Data Analytics for Beginners | What Is It and What Does a Data Analyst Do?








Throughout the extensive history of humanity, numerous illustrious figures like Francis Bacon, Sam Walton and Bill Gates himself have reached the conclusion that information is power. A phrase that has been given new meaning over and over across the centuries; and that today makes more sense than ever.


Prominent analysts claim that information is the new oil, something that no longer seems at all far-fetched when we watch today's big companies like Google, Amazon, Facebook, Apple and Microsoft compete fiercely to obtain the largest amount of data from their users. This has led governments to start creating regulations and taking forceful measures to protect people's privacy.


The companies that understand the importance of analyzing data to create innovative solutions are the ones that will manage to gain a competitive advantage in the world that's coming, and this is why the Data Analytics methodology is so important. A specialty we'll be introducing in this new Coder Blog.


In this article on Data Analytics for beginners we'll be analyzing what this discipline is about, its tools, its differences with similar disciplines, the role of a Data Analyst and other important factors to get us started in it.


Let's start at the beginning!








1.1 Data Analytics for Beginners: What is Data Analytics?


In a few words, we can say that Data Analytics is basically a set of measurement methods and techniques that help us study, clean and transform data into relevant information. This process lets us reach conclusions and make strategic decisions for our business.


Data Analytics is not a software; rather, it's a group of tasks that are carried out in three major stages: collection, transformation and visualization. This process lets us identify the needs of the business in order to work on different solutions.








The philosophy of Data Analytics.





This methodology must be carried out by professionals with sharp judgment, capable of determining the metrics that should be taken into account since not all of them are valid. Likewise, these people must have the ability to analyze data using various basic tools. 


The tendency to work based on data analysis is on the rise since organizations that work this way can make better operational decisions and manage risks.


To understand the concept of Data Analytics it's important to mention that there's an important difference between Data and Information:


Data: It's a characteristic or attribute without processing, which informs nothing on its own. Data are simply those attributes that give identity to an object or living being. For Example: First name, Last name, Country, ID, Brand, Model.


Information: It's the union of processed data, which comes together to report a fact. Information is the union of several pieces of data; it lets us complement data that don't make sense on their own, to provide a complete context for a situation. For Example: Andres Perez, a 62-year-old Argentine. 











By exhaustively analyzing a business's data, this methodology obtains relevant information to make decisions.





1.2 History of Data Analytics


To continue with our Data Analytics for Beginners guide we're going to briefly review the history of this discipline. The history of Data Analytics or Data Analysis is divided into three major milestones, called Analytics 1.0, 2.0 and 3.0





Analytics 1.0:


It began around the year 1990.This stage is characterized mainly by being the era of the enterprise data warehouse, used for information, and of business intelligence software, used to query and report.The first information systems custom-built for companies whose large scale justified the investment were created, which were then marketed by external providers in more generic forms.The data management specialists began to appear, who dedicated much of their time to preparing data for analysis and relatively little time to the analysis itself.The data sets were small enough in volume and static enough to be separated into warehouses for their analysis.





Analytics 2.0:


It began around the year 2000.The term Big Data appears for the first time, when social media and Internet-based companies like Google or eBay began to accumulate and analyze new types of information.More powerful analysis tools began to be created and the opportunity to obtain profits by providing them was detected, so companies rushed to develop them.Analysts and data management specialists begin to dedicate more time to analysis, which produces the emergence of new types of information.Big data begins to appear, which started to be distinguished from small data because it wasn't generated exclusively by a company's internal transaction systems. It was also obtained from external sources, coming from the Internet, sensors of various types, public data initiatives like the human genome project and captures of audio and video recordings.





