Understanding Data Deluge and Analytics Levels
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Questions and Answers

What is the primary focus of the Advanced Analytics Framework?

  • Experimentation and hypothesis testing
  • Descriptive statistics and reporting
  • Machine learning and optimization (correct)
  • Data mining

In the context of data modeling, what should be done to ensure the best model is selected?

  • Use complex algorithms without testing
  • Only test one model to avoid confusion
  • Validate all models and select the best according to the goal (correct)
  • Estimate data outputs without validation

What question relates to the initial process step of data handling?

  • Is the data privacy compliant?
  • How many models can be tested?
  • What predictions can be made from the data?
  • Do you need to aggregate/create the data? (correct)

What might indicate a need for data transformation or imputation?

<p>There are anomalies or patterns in the data (A)</p> Signup and view all the answers

Which of the following best describes OLAP's function in analytics?

<p>To ask complex queries and compile reports (A)</p> Signup and view all the answers

What primarily drives the increase in data velocity?

<p>Streaming data feeds and point-of-sale systems (A)</p> Signup and view all the answers

Which of the following is NOT a reason cited for the big data explosion?

<p>Improved data processing algorithms (A)</p> Signup and view all the answers

Which statement about big data is accurate?

<p>Big data signifies the low cost of storing compared to the cost of discarding it. (C)</p> Signup and view all the answers

What are companies increasingly seeking to do with data from social media?

<p>Perform sentiment analysis (A)</p> Signup and view all the answers

What factor contributes to the demand for big data solutions?

<p>Increasing requirements around real-time reporting (C)</p> Signup and view all the answers

Which of the following does NOT typically generate data?

<p>Traditional paper-based systems (A)</p> Signup and view all the answers

How has the role of analytics evolved as a result of the data deluge?

<p>Analytics has become a necessity for every company. (B)</p> Signup and view all the answers

What factors are associated with big data?

<p>Data volume, velocity, variety, variability, complexity (C)</p> Signup and view all the answers

Which of the following contributes to increasing data volume?

<p>Social media usage and automated tracking devices (A)</p> Signup and view all the answers

Which of the following types of data is classified as unstructured?

<p>Digital images (B)</p> Signup and view all the answers

What does data variability refer to?

<p>The flow of data changes over time and its values vary (D)</p> Signup and view all the answers

What challenge does data complexity present?

<p>It complicates merging, cleansing, and transforming data (C)</p> Signup and view all the answers

Which of the following is NOT a characteristic defined under data velocity?

<p>Archiving historical data (D)</p> Signup and view all the answers

How does the use of machines communicating with each other affect data?

<p>It increases data velocity (B)</p> Signup and view all the answers

Which of the following is an example of structured data?

<p>Database tables (A)</p> Signup and view all the answers

What aspect of data complexity complicates its management?

<p>Diverse data formats and sources (C)</p> Signup and view all the answers

What is a primary characteristic of data scientists?

<p>They have a blend of technical skills and curiosity. (B)</p> Signup and view all the answers

Which programming languages are commonly used by data scientists?

<p>SAS, R, and Python (C)</p> Signup and view all the answers

Which analytical technique is NOT typically associated with data scientists?

<p>Graphic design (C)</p> Signup and view all the answers

What type of data do data scientists transform into more usable formats?

<p>Large amounts of unruly data (D)</p> Signup and view all the answers

Why are data scientists increasingly important in businesses?

<p>They help solve business-related problems through data. (D)</p> Signup and view all the answers

Which of the following is a task that data scientists commonly perform?

<p>Transforming data into a usable format (D)</p> Signup and view all the answers

Which skill is essential for data scientists regarding data handling?

<p>Understanding statistical tests and distributions (A)</p> Signup and view all the answers

What type of problems do data scientists typically aim to address?

<p>Data-related business problems (B)</p> Signup and view all the answers

Which is NOT a task commonly expected of a data scientist?

<p>Providing customer service (C)</p> Signup and view all the answers

What is the primary purpose of developing a team of data scientists across a business?

<p>To diversify skills and enhance analytics capabilities. (B)</p> Signup and view all the answers

Which characteristic is essential for a Citizen Data Scientist?

<p>An interest in learning new methods and tools. (B)</p> Signup and view all the answers

Which specific skill is NOT listed as essential for a data scientist?

<p>Legal Knowledge (B)</p> Signup and view all the answers

Which of the following is an example of applied data science?

<p>Anomaly Detection in financial transactions. (A)</p> Signup and view all the answers

What motivates Citizen Data Scientists in their analytics pursuits?

