The field of big data is exploding. Businesses across all industries are turning to new tools and technologies to analyze their data and gain a competitive edge. Talented individuals with the specialized knowledge and skills required to achieve this are in hot demand. Kick-start your big data career now!
Build in-demand skills with our big data courses and gain practical experience and certifications employers are actively seeking.
Spark Fundamentals II
Machine Learning with Python
Foundations of Big Data Program
MapReduce and YARN
Simplifying data pipelines with Apache Kafka
Exploring Spark's GraphX
Machine learning with Apache SystemML
Predictive Modeling Fundamentals I
Big Data 101
Spark Fundamentals I
NoSQL and DBaaS 101
Using R with Databases
Our big data courses offer forward-looking curriculums that build job-ready skills specific to technical field. You can benefit from a range of helpful support services and gain practical experience as you learn.
Support & guidance
Through our big data courses, you can earn career-enhancing IBM Certificates confirming job-ready skills.
Confirm your tech skills and practical experience with our industry recognized Certificates of Completion.
Big data is a term that refers to data sets that are significantly large or of a form that exceeds the ability of typical relational databases to gather, manage, and process the data with minimal latency.
Big data has one or more of the following characteristics:
With the rise of artificial intelligence (AI), smartphones, social media, and the Internet of Things (IoT), data complexity is increasing significantly as new forms and sources of data are created. Substantial amounts of data are generated in real-time and on a massive scale now, and data is coming from a wide variety of sources such as sensors, devices, video/audio, networks, log files, transactional applications, the web, and social media.
Using substantial amounts of data, academics and business users can make better and faster decisions by utilizing information that was previously inaccessible or worthless due to a lack of resources. Enterprises can gain new insights from previously untapped data sources by utilizing advanced analytics techniques such as text analytics, machine learning, predictive analytics, data mining, statistics, and natural language processing. These techniques can be used independently or in conjunction with existing enterprise data to gain new insights from previously untapped data sources.
Big data experts, such as data scientists, architects, and engineers, evaluate data that is generated utilizing a variety of big data techniques and skills. As a result, there is an increasing demand for big data specialists who possess a specific skill set.
In the light of this, the following points are good reasons for choosing big data as a career.
It is well-remunerated with very competitive salary packages
The demand for big data experts such as data analysts, data scientists, data architects, and others is expanding in tandem with the growing amount of data. Companies like Amazon, Google, Facebook, Microsoft, and others offer their big data experts very competitive packages to work on their consumer data. The average big data salary in the USA is $131,504 per year or $67.44 per hour.
It is used across many industries
Every industry uses big data for gaining a competitive advantage and making data-driven decisions. You have a great set of options to choose from that include finance, manufacturing, information technology, healthcare, communications, retail, logistics, and travel.
It offers an interesting mix of opportunities
Big data experts have a wide range of work opportunities available to them that build on existing skills as well as enable you to further your competencies on the job. If you're interested in the subject, you have an attractive selection of possibilities to choose from that will enable you to tailor your career path to match your personal interests. Here are some examples of what we’re talking about:
Information technology (IT) data scientists who are capable of mining and interpreting complicated data for large organizations have good job prospects. Compiling and developing various models of statistical data in collaboration with cross-functional IT teams helps them in the development of system-related suggestions and action plans.
When it comes to aggregating, analyzing, and manipulating large data sets, data engineers rely on their computer science and engineering backgrounds. Computer algorithm development and translation into prototype code, development of technological processes to improve data accessibility and reports, plus management of dashboards and other tools for end-users are all examples of common responsibilities.
By planning and administering large-scale surveys, data analysts can acquire actionable information on a variety of issues. Recruitment of survey participants, compilation and interpretation of supplied data, and dissemination of findings in traditional charts and reports as well as digital media are all part of their responsibilities.
The diagnostics and repair of complicated databases is carried out by database managers who have been trained and are highly proficient in project management and multitasking. They also assess business needs for data and data utilization, evaluate data sources in order to optimize data feeds, and assist in the design and installation of storage equipment.
Data architects utilize their understanding of data-oriented computer languages to organize and preserve data in relational databases and corporate repositories. They also build data architecture plans for each subject area of the enterprise data model, which they call data architecture strategies.
Recruitment specialists in the field of big data, information technology, and other technical fields are recognized for identifying and screening bright candidates for applicant pools. They collaborate closely with businesses to assess their hiring requirements, and then scour the market for the most qualified applicants for specific job positions. They also help the professional candidates they recruit throughout the job application and interviewing stages, as well as the hiring and onboarding processes.
Analysis skills are one of the most important big data skills to have in order to become a successful big data expert in your field. It is necessary to have mathematics and specific science skills in big data in order to understand the nature of complicated data. However, an individual can develop the analytical skills required to address an issue in big data using analytics tools.
Data visualization skills
When training to become a big data professional, individuals should focus on improving their data visualization abilities. In order to effectively communicate a given message, data must be presented in an appropriate manner. Because of this, visualizing abilities are critical in this field.
To develop your data visualization skills, familiarization with big data tools and applications is a great place to start. Data professionals who can present data convincingly to business stakeholders will be highly successful.
Having knowledge and competence in programming languages such as Scala, C, Python, Java, and many others is an advantage for big data professionals. There is a considerable demand for programmers who have prior experience in data analytics and business intelligence.
In order to be a successful big data professional, you must also have a solid understanding of the principles of algorithms, data structures, and object-oriented programming languages. Performing and coding quantitative and statistical analysis are essential skills for professionals working in the big data market.
Big data is a challenge because it contains unstructured data by its very nature. To excel in a career in big data, therefore, you need to be motivated by a desire to find solutions to issues. Your ability to think creatively will aid you in developing a more effective solution to an issue.
Familiarity with technologies
Professionals in the big data field should have knowledge of a variety of the technologies and techniques that are used in the big data industry. For example, big data tools aid in the conduct of research, the analysis of findings, and the conclusion. It will be of value to you, therefore, to develop skills in big data tools and technologies such as Scala, Hadoop, Linux, MatLab, R, SAS, SQL, Excel, SPSS, and many others.
Skills of data mining
Data mining professionals with a lot of experience are in high demand. To advance in your career, therefore, you should concentrate on developing your knowledge, skills, and expertise in data mining technologies and techniques. One route is to learn from top data mining technologies such as KNIME, Apache Mahout, Rapid Miner, and others in order to gain the most in-demand data mining abilities.
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