Analytics 3.0:


It began around the year 2010 and extends to this day.Organizations integrate large and small volumes of data, from internal and external sources and in structured and unstructured formats to produce new knowledge in predictive and prescriptive models.Today, not only information companies and online companies can create products and services from data analysis, but also all companies in all industries. If a company makes things, moves things, consumes things or works with customers, it has increasingly larger amounts of data about those activities. Every device, shipment and consumer leaves a trail, which can be analyzed for the benefit of customers and markets. In addition, every company has the ability to integrate analysis and optimization into every business decision made on the front line of its operations.





1.3 What is Data Analytics for?


The Data Analytics discipline has a large field of applications; it gives us many benefits and serves multiple purposes:


Mainly, it helps us eliminate subjectivities by allowing all members of the company to make decisions based on data and not on instinct or previous experience.It lets us identify both the products and the customers that generate the most and least profitability for us. This way we can focus on boosting those that produce more benefits and discontinue or improve those that don't generate enough return.It makes it possible to greatly improve and optimize the user experience on our digital platforms.It gives us access to getting to know our customers in depth, knowing who our standout customers are, identifying the way to meet their needs and finding others with similar characteristics.It gives us the possibility of generating forecasts of future production volumes.It helps us identify customer behavior patterns and trends in a simpler way to use them in favor of our business.It enables us to identify and anticipate different risk factors that could jeopardize the viability of the business. It lets us find undetectable opportunities and gain new perspectives to create new products and boost the growth of our business.It helps us provide a better service and avoid possible fraud.





Get to know our Data Analytics courses and career tracks for Beginners by clicking the photo:








1.4 Types of Data Analytics





Descriptive Analysis.Diagnostic Analysis.Predictive Analysis.Prescriptive Analysis.





Descriptive Analysis:











Nature: It's a type of data analysis used to describe important trends in the data we have and to bring to light the situations that lead to new facts.


It's an answer to the question: What happened?


How is a descriptive analysis carried out?: The analysis is based on one or several research questions and doesn't have a hypothesis. Likewise, it involves collecting related data, organizing it, tabulating it and describing the result.


To carry out a descriptive analysis it's necessary to calculate simple measures of composition and distribution of variables. Depending on the type of data they can be rates, proportions, averages or ratios. Likewise, whenever it's relevant, as in the case of sample surveys, measures of association between variables can be used to determine whether or not the observed differences between men and women are statistically significant.


The knowledge bases that arise from this analysis can give rise to quantitative analyses. The data can give rise to useful perspectives for creating a hypothesis, in case they're interpreted appropriately.





Diagnostic Analysis:











Nature: It's a type of data analysis in which conclusions are defined based on historical data identified by the descriptive analysis. While descriptive analysis represents a starting step for making decisions in most companies, narrating facts that already happened. Diagnostic analysis goes a step further to discover the reasoning behind certain results 


It's an answer to the question: Why did it happen?


How is a diagnostic analysis carried out?: This type of analysis is generally done through techniques like data discovery, drill-down, data mining and correlations. In the discovery process, analysts identify the data sources that will help them interpret the results. Drilling down involves focusing on a certain facet of the data or a particular widget. This drill-down is easily done using a BI platform.





Predictive Analysis:











Nature: It's a type of data analysis that Data Scientists master and basically serves to identify future behaviors based on historical information.


It's an answer to the question: What will happen?


How is a diagnostic analysis carried out?: This analysis is done by data scientists, who are assigned steps in the analytical workflow represented by the following five categories:


Identify the business outcomes: It's important to determine what questions must be answered through predictive analysis. If the right outcomes aren't identified, running predictive analytics is like throwing darts in the dark. It's also important to identify the drivers (independent variables) that are likely to affect the outcome (dependent variable).


Determine the data needed to train: Predictive analytics requires data from multiple sources, so analysts must identify the current data sources. If the existing sources are insufficient, they must acquire data from other sources to ensure the models can be trained accurately.


Determine analysis methods: Different techniques are suitable for answering different questions depending on the amount and type of data available. Statistical analysis, neural networks, machine learning and data mining are examples of sophisticated techniques that can predict outcomes.