<p>Frustration with repetitive reporting and a quest for new insights. (B)</p> Signup and view all the answers

Which of the following roles or tasks is NOT typically performed by a data scientist?

<p>Implementing marketing strategies. (A)</p> Signup and view all the answers

What is one of the goals of the data science process?

<p>To improve business efficacy through data-driven insights. (D)</p> Signup and view all the answers

Which of the following best exemplifies the concept of Citizen Data Scientists?

<p>Individuals who work independently with data to derive insights. (A)</p> Signup and view all the answers

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Flashcards

Data Deluge

The rapid increase in the amount of data being created and collected.

Analytics

The process of analyzing data to extract insights, trends, and patterns.

Data Science

The set of techniques used to analyze large volumes of data, often unstructured or semi-structured, to discover meaningful information.

Data Velocity

The growth in the speed at which data is generated and processed.

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Real-Time Reporting

The ability to access and process data in real-time as it is generated.

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Sentiment Analysis

The ability to extract information about user sentiment from social media data.

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Big Data

The idea that the cost of storing data is now lower than the cost of making decisions without that data.

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Big Data Threshold

The point where the amount, speed, and diversity of data surpass an organization's ability to store or process it efficiently for timely and accurate decision-making.

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Data Volume

The sheer quantity of data accumulated within an organization.

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Data Variety

The diverse types and formats of data collected by an organization.

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Data Variability

The way data changes over time, including seasonal fluctuations, peak demands, and evolving trends.

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Data Complexity

The complexity of data coming from diverse sources and in various formats, making it challenging to merge, clean, and transform.

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Structured Data

Data that is structured in a predefined format, often stored in tables with rows and columns.

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Unstructured Data

Data that does not conform to a fixed format, often text-based, multimedia, or other less organized forms.

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Data Transformation

The process of organizing and cleaning raw data to make it usable for analysis.

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Collect the data

The process of collecting data from different sources, often requiring evaluating data relevance and handling privacy concerns.

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Explore the data

Exploring the collected data to identify patterns, anomalies, and potential issues. This step involves analyzing data quality, completeness, and consistency.

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Model the data

Using algorithms and techniques to model the data and provide answers to the questions posed. This step involves selecting appropriate models and evaluating their performance.

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Validate the model

Testing the model's ability to generalize and predict outcomes on unseen data, aiming for accurate and reliable predictions.

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Deploy the model

Deploying the trained model to a production environment for real-world application, ensuring timely and efficient execution of the model.

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Citizen Data Scientist

Professionals who use data analysis tools and techniques to gain insights and solve problems, they are not trained as data scientists but possess a business understanding and analytical skills.

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Anomaly Detection

The process of identifying patterns and trends in data, often used for predicting future events or understanding customer behavior.

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Segmentation

A technique for classifying customers into distinct groups based on their characteristics, behavior, or preferences.

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Forecasting

The use of data analysis to predict future events or outcomes, such as sales, demand, or customer churn.

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Customer Transaction Behavior

The process of collecting and analyzing data about customer interactions to understand their behavior, preferences, and needs.

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Fraud Detection

The use of data analysis to identify and prevent fraudulent activities, such as credit card fraud or money laundering.

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Churn

Analyzing data to identify and understand the reasons why customers stop using a product or service

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Risk Analysis

Analyzing financial data to assess the risk of potential losses or defaults.

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Spending Optimization

The use of data analysis to identify patterns and trends in data to optimize resource allocation and improve efficiency.

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Collecting Prediction

The use of data analysis to improve the process of collecting data, ensuring accuracy and completeness.

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Data Scientist

A skilled professional who uses data analysis techniques to solve complex problems and uncover hidden insights in large amounts of data.

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Data Wrangling

The ability to collect, organize, and transform raw data into a more manageable and usable format.

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Analytical Techniques

Techniques like machine learning and deep learning that analyze data to predict future outcomes, discover patterns, and make better decisions.

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Data Storytelling

The ability to effectively communicate complex data insights to both technical and non-technical audiences.

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Data-Driven Decision Making

The ability to identify trends, patterns, and insights within data that can improve business performance.

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Data Scientist Shortage

The growing demand for data scientists due to the exponential increase in the volume of data being generated.

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Data Science Programming Languages

Programming languages like Python, R, and SAS are commonly used by data scientists to process, analyze, and visualize data.

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Data Integration

The ability to combine data from multiple sources to get a more complete picture and gain deeper insights.

