topic: KM-01-KT06 Attributes of a Data Science Practitioner

Learning Outcomes

  • KT0601: Data science jobs, roles, and career path progression
  • KT0602: Attributes of a data science practitioner
  • KT0603: Oral and written communication skills
  • KT0604: Attention to detail
  • KT0605: Analytical thinking

KT0601: Data Science Jobs, Roles, and Career Path Progression

What Jobs Do Data Scientists Do? A career as a data scientist involves turning data into value. Responsibilities include retrieving and analyzing data to improve business performance and building AI tools to automate tasks.

Types of Data Science Jobs:

  1. Data Scientist
  2. Data Analyst
  3. Data Engineer
  4. Data Architect
  5. Data Storyteller
  6. Machine Learning Scientist
  7. Machine Learning Engineer
  8. Business Intelligence Developer
  9. Database Administrator
  10. Technology Specialized Roles

Roles and Responsibilities of Key Jobs:

  1. Data Analyst:

    • Visualize, process, and analyze massive amounts of data.
    • Work with tools like SQL, Python, R, and SAS.

  2. Data Engineer:

    • Design and maintain scalable data ecosystems.
    • Work with technologies like Hive, NoSQL, and ETL tools.

  3. Database Administrator:

    • Manage and maintain databases, ensuring proper functioning.
    • Backup, recover, and implement security measures.

  4. Machine Learning Engineer:

    • Develop machine learning systems, run A/B testing, and implement algorithms.
    • Strong knowledge of Python, Java, and statistics is required.

  5. Data Scientist:

    • Use data analysis and processing techniques to solve business challenges.
    • Perform predictive analysis and integrate unstructured data.

  6. Data Architect:

    • Create blueprints for data management and ensure integration, security, and scalability.
    • Expertise in data warehousing and ETL processes.

KT0602: Attributes of a Data Science Practitioner

Types of Attributes in Data Science:

  1. Nominal Attributes:

    • Represent categories or labels (e.g., skin color, education status).

  2. Binary Attributes:

    • Two classes: 0 or 1, true or false (e.g., drinker status, medical test results).

  3. Ordinal Attributes:

    • Attributes with a ranking but unknown distance between values (e.g., food quantity: small, medium, large).

  4. Numeric Attributes:

    • Quantitative values, can be either interval or ratio scaled (e.g., temperature, height).

Importance of Attributes in Data Analysis:

  • Nominal, Binary, Ordinal, and Numeric Attributes help in categorizing and analyzing data efficiently in machine learning, statistics, and data analytics.

KT0603: Oral and Written Communication Skills

Verbal and written communication involve articulating thoughts effectively through speech and writing. It also includes non-verbal communication skills like body language and active listening, which are vital for clear expression and understanding.

KT0604: Attention to Detail

Definition: Attention to detail refers to the ability to complete tasks thoroughly, ensuring that every part of the task is done with precision, regardless of its size.

Importance:

  • Minimizes errors, increases productivity, and ensures the quality of work.
  • Examples of attention to detail skills include organizational, time management, analytical, observational, and active listening skills.

How to Improve Attention to Detail:

  1. Get Organized Maintain a clean workspace and plan tasks in advance.
  2. Create Lists Keep track of tasks to stay focused.
  3. Maintain a Routine Reduce distractions and improve focus.
  4. Prioritize Quality Focus on delivering high-quality work in every task.
  5. Practice Focus-Enhancing Games Engage in activities that strengthen concentration.
  6. Learn to Meditate Improve mental clarity and attention through meditation.

KT0605: Analytical Thinking

Definition: Analytical thinking involves breaking down complex information, identifying patterns, and checking whether statements logically follow based on the facts.

Key Components:

  • Reasoning: Analyzing alternatives and making well-informed decisions.
  • Critical Thinking: Seeking information, analyzing alternatives, and forming conclusions.

Application in the Workplace:

  • Analytical thinking helps solve problems, improve processes, and make data-driven decisions that contribute to business growth.
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