Data analysis is a science that is indispensable in many fields. Companies, regardless of their sector of activity, generate large volumes of data. The analysis of this data is therefore essential and undoubtedly a most valuable asset. The analysis of the data is carried out by the data analyst, who is also responsible for interpreting the data. Jedha proposes to present this most promising profession and in particular how to train for data analysis.
What is data analysis?
Data Analysis is the process of cleaning, transforming and modelling data to translate it into actionable information for better decision making. Every day, companies receive an immense amount of data, which is often just raw material. Data analysis allows this information to be put into shape in order to make sense of it. The data analyst is the specialist who extracts usable information for the company from the data generated. He or she uses current and past data to find insights that can be used by decision-makers. These results can be decisive for the growth of the company, its management and for the development of new products.
The data that the data analyst looks at in the context of data analysis includes prospects, customers, company processes or products. The information provided by the data analysis can be used toimprove the user experience of a website, to optimise production processes or to create offers that meet the needs of the customer.
What is the purpose of data analysis?
The world as we know it today has never produced so much data. The results of data analysis are a major issue for many companies. Today, companies have become aware of the importance of data analysis. It allows them to predict their customers' needs, control their buying behaviour and meet their expectations. This is a major challenge for marketing.
Data analysis therefore enables companies to make better decisions for their growth. The results of data analysis can be very useful for market research, positioning, product development, but also for studying customer opinions and feelings.
Data analysis generally allows reasoned choices to be made based on concrete elements rather than on mere intuition. It can also be used to optimise production tools by analysing order histories and the functioning of the supply chain. This allows forimproved after-sales service.
Training in data analysis
Data analysis, also known as data analysis or data management, is a Big Data-oriented function. Data analysts are responsible for large databases. They are also responsible forextracting useful information and making connections. They are able to produce accurate figures and are true specialists in data modelling. The datat analyst is a profile that is increasingly sought after by companies looking to exploit data in their favour. It is estimated that currently only 0.5% of available data has been analysed. Data analysis therefore has a bright future.
To become this data analysis specialist, the path to follow includes training. For example, it is possible to go to a school specialising in digital marketing. Such schools offer training to become a data analyst. The classic course they offer lasts five years and is based ona Bachelor's degree and a specialised Master's degree. One of the best options in France to become a data analyst is probably to enrol in the data courses organised by Jedha. Jedha's data courses are in pole position among the best in France. They offer great flexibility and allow applicants to choose a part-time or full-time, face-to-face or distance learning course according to their schedule.
Jedha's data analytics courses develop real skills in data visualization, machine learning, Big Data, data mining or business intelligence. Jedha's data analytics courses are very quality-oriented, as evidenced by the careful selection of instructors to provide students with the best possible learning experience. They are also constantly monitored to ensure that they remain at the cutting edge of technology.
The different types of data
In data analysis, variables are central. They determine the different types of data exploitable data. Data management analyses are made possible by identifying and manipulating different variables in order toextract useful information from the data. Variables are types of attributes, measurable characteristics that can take on different values. In data analysis, an attempt is made to verify the existence of a statistical relationship between different variables, for example by means of contingency tables or correlation tests.
Different types of variables can be distinguished, which can be grouped into two main types: categorical variables or qualitative variables and numerical variables or quantitative variables. Qualitative variables refer to characteristics that cannot be quantified, i.e. data to which no value can be assigned. They include nominal and ordinal variables. Nominal variables or data describe a name or category that has no natural order. Examples of categorical variables are type of housing, gender and means of travel.
Ordinal data or variables refer to variables whose values are ordered between the different possible categories. An example of ordinal qualitative data is customer satisfaction (poor, fair, good, very good, excellent).
In addition, numerical or quantitative variables refer to quantifiable characteristics that have numbers as their value. Quantitative variables can be continuous or discrete. A continuous variable is data that takes on an infinite number of real values. The size or turnover of a company over the years is an example of a continuous quantitative variable. In contrast, discrete quantitative variables can only take a finite number of real values. Examples of discrete numerical variables are age, the number of sales of a given product, the size of a company or a household.
The different steps in data analysis
Data analysis is a multi-step process: data collection, data cleaning, data analysis and data visualisation. data storageData cleaning, data analysis and data visualisation.
