data science

Data Science, Science and Technology

Data Science 101: What are concepts you need to know before entering the Data Science world?

Data Science 101: What are concepts you need to know before entering the Data Science world? 650 486 Exist Software Labs

I was playing around with data and then I found the Science — Yes, my introduction to the world of Data Science has been a part of my research work.

If you’re like me, starting out with Data Science looking for resources that can give you a jump start or at least a better understanding of it or you have just heard/read the term being coined and want to know what it is, of course, you can find a gazillion materials about it, this is, however, how I started and got familiar with the basic concepts.

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What is ‘Data Science’?

Data Science provides meaningful information based on larger amounts of complex data or big data. Data-Driven Science combines different fields of work in statistics and computation to interpret data for decision-making purposes.

Understanding Data Science

How do we collect data? — Data is drawn from different sectors, channels, and various platforms including cell phones, social media, e-commerce sites, various healthcare surveys, internet searches, and many more. The surge in the amount of data available and collected over a period of time has opened the doors to a new field of study based on big data — the huge and massive data sets that contribute towards the creation of better operational tools in all sectors.

The continuous and never-ending access to data has been made possible due to advancements in technology and various collection techniques. Numerous data patterns and behavior can be monitored and it can make predictions based on the information gathered.

In technical terms, the above-stated process is defined as Machine Learning; in layman’s terms, it may be termed Data Astrology — predictions based on data.

Nevertheless, the ever-increasing data is unstructured in nature and is in constant need of parsing in order to make effective decisions. This process is really complex and very time-consuming for organizations — and hence, the emergence of Data Science.

A Brief History / Background of Data Science

The term ‘Data Science’ has been in existence for about three decades now and was originally used as a substitute for ‘Computer Science’ in the 1960s. Approximately 15–20 years later, the term was used to define the survey of data processing methods used in different applications. 2001 was the year when Data Science was introduced to the world as an independent discipline.

Disciplinary Areas of Data Science

It incorporates tools from multiple disciplines to gather a data set, process and derive insights from the data set and interpret it appropriately for decision-making purposes.

Some of the disciplinary or noteworthy areas that make up the Data Science field include Data Mining, Statistics, Machine Learning, Analytics Programming, and the list goes on. But, we would be doing a brief discussion mainly on the aforesaid topics as the concept of Data Science mainly revolves around these basic concepts, just to keep it simple.

Data Mining applies algorithms to complex data sets to reveal patterns that are then used to extract useful and relevant data from the set.

Statistics or Predictive Analysis uses this extracted data to gauge events that are likely to happen in the future based on what the data shows happened in the past.

Machine Learning can be best described as an Artificial Intelligence tool that processes massive quantities of data that a human is incapable of doing in a lifetime — it perfects the decision model presented under predictive analytics by matching the likelihood of an event happening to what actually happened at a predicted time in the past.

The process of Analytics involves the collection and processing of structured data from the Machine Learning stage using various algorithms. The data analyst interprets, converts, and summarizes the data into a cohesive language that the decision-making team can understand.

Data Scientist

Literally speaking, the job of a Data Scientist is multi-tasking: We collect, analyze and interpret massive amounts of structured and unstructured data, and in a maximum number of cases, to improve an organization’s operations. Data Science professionals develop statistical models that analyze data and detect patterns, trends, and various relationships in data sets.

This vital information can be used to predict consumer behavior or to identify business and operational risks. Hence, the job of a Data Scientist can be described as a story-teller that uses data insights in telling a story to the decision-makers in a way that is understandable. The role of a Data Scientist is becoming increasingly important as businesses rely more heavily on data analytics to drive decision-making and lean on automation and machine learning as core components of their IT strategies.

Present & Future of Data Science

Data Science has become the real thing now and there are potentially hundreds and thousands of people running around with that job title. And, we too have started seeing these Data Scientists making large contributions to their organizations. There are certainly challenges to overcome, but the value of data science from a business point of view is pretty clear at this point.

Now, thinking about the future, certain questions definitely arise — “How will the practice of data science be changing over the next five years? What will be the new research areas of data science?”

“Will the fundamental skills remain the same?”

These are certainly debatable questions, but one thing is for sure — inventions have happened and will continue to happen when there arises any demand for the betterment of the future. And, the world would keep benefiting from data science through its upcoming innovations.

The possibilities of how to utilize Data Science in real-world scenarios are endless! Our Data Solutions team would be happy to help you capitalize on this technology for your enterprise.

Befriending Your Data in 2021, Java, Java Philippines

Befriending Your eye-opening Data in 2021

Befriending Your eye-opening Data in 2021 768 487 Exist Software Labs

It’s the new year and everybody is still living in the wake of the COVID-19 pandemic. We all need a friend in times of trouble and this is no different in the case of business organizations.