Validate results: Advanced analytics can't be used as a black box. Incorrect training data, wrong algorithms and poor assumptions are just some of the pitfalls that can result in false predictions. Data scientists must work closely with analysts and business-line leaders to ensure that predictive models make business sense.


Test the predictions on performance: Predictive models must be continuously tuned to improve accuracy. If a model fails, analysts must identify the root cause and retrain and test to improve the models.


Modern predictive analytics solutions must provide data scientists with a productivity workbench that supports all these functions. But predictive analytics isn't just about data scientists' productivity. The value of predictive analytics comes from its deployment within other applications in the business flow. That means a predictive analytics solution must support executing predictive scores at scale.





Prescriptive Analysis:











Nature: Prescriptive analysis complements predictive analysis since it takes as its information source the output of the prediction, combined with rules and constraint-based optimization. This allows better decisions to be made about what to do. The decision could be to send an automated task to a human decision-maker along with a set of next-action recommendations, or to send a precise next-action command to another system.                                               


Therefore, prescriptive analysis is best suited to situations where the constraints are precise. This usually happens with tactical choices, in which many decisions must be made within a given period. Some examples of this include programmatic advertising purchases, stock trading and fraud detection. However, the universe of situations in which prescriptive analytics is being applied continues to expand and will eventually be in many types of decision-making processes.


The broader adoption of prescriptive analytics is often hindered not by the functionality of prescriptive analytics solutions, but rather by external factors like government regulation, market risk or organizational behavior.  This is the situation in healthcare, for example, where there are some early successes, but the widespread adoption of prescriptive analytics will still take years. Regardless of the timeline for the widespread adoption of prescriptive analytics, every company should start evaluating the applicability of this type of analysis for its own operations.


It's an answer to the question: What should I do?





1.5 Data life cycle


One of the main concepts of Data Analytics is the fact that data has a life cycle made up of 6 key stages: Collection, Maintenance, Synthesis, Use, Publication and Cleaning.











Collection


Objective of the Stage: Identification of the origin of the data. Digital storage.


Data collection plays the most important role in the data life cycle. The Internet provides almost unlimited data sources for a wide variety of topics. The importance of this area depends on the type of business, but traditional industries can acquire a diverse source of external data and combine it with their transactional data.


In this stage the main users of the development are defined, since, based on their vision, we'll be able to carry out an initial assessment, and lay out what the objective is as a deliverable.


For example, let's suppose we'd like to create a system that recommends restaurants. The first step would be to collect data, in this case, restaurant reviews from different websites and store them in a database. Since we're interested in plain text, and we use it for analysis, it's not so relevant where the data will be stored to develop the model. 





Maintenance


Objective of the Stage: Review of the information. It's processed to guarantee its quality.


Once we've collected all the necessary data, in this phase we have to clean that data. And when we talk about cleaning we mean removing fields that aren't useful, transforming them and normalizing them as we need. It's important to remove inconsistent records. 





Synthesis: 


Objective of the Stage: The information is modeled according to the required indicators.


During this phase we'll do some statistical tests and use visualization and measurement techniques to learn more details/patterns about our data. We'll identify possible distributions of each variable/feature.


Determine which are the most important segmentations, as well as the date or dates that will determine the history and the time periods of the analysis. 





Use: 


Objective of the Stage: The information is made available for its exploitation, in the generation of reports, dashboards, models, etc.


In this stage of the cycle the users who will be able to use the already-synthesized data are identified. The data can be available in the database for access and use in any tool, or in an already-developed control dashboard. 


It's important to highlight that the main users were defined in the data collection stage, since, when starting the data analysis and exploitation project, it's identified who will be benefited by the deployment.





Publication:


Objective of the Stage: The analysis carried out is exposed, for decision-making. 


This stage complements the use stage; in most cases they're stages that can be developed in parallel. At this point it's defined in which tool the information will be published, so that everyone involved can start using it and making decisions for the business. 





