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Data Science vs. Big Data vs. Data Analytics

Big Data describes the large volume of structured and unstructured data that overwhelms. But it’s not the amount of data that’s important. Businesses are although not worried about the amount of data but how it can be analyzed for insights that lead to strategic business decisions.

To sum up, business utilize big data to enable cost reductions, save time, develop optimized product offering, and smart decision making. Big Data professionals combine big data with high-powered analytics to solve business problems.

How Big Data works?

There are five key steps involved in it which includes traditional, structured along with unstructured and semi structured data.

  • Setting up a big data strategy – A plan that oversees and improves the way we acquire, store, manage, share, and use data within and outside of your organization.

  • Knowing the sources of big data – You can analyze big data that flow into IT systems from different sources deciding which data to keep or not keep, and which needs further analysis.

  • Accessing, managing, and storing big data – Modern computing systems provide the speed, power, and flexibility needed to quickly access massive amounts and types of big data. There are flexible, low-cost options for storing and handling big data via cloud solutions, data lakes, and Hadoop.

  • Analyzing big data – With high-performance technologies like grid computing or in-memory analytics, organizations can choose to use all their big data for analyses. Increasingly, big data feeds today’s advanced analytics endeavors such as artificial intelligence.

  • Making intelligent, data-driven decisions – Data-driven organizations perform better, are operationally more predictable, and are more profitable. To stay competitive, businesses need to seize the full value of big data and operate in a data-driven way.

The differences we are set to achieve lies in these 5 steps and the skills required to carry on that particular step. Let’s delve deeper into each of our three broad big data spearheads.

Big Data Engineering

It involves the building and maintain the organization’s data pipeline systems. Data pipelines encompass the journey and processes that data undergoes within a company. Data engineers are responsible for creating those pipelines. A big data engineer understands and chooses the right tools, various technologies and frameworks, and combine them to create solutions to enable a company’s business processes with data pipelines.

Data engineers make sure that the data is clean, reliable, and prepped. Data engineers wrangle data to a state that data scientists can run queries against it. A good data engineer should be able to anticipate the problems of a data scientist and create usable data product for them.

Bid Data engineers should have the following skills and knowledge:

  • Linux and be comfortable using the command line

  • Programming experience in Python/Scala/R, Java, and SQL

  • Understand distributed systems

  • Understand ingestion (Kafka, Kinesis), processing frameworks (Spark, Flink) and storage engines (S3, HDFS, HBase, Kudu).

  • They need to know how to access and process data.

Data Science

Data science unifies the context of mathematics, statistics, computer science, domain knowledge, and information science, and machine learning in order to “understand and analyze actual phenomena” with data. Data science uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from many structural and unstructured data. 

It remains one of the most in-demand career paths for skilled data professionals. Data professionals are getting past the traditional skills of analyzing large amounts of data, data mining, and programming skills. Today data scientists need to master the full spectrum of the data science life cycle and possess a level of flexibility and understanding to maximize returns at each phase of the process. Data Scientists can both unlock the insights of data and tell a fantastic story via the data.

Data Scientists should have the following skills sets:

  • Statistics, Mathematics, Data Modelling, Python or R programming

  • Other skills: Database skills, Business acumen, Visualisation/BI, Story Telling

Data Analytics 

The role of a Data Analyst revolves around using big data to generate actionable insights which then the C-suite can take action upon. Another interesting fact that data analysts may be working on different departments of the business in different parts of the year.

Data Analytics focuses on why something happened and what will happen in the future based on the data collected over the years. It uses statistical models for predictive forecasting or the classification of different data types like statistical, text, and linguistic. This requires extensive use of computer skills, mathematics, statistics, the use of descriptive techniques and predictive models to gain valuable knowledge from data through analytics. It also includes Supervised and Unsupervised Machine learning techniques.

Applications

  • Marketing analytics 

  • Portfolio analytics

  • Risk analytics

  • Digital analytics

Skillset required: Data Modelling, Python or R programming, Tableau Other skills: Business acumen, Database cleaning skills, Visualisation/BI, Story Telling 

IIHT offers an array of Big Data relayed courses and learning paths that lead to data science, big data, and data analytics job roles. You can also enroll in the PG Program in Big Data Development offered by Jain University with IIHT as the technology training partner. Keep Upskilling.

 

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