With the exponential data added daily into the warehouses, conventional analysis procedures are insufficient. The value from the data can be gained only when you know how to use it. Big data is characterized by its features like volume, variety, velocity, value, and veracity.
This diverse data can be processed by experts knowing the field. There are many skills required for big data analytics, which the industry is looking for today. Big data requires a big skill set. Let’s look at some top big data skills in demand for big data developers.
What is Big Data?
Big data comprises structured, semi-structured, and unstructured data that organizations gather, allowing for information mining and its application in machine learning projects, predictive modeling, and other sophisticated analytics undertakings.
Within organizational data management architectures, big data processing and storage systems have become standard elements, often accompanied by tools that facilitate big data analytics applications.
Also read: Understanding Structured Data in Big Data
Why is big data important?
Businesses leverage big data within their systems to enhance operations, deliver enhanced customer service, design personalized marketing initiatives, and undertake other strategic measures that can ultimately bolster their revenue and profits.
Companies that effectively harness big data hold a potential competitive edge over their counterparts who fail to do so, as they gain the ability to make quicker and more well-informed business decisions.
For instance, big data furnishes valuable insights about customers, which companies can utilize to refine their marketing, advertising, and promotional strategies, resulting in improved customer engagement and higher conversion rates.
By analyzing both historical and real-time data, businesses can gauge the changing preferences of consumers or corporate buyers, thereby becoming more adaptive and responsive to customer desires and requirements.
Here are additional instances of organizations utilizing big data:
- In the energy sector, big data assists oil and gas companies identify promising drilling sites and monitor pipeline activities. Similarly, utilities employ it to monitor and optimize electrical grids.
- Financial services firms employ big data systems to manage risk and analyze real-time market data.
- Governments also utilize big data for emergency response, crime prevention, and implementing smart city initiatives.
Top 12 Big Data Skills to Learn
As the field of technology and data science constantly evolves, the skills required for big data analytics may continue to expand. Let’s look at the 12 most important skills a big data engineer must know to give a boost to their career:
1. Analytical Skills
Are you an individual having the curiosity to find the hidden patterns in the available data? Or you analyze the data to derive some valuable information from it. If you keenly observe the facts and figures, you have checked one of the big data requirements.
Analytical skills are among the very vital big data developer skills. They are a set of problem-solving skills that allow you to parse datasets and information to derive unique solutions to problems.
Critical analysis helps identify seasonal trends, customer behavior, probable downfalls, and forecasting sale models to ensure the smooth functioning of the organization.
Also read: A Beginners’ Guide to Data Analytics in IoT
Big data analytics allows big data developers to dig out the data for hidden patterns, numbers, facts, and information that can generate valuable insights.
Recruiters look for experts with sound analytical skills who can observe complex problems with different perceptions and build an understandable theory from it.
An inquisitive big data professional adds to the organization’s productivity by using their research, logic, and observations. The analytics procedures include the multi-channel communication approach, reflective analysis, creating models, making charts and graphs, and drafting reports.
Also read: Top Free and Paid Data Analytics Tools
2. Data Visualization Skills
Not everyone can understand the hidden patterns in the data. The actionable insights must be converted into clear formats that tech and non-tech people can use in the business. Data visualization is a big data skill wherein the data and information is represented pictorially or graphically.
To become an industry-ready professional, one must learn data visualization tools. Industries hire professionals who can tell stories from the information obtained from the datasets.
The data visualization process highlights the data trends and removes the outliers to make the data presentable and precise.
Data visualization is a prerequisite for big data experts.
It should be concise and deliver the necessary information without any confusion. Charts, tables, graphs, infographics, dashboards, and geospatial data are types of data visualizations.
The big data industry uses numerous tools to create these visualizations for large datasets. These tools make the visualization job easier for the designer by including millions of data points and automating the visualization process.
If you need to stand out from the bundle of resumes, just learn to use the following data visualization tools.
- Tableau
- ChartBlocks
- Infogram
- Datawrapper
- D3.js
- Google Charts
- Grafana
- Polymaps
- Looker
- Qlik Sense
- Microsoft Power BI
- Zoho
3. Understand Business Domains and Big Data Tools
A skilled professional is one who knows the data and the field well. Domain familiarity helps you sustain yourself in the field. If you aim to start a business, you must do background research. Knowing the market segment, potential customer base, and events is essential.
This knowledge strongly supports the business by developing insights, finding patterns, and observing loopholes keenly. Professionals with domain expertise put forward innovative solutions for product development and customer acquisition. Businesses look for a workforce who can add value to their models.
The vastness of big data requires tools and techniques to process it. Big data tools are software that put the raw data into algorithms to build valuable insights from the complex data sea. Companies hire professionals who have hands-on experience with big data tools.
Also read: What is Big Data Architecture: Types, Tools, and More
Learning big data tools can help you land high-profile job roles like big data engineer and big data developer. The market is full of a variety of big data tools. Your selection depends on features like business objectives, dataset size, analysis type, tool pricing, ease of use, and security. The most used big data tools are mentioned below.
- Apache Hadoop
- OPenrefine
- Apache Spark
- MongoDB
- Tableau
- Hive
- RapidMiner
- KNIME
- Lumify
- Apache Storm
- Cassandra
- Adverity
- Integrate.io
- Pentaho
- Cloudera
4. Predictive analytics
It is a branch of advanced analytics that answers a crucial business question: “What might happen in the future?” The analytics procedure yields predictions regarding business outlook using historical data.
Employing the approaches of statistical modeling, data mining, and machine learning, analytics models assess historical data to unveil patterns, monitor trends, and exploit this information to forecast upcoming business trends. Industries today are flooded with voluminous data from log files, images, and video.
This data exists in distinct data repositories. To make it functional, an individual must have predictive analytical skills.
Algorithms and statistical techniques, including logistic and linear regression models, clustering, classification models, neural networks, time series models, and decision trees, are used for the process.
Predictive analysis is a crucial big data skill; mastering this is essential for generating intellectual input for business modeling. Companies are highly professional with predictive analytics skills to model the business features, generate actionable insights, set operational priority, fraud detection, cost optimization, and risk analysis.
Also read: Predictive Analytics – A Customer-centric Approach
Some important tools which you can learn for learning predictive analysis are
- H20.ai
- Orange
- Anaconda
- IBM SPSS
- IBM Watson
- Altair