visualisation,
- Machine Learning,
- A/B Testing & Web Analytics,
- Statistics & Python.
After the data analysis training, Jedha can also support you in your professional integration.
For Big Data applications, there are many methods, tools and technologies for data analysis. It all depends on the datasets involved and the objectives of your analysis. As a state-recognised training organisation, Jedha provides high-quality training to help you become a master in the field of data analysis.