This year, 2021, the friend that your company needs more than ever, especially in these trying times, is data.

Given the disruption that this virus caused in the preceding year, enterprises need to start (if they haven’t already) befriending their own internal data, and perhaps external data as well if they are to at least stay viable and at most grow.

The following are some insights from respected data management leaders on how to make friends with your data this year:

  • “Data warehouses are not going to disappear. Data warehouses will continue to be an important legacy technology that organizations will use for mission-critical business applications well into the future.

    With the transition to the cloud, data warehouses got a fresh new look and offer some modern attractive capabilities including self-service and serverless.

    With the rise of the cloud, data lakes are the new kid on the block. Data lakes are becoming a commodity, a legacy technology in their own right. Their rapid emergence from the innovation stage means two things going forward.

    First, organizations will demand simpler, easier to manage, and more cost-effective means of extracting usable business intelligence from their data lakes, using as many data sources as possible.

    Second, those same organizations will want the above benefit to be delivered via tools that do not lock them into proprietary data management platforms.

    In short, 2021 will begin to see the rapid introduction and evolution of tools that allow users to keep their data lakes in one place and under their control while driving performance up and cost down.”

  • “Distributed analytical databases and affordable scalable storage are merging into a single new thing called either a unified analytics warehouse or a data lake house depending on who you’re talking to.

    Data lake vendors are scrambling to add ACID capabilities, improve SQL performance, add governance, resource management, security, lineage, and all the things that data warehouse vendors have been perfecting for the last three or four decades.

    During the ten years, while data lake software has been coalescing, analytical databases have seen their benefits and added them to their existing stacks: unlimited scale, support for widely varied data types, fast ingestion of streaming data, schema-on-read, and machine learning capabilities.

    Just like a lot of things used to claim to be cloudy before they really were, some vendors will claim to be a unified analytics warehouse when they’ve just jammed the two architectures together into a complicated mess, but everyone is racing to make it happen for real.

    I think the data warehouse vendors have an unbeatable head start because building a solid, dependable analytical database like Vertica can take ten years or more alone.

    The data lake vendors have only been around about ten years, and are scrambling to play catch-up.”

  • “One single SQL query for all data workloads

    The way forward is based not only on automation but also on how quickly and widely you can make your analytics accessible and shareable.

    Analytics gives you a clear direction of what your next steps should be to keep customers and employees happy, and even save lives. Managing your data is no longer a luxury, but a necessity–and determines how successful you or your company will be.

    If you can remove the complexity or cost of managing data, you’ll be very effective.

    Ultimately, the winner of the space will take the complexity and cost out of data management, and workloads will be unified so you can write one single SQL query to manage and access all workloads across multiple data residencies.”

  • “Expect more enterprises to declare the battle between data lakes and data warehouses over in 2021 – and focus on driving outcomes and modernizing.

    Data warehouses can continue to support reporting and business intelligence, while modern cloud data lakes support all analytics, AI and ML enablement far more flexibly, scalably, and inexpensively than ever – so enterprises can go transform quickly.

    Cloud migrations and related cloud data lake implementations will get demonstrably faster and easier as DIY approaches are replaced by turnkey SaaS platforms.

    Such solutions will slash production cloud data lake deployment times from months to minutes while controlling costs and providing the continuous operations, security and compliance, AI and ML enablement, and self-service access required for modern analytics initiatives.

    That means that migrations that used to take 9-12+ months are complete in a fraction of the time.”

  • “Co-locating analytics and operational data results in faster data processing to accelerate actionable insights and response times for time-sensitive applications such as dynamic pricing, hyper-personalized recommendations, real-time fraud and risk analysis, business process optimization, predictive maintenance, and more.

    To successfully deploy analytics and ML in production, a more efficient Data Architecture will be deployed, combining OLTP (CRM, ERP, billing, etc.) with OLAP (data lake, data warehouse, BI, etc.) systems with the ability to build the feature vector more quickly, and with more data for accurate, timely results.”

To summarize the various points made by these industry pundits:

1

SQL-driven data warehouses are here to stay and will continue to be the data analytics platform of choice for enterprises in the current year.

2

Data management platforms that integrate well with existing data lakes will dominate as opposed to platforms that focus on one or the other.

3

Data management platforms that have built-in AI/ML functionalities will dominate as well, as this eliminates the cost and complexity of separate AI/ML analytics platforms.

4

Data management platforms that are cloud-ready will also have an edge over those that are not.

Is there a data management platform that possesses all these qualities and has a proven track record in Fortune 500 companies?

Yes, there is. It’s called Greenplum. Read about it here.