Data collection and storage
Data collection is the first step in Data Analysis. The raw data comes from several sources and is collected through a tool or application. The choice of data to be collected depends on the objectives set, i.e. the results expected from the data analysis. This data is then stored in a system designed to facilitate the delegation of tasks.
The raw data collected is not always usable to produce results. In this case, the data analyst is responsible for cleaning and organising the data in order to facilitate their subsequent analysis. This process is called data cleaning. During this operation, the data is converted into a format suitable for analysis. Duplicates are also eliminated during data cleaning. The same applies to erroneous or corrupted information.
After the data has been collected and cleaned, the analysis can begin. The role of the data analyst is to set up analysis systems and use approved data analysis methods to generate relevant results based on the collected data. The data analyst has tools at his or her disposal to interpret the results of the analyses conducted. Data analysis in practice answers different types of questions that correspond to the main categories of data analysis. These include :
- statistical analysis of the data,
- text analysis,
- descriptive and inferential data analysis,
- diagnostic analysis of the data,
- predictive data analysis,
- prescriptive data analysis...
Statistical analysis of data
Statistical data analysis is the use of past data to gain a better understanding of the present, particularly in the form of a dashboard. It includes data collection, analysis, presentation and modelling.
Descriptive analysis of the data
Descriptive data analysis involves analysing numerical data and presenting it in tabular and graphical form. Inferential data analysis is the analysis of a sample of data to draw conclusions about a population.
Textual data analysis is about discovering patterns in large volumes of text. This technique uses data mining tools to transform raw data into strategic information.
Diagnostic analysis of the data
Diagnostic data analysis is used to understand the causes of an event that is discovered through statistical analysis of the data. Among other things, it identifies patterns of behaviour in the data with a view to solving similar problems. By identifying the causes of a phenomenon through the results of diagnostic data analysis, a company can take preventive measures.
Predictive data analysis
Predictive data analysis is the process of identifying likely events and making predictions from the data. The results of data analysis are useful for predicting future probabilities. The reliability of these predictions depends largely on the accuracy of the information, the amount of information and the degree of exploration. For example, predictive data analysis allows a company to use statistics to predict what might happen in the next quarter. Predictive analysis models are useful for forecasting current revenues or market trends, among other things. This allows a company to adjust its marketing strategy.
Prescriptive data analysis
Prescriptive data analysis is the combination of information from descriptive, diagnostic and predictive analyses. It is used to determine what action should be taken to solve a problem or make a decision. Prescriptive analytics is widely used by data-driven companies due to the poor performance of predictive or descriptive data analysis. With prescriptive data analysis, the data analyst looks for information that will have a positive impact on the company's results and direction.
After analysing the data, the Data Analyst must provide a report to communicate the results of his analysis to his employer. To achieve this, he or she uses data visualisation tools to produce reports that are intelligible, especially for non-data analysts. For this reason, the results of the data analysis are organised in the form of a graph, table or diagram. These visualisations are more understandable and interpretable by the human brain. In order to detect patterns and produce relevant results, data analysis must provide reports on a regular basis.
The Big Data now occupies a prominent place in companies. The numbers are clear. It is estimated that companies that use Big Data have earned an additional $1.2 billion. They want to gain as much information as possible about their customers' behaviour and motivations. To do this, they need to extract large volumes of data to identify repetitive patterns. This allows the data analyst to analyse the data, classify and interpret the results to draw conclusions.
Big Data applies to datasets whose size exceeds the capacity of traditional databases. Big data (terabytes to zettabytes) comes from a multitude of sources: web log files, social networks, online audio and video files, etc. This data is a goldmine for businesses. If it was previously unusable or inaccessible, companies can now use advanced analysis techniques such as predictive analysis, machine learning, data mining, natural language processing, etc.
Difference between data-oriented management and data management
The use of data in business involves many concepts that deserve to be known. Among these, some are confusing. This is particularly true of data-oriented management and data management. Although these two concepts are quite similar, they are nonetheless different. Data-oriented management refers to a data-driven management style. A manager who adopts this method places data analysis at the centre of his management strategy. In contrast, data management, also known as data management, is a process that includes collecting, storing and securing data.