Cleaning: Objective of the Stage: Thanks to the exposure, it's detected whether the information requires additions, deletions or changes.


In this stage all those involved are asked for feedback on the use they gave to the data. Based on this validation information, we'll be able to understand whether our data had a correct extraction, transformation and load process. 


At this point all the parts of the initiative benefit, because it lets us identify improvement opportunities for the creation of the information, identify new ways of use and publication, and also the definition of new indicators and segmentations for decision-making.








Through the first four points of this article we've already been able to understand what Data Analytics is and its main characteristics. To continue with our Data Analytics for Beginners guide, it's time to define what Data Analytics is NOT, differentiating this concept from other related ones.








2.1 Data Analytics Vs Data Science


On many occasions the concepts of Data Analytics and Data Science tend to be confused since these work closely together; however, there are big differences between both methods. The difference between Data Analytics and Data Science lies mainly in the function of each one. While a data scientist makes predictions based on patterns from the past, the data analyst extracts vital information from a business's historical data. 


Another important difference is that while the Data Scientist is concerned with asking questions, the Data Analyst is concerned with answering questions. Likewise, a scientist will turn to extracting information from various sources, while the analyst will generally do so from just one. Regarding this point, it's important to mention that the Scientist uses Machine Learning to obtain information, while the Analyst turns to programming languages like Python.


Regarding the field of application of their knowledge, we can say that the data scientist has greater freedom of action while an analyst is in charge of applying it exclusively to solving business problems.





2.2 Data Analytics Vs Business Intelligence


Two other concepts that are often confused are those of Business Intelligence and Data Analytics. 


The difference between Business Intelligence and Data Analytics is that business intelligence is a set of methodologies, processes, architectures and technologies that leverage the result of information management processes for analysis, reporting, performance management and information delivery. While, on the other hand, data analytics is the process of examining data sets for their transformation and visualization. This way conclusions are drawn about the information they contain in order to manage indicators.


The work of Data Analytics has a greater degree of complexity than Business Intelligence; however, these aren't mutually exclusive and complement each other to provide both descriptive and predictive analyses.














Now that we understand what Data Analytics is and what Data Analytics isn't, we're going to continue this data analytics for beginners guide by briefly analyzing the tools used to carry out this methodology.








3. Data Analytics Tools, Languages and Programs


There's a wide variety of software, languages and tools for different phases of the Data Analytics process: Database Tools, ETL Tools, Visualization Tools and Programming Languages.





Database Tools


They're systems that allow information to be managed by people or applications. There are ones for relational databases, characterized by having a structure, and non-relational databases, characterized by not having one. 


To work with relational databases there are systems like:


Microsoft SQL Server.PostgreSQL.MySQL


On the other hand, to work with non-relational databases there are systems like:


MongoDB.Cassandra.Hadoop.





ETL Tools (Extract, Transform, Load)


They're systems that allow the processing of data to later convert it into information. Among the most used software we find:


Informatica ETL.IBM Datastage.SSIS - SQL Server Integration Services.Oracle Data Integrator.





Visualization Tools


They're systems that allow the visualization of information with charts and by applying measurement techniques. Among the most used software we find:


Power BI.Tableau.MicroStrategy.QlikView.





Programming Languages


They're languages created for building applications, designing interfaces and managing information, among other purposes. The most used programming languages in the world of Data Analytics are:


R.Python.Julia.





Having done an overview of the most used tools, software, programs and programming languages for the different stages of the Data Analytics process, it's time to understand much more about the role of the Data Analyst.








4.1 What is a Data Analyst?


As we've already mentioned throughout this Data Analytics for beginners article, a Data Analyst or Big Data Analyst is the professional in charge of leading a Data Analytics process. Data analysts seek to determine how data can be used to answer questions and solve problems, in addition to generating business strategies.


