Becoming a Big Data Analyst: A Step-by-Step
Big data analysis is a relatively new, but quite in-demand area of the labour market. The demand for data scientists is constantly growing. Big Data are data sets of very large size, which are also characterised by diversity and high update rate. A big data analyst is a specialist who identifies and investigates patterns in data using special software tools.
Overview of Big Data and AI
The generation and sharing of big data across devices is happening in almost every social sphere. Big Data is used by giants such as Google, Uber, IBM, Amazon to optimise customer experience, reduce the risk of fraud and data security threats. Big Data specialists after big data and ai courses are needed in: marketing, search technology, retail, social media, gaming, personalisation, speech technology, financial institutions and recommendation systems.
Skills You Will Gain
It is not necessary for an analyst to have a university degree in information technology. However, a Data Analyst must understand business processes, understand statistics, perform machine learning, and be able to work with tools.
Types of data analysis:
Descriptive - for collecting characteristics, processing the information obtained.
Predictive is aimed at predicting future results.
Diagnostic helps to detect errors in the data.
Prescriptive includes the above types of information analysis.
The duties of the analyst also include tasks on Business Inteligence (BI) and optimisation of processes in production. A specialist should know the methods of analysing business processes: SWOT, ABC, IDEF, BPMN, MTP, PDCA, EPC and others.
Basic Data Analyst skills:
Ability to extract data from various sources (Hadoop, MS SQL, MySQL, etc.).
Processing information using Scala, R, Python or Java.
Visualising structured data using Qlik, Plotly or Tableau.
Generating research that fits the category of the business problem.
Providing hypotheses in line with the business objectives.
Additionally, the analyst may use Apache Storm, Apache Kinesis, Apache Spark Streaming.
Big Data specialists need to be able to build graphical models using Bayesian and neural networks, clustering and types of analysis. A Data Scientist, Data Analyst or Data Engineer should be skilled in working with Data Lakes, as well as security and Data Governance. Becoming an expert will help you develop each of these skills in depth.
Why Learn Big Data and AI?
In the era of digital transformation, when the amount of data doubles every two years, the art of analysing and using it has become not just an important skill but also a key competitive advantage. In the different fields, traditionally based on knowledge and experience, big data and machine learning course opens new horizons. With the ability to analyse data in depth, we have a tool that allows us to not only respond to current educational needs, but also to predict them, adapting to changing realities faster than ever before.
Career Opportunities and Job Roles
Let's take a look at the main roles and vacancies in Big Data and Data Science.
Data Scientist
A Data Scientist is a specialist who analyses data and develops machine learning with big data to solve business problems. Key responsibilities include:
Data collection and processing.
Developing and testing machine learning models.
Visualising data and presenting results.
The Data Scientist should have a strong knowledge of statistics, programming and machine learning.
Data Engineer
The Data Engineer is responsible for building and maintaining the infrastructure for data processing. Key responsibilities include:
Developing and supporting ETL processes (Extract, Transform, Load).
Working with databases and data warehouses.
Optimising the performance of data processing systems.
The Data Engineer plays a key role in ensuring that data is available and ready for analysis.
Big Data Engineer
The Big Data Engineer develops and maintains systems to process large amounts of data. Key responsibilities include:
Working with distributed systems such as Hadoop and Spark.
Optimising the performance and scalability of systems.
Ensuring data security and reliability.
The Big Data Engineer should have in-depth knowledge of distributed computing and big data.
Machine Learning Engineer
Machine Learning Engineer specialises in the design and implementation of machine learning models. Key responsibilities include:
Developing and optimising machine learning algorithms.
Implementing models into production systems.
Monitoring and maintaining the models.
The Machine Learning Engineer must have a strong knowledge of machine learning and programming.
Industry Demand
Big Data and AI are two rapidly developing fields that play a key role in today's world. Big Data refers to processing and analysing huge amounts of data that cannot be processed using traditional methods. Data Science, on the other hand, involves the use of statistical methods, machine learning and other technologies to extract knowledge and insights from data. These fields are of great importance to business, science and technology as they enable better informed decision-making and the development of innovative products and services!
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