The Data Science Method and Generative AI/data-science-insights/the-data-science-method-and-generative-ai

The Data Science Method and Generative AI

The Data Science Method and Generative AI

Data is the lifeblood and takes a whole lot of caliber and skill to make the businesses drown-proof against adversities. Fail-proofing your business is the key; that shall unlock a humungous opportunity of benefits ahead. As is world-renowned, Generative AI is making waves in the global Artificial Intelligence panorama. It is predicted to enhance its capabilities beyond the traditional Artificial intelligence loop. On one hand, data science is restructuring the business landscape with a targeted approach; Generative AI is sure to provide it with a bird’s eye viewpoint.

Business Standard reveals that US Businesses plan to invest 67% more in Generative AI in 2024. That is a clear path to a roadmap that sets your career trajectory on fire. Easy and handsome remuneration; alongside enjoying a diversified career profile are some of the perks of enhancing your Data science skills down the line.

“Thinking Like a Data Scientist”- that is the rule of law for winning tricky business scenarios. Data scientists are revealing big business insights via targeted generative AI advancements. Let us take you through the latest in AI; and what it takes to understand the latest addition in the tech zone; ie- “Thinking Like a Data Scientist.

Understanding Generative AI:

McKinsey and Company explain Generative AI the algorithms that can be used to create new content, including code, audio, images, text, simulations, and videos. It is one of the recent breakthroughs the Artificial intelligence industry has experienced today.

Why use AI Tools for data science?

  • They automate repetitive tasks
  • Enable professionals to allocate their time and resources toward more strategic aspects of data analysis
  • Enhance accuracy and consistency in data handling
  • Reduces margin of human error
  • Ensures reliable outcomes
  • Facilitate data handling, extracting insightful patterns, and predictions that are humanly impossible to discern
  • AI can foster innovation by offering a platform to experiment, optimize, and deploy models to drive actionable insights
  • Steer organizations toward data-driven decision-making; and strategic planning culture

Popular GenAI Tools for Data Scientists:

To be effective and efficiently utilized at work, data scientists work their way up the ladder with Artificial intelligence to create algorithms and models that can make predictions and automate complex tasks. New AI Statistics show that 35% of companies are using AI and 42% of companies are exploring Artificial intelligence for its implementation in the future.

This calls for an in-depth understanding of the Generative AI tools and strategies that can be deployed effectively; to crease out the process of clear decision making. Let us take a deep dive to understand the top 4 GenAI tools set to decipher data science:

Open AI is back with yet another revelation and a leveled-up Large Language Model beyond GPT 3.5. GPT 4 targets high invention and accurate solutions; offering a much safer and more stable resolve than the previous generation models.

  1. GPT - 4
    • Positives:
      • Possesses over 100 trillion parameters
      • Enhances Factual performance
      • Image input and multilingual capabilities
      • Amplified Steerability
      • Human-like performance on most of the benchmarks
      • Cost effective
      • Consistent and reliable
      • Time saving
      • Free Version
    • Negatives:
      • Can yield incorrect answers or responses
      • Extremely biased

    A transformer-based language model dealing with extracting existing language models such as OpenAI Codex; with 41.4 billion parameters. It offers a variety of programming languages inclusive of C#, Ruby, Scala, Java, Javascript, PHP, Go, and Rust; while excelling in Python and C++.

    • Positives:
      • Smart filtering post the large-scale code generation
      • Transformer-based language model
      • Datasets and solutions available on GitHub
      • Exemplary programming capabilities
      • Access to over 13000 sample training tasks
      • Generates code at an unprecedented scale
      • Allows for efficient critical thinking by experience
      • Free tool
    • Negatives:
      • User-dependent learning
      • Can yield incorrect results
  3. BARD AI

    Bard, a popular Chatbot and content generation tool developed by Google; uses LaMDA; and is viewed as a counterpart to Chat GPT. Still in its initial stages of development; it is still in its experimental phase.

    • Positives:
      • Available through individual Google accounts
      • Capable of assisting with tasks involving software programming
      • Ethical and transparent AI development approach
      • Pre-tested by numerous testers
    • Negatives:
      • No conversation history enabled
      • No access through Google account

    GitHub in partnership with OpenAI offers itself as a code completion Artificial intelligence tool.

    • Positives:
      • Supports multiple programming languages
      • Learns from Open-Source code
      • Autocompletion for documents available
      • Rapid prototyping and exploration
      • Context-aware suggestions
      • Collaborative coding
      • Customization and adaptation
      • Enhances developers’ productivity
    • Negatives:
      • Code quality and security may be compromised
      • Over-reliance on Autocomplete
      • Comes at a cost

How does a Data engineer benefit from Generative AI?

While Generative AI has the potential to automate certain data engineering aspects; Generative AI models excel at creating SQL and Python code, debugging, and optimizing it. It also assists in improving the productivity of data engineers and enables them to create more data pipelines and data products.



    Generative AI augments the assessment of targeted business initiatives; while including identifying added outcomes, benefits, impediments, risks, and other metrics.


    It enables stakeholders to gain a clear understanding of the key decisions, KPIs, and metrics to facilitate the success of business initiatives.


    GenAI allows enough room for identifying, exploring, and understanding business initiatives like a pro. It strengthens a business’s performance propensity and optimizes decision-making.


    GenAI tools via AI prompts enable identifying and exploring the factors that impact use case evaluation and implementation feasibility; fostering the use case prioritization process.


    It allows for detailed information about the targeted use case, to identify and explore analytical scores and their supporting features.


    This is an entirely novel addition to the GenAI tool features that allows for an in-depth exploration of the analytical algorithms.


    It enables the validation process of the linkage between analytic scores and the recommendations that will drive the precise decisions; that eventually optimize prioritized use cases.


    GenAI ensures a holistic feedback look to build analytic scores that can be continuously deployed to learn and adapt.


Generative AI is the efficient means to further the acceleration and expansion of the “Thinking Like a Data Scientist” method. Mastering data engineering like a pro is an act of sheer skill and expertise gained via credible data science credentials that can make a marked difference in the way you are perceived as a data professional. In the times today, Statista reveals that the generative AI market is projected to reach a whopping USD 207 billion by 2030. This makes the air clearer and lighter while landing the most preferred data science jobs via the most trusted names backing your data prowess. Making the right move now shall pave the way toward a thriving data science career.

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