Likewise, this type of professional studies what's happening in the present moment, to identify trends and make predictions about the future. This profile works with enormous volumes of data, which they must organize in order to find previously unidentified patterns.


To do their work, an analyst must understand how to collect the relevant data and how to analyze it statistically.





4.2 What does a Data Analyst do? - Roles of a Data Analyst


Work with technology, management and/or data science teams to set goals.Data mining from primary and secondary sourcesCleaning and slicing of dataAnalyze and interpret results using statistical tools and techniquesIdentify trends and patterns in data setsIdentify new opportunities for process improvement.Provide data reports for management.Design, build and maintain databases and data systems.Troubleshoot code and data-related problems





4.3 What do I need to be a Data Analyst?


To be able to work as Data Analysts, people must have a series of hard and soft skills that will let them solve problems and do their jobs as efficiently as possible.


Among the hard skills we can mention a solid background in statistics, mathematics and programming languages like Python, which will be indispensable for carrying out in-depth analysis of a business's data.


On the other hand, a Data Analyst should also have notions of Business Intelligence to be able to work side by side with this type of professional and broad knowledge of software and tools like the ones we mentioned earlier in this same article.


Among the soft skills we can highlight interpersonal and communication skills, since this type of profile must ensure that all a company's workers and not only those in their area can understand the conclusions drawn from the data they work with.


A data analyst is also a curious person about discovering what's hidden behind a company's data, has a great sense of innovation and receptiveness to change, which is fundamental in an environment that's constantly changing. 


Other extremely important traits for this position are having a problem-solving-oriented profile and the ability to adapt to any sector of the company.





4.4 Where to study Data Analytics?


Currently, more and more companies are demanding Data Analyst profiles, which has led to a large educational offering emerging.


Coderhouse, a leading Latin American company in digital education, has a Data Analytics course in which you learn the general concepts of databases and then work on relational databases.


In this course curated by the best professionals in the industry, students learn to:


Perform as technical analysts and as users of relational databases through the structured query language (SQL)Design and develop control dashboards with the Power BI tool, using Data Analysis Expressions, M Language and Power Query.Develop Data Analytics projects as comprehensive analysts, from the initial assessment to the creation of dashboards.Design relational databases.Manage information with SQL.Estimate the timing of the stages of a data exploitation project.Manage the initial assessment of a data analysis and exploitation initiative.Manage the initial assessment of an information analysis and exploitation initiative.Identify the data life cycle.Create an efficient control dashboard.Create optimal DAX.Develop data manipulation and transformation techniques.Implement storytelling to report correctly.Identify indicators for information management.Generate effective conclusions with data.








5. Conclusion


Throughout this complete Data Analytics for beginners article, we were able to understand the importance that Big Data analysis is currently taking on for making key decisions in a company. Likewise, we've also analyzed its difference with other related disciplines, the role of a Data Analyst and the characteristics this profile must gather to be able to perform its functions in the best possible way.


As we mentioned at the start of the article, in these times companies can no longer keep doing without analyzing the data they generate, since discovering trends and patterns through it is the best way to obtain competitive advantages in order to get ahead of the competition. The companies that understand this will be the true owners of tomorrow.


If you want to always stay up to date on the new trends that emerge we recommend subscribing to the Coder Blog by scrolling down, and if you want to learn alongside the best professionals in the industry we invite you to discover the intensive Data Analytics courses  that Coderhouse has for you.

If you'd like to keep exploring this topic, you can also read the right learning path to become a data analyst.

Recommended Coderhouse courses

If you want to go deeper into data analysis and applied artificial intelligence, Coderhouse has programs for all levels:

• Data Analytics Course: to learn to analyze data, build dashboards and make decisions based on real information.

• AI Engineering Course: to incorporate AI and machine learning models into your data projects.

• Introduction to Artificial Intelligence Course: to understand the AI ecosystem and complement your data profile with knowledge about language models.