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Data-Driven Business

The increasing importance of data in understanding customer behavior, market trends, and optimizing business processes.

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Statistical Analysis

The use of statistical methods and algorithms to analyze data and extract meaningful information.

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Study Notes

Data Deluge

  • Data deluge refers to the massive volume of data generated across various sources.
  • Sources like hospital patient registries, point-of-sale transactions, stock trades, website interactions, bank transactions, catalog orders, remote sensing images, airline reservations, web comments, tax returns, credit cards, and sensor data contribute to the deluge.
  • Every problem generates associated data.
  • Every company and individual will eventually require data analytics.

Consequences of the Data Deluge

  • Every problem inevitably creates data.
  • All companies and organizations will need analytics solutions to process that data.
  • Individuals will also need the capability to analyze data.

Levels of Analytics

  • Different levels of analytics, ranging from basic to advanced, provide varying degrees of insight
  • Raw data, clean data, statistical analysis, query drill down, reports (ad hoc and standard), alerts, and various levels of intelligence represent different analytic levels.
  • Understanding "what if", determining future trends ("what will happen next"), and insights into the causes of events ("why is this happening?") are higher levels of analysis.

Data Science

  • Data science combines domain expertise, advanced analytics, and software engineering to analyze large, diverse datasets.
  • It involves communication skills to share findings and actionable insights with stakeholders.

Reasons for the Big Data Explosion

  • Increased data velocity due to streaming data feeds, point-of-sale (POS) systems, radio-frequency identification (RFID) tags, smart metering, and larger, cheaper data storage.
  • Social media, automated business processes, mergers, and increasing online self-service applications contribute to the data volume explosion.

Big Data Definition

  • Big data emerges when the cost of storing information is less than the cost of discarding it.
  • Big data occurs when the volume, velocity, and variety of data exceed an organization's ability to process it for sound decision making.

Factors Associated with Big Data

  • Data volume, from social media, machines communicating, manufacturing innovations, and automated tracking.
  • Data velocity, including more automated business processes, social media use, self-service applications, and business integrations.
  • Data variety, encompassing structured and unstructured data types.
  • Data variability, which changes based on time trends and seasonality.
  • Data complexity, resulting from diverse data formats from numerous systems.

The Citizen Data Scientist

  • Data scientists are analytical experts capable of solving complex problems.
  • Citizen data scientists are individuals who have the tools and inclination to analyze data themselves.
  • Increasing tools and availability of data enable individuals to analyze it, leading to the requirement of more citizen data scientists.

Typical Job Duties & Responsibilities of a Data Scientist

  • Collection and transformation of large datasets.
  • Solving business problems using data, along with specific techniques.
  • Programming language proficiency.
  • Statistical knowledge regarding techniques and distributions.
  • Employing analytical techniques.
  • Communicating with IT and business stakeholders.
  • Identifying trends and patterns in data.

How to Find Citizen Data Scientists

  • There isn't a sufficient number of data scientists skilled in this area.
  • Analytics is important for society, and domain expertise is not required.
  • Easy-to-use analytics tools are increasing, and individuals can become citizen data scientists.

Characteristics of Citizen Data Scientists

  • Desire to learn and use data analysis tools independently
  • Willingness to analyze datasets and identify patterns.
  • Analytical mindset to address problems through data analysis and patterns.

Three Roles Working Together

  • Business analysts, citizen data scientists, and data scientists collaborate for optimal analytical results.

Data Scientist Skills

  • Communication and visualization are crucial in conveying results to decision-makers.
  • Knowledge of mathematics and statistics for analytical processes.
  • Computer science skills for effective data manipulation and analysis.
  • Domain knowledge specific to the problem area.

Applied Data Science

  • Examples demonstrating the application of data analysis techniques to solve real-world problems, ranging from retail to banking and government sectors.

Data Science Process

  • Defining the goal (classification, estimation, description)
  • Gathering and validating data
  • Exploring data patterns and abnormalities
  • Constructing models
  • Assessing and explaining results
  • Deploying the model to address business needs

Advanced Analytics Framework

  • Focuses on tasks like data mining and optimization used by businesses.
  • Techniques for optimization, data mining, and business value creation.

Traditional Analytics at Rest vs. Streaming Analytics

  • Traditional analytics uses batch data processed on stored data, whereas streaming analytics processes data as it is generated in real time.
  • Traditional analytics delays insights, while streaming analytics provides immediate feedback.
  • Critical differences between these frameworks and their effectiveness in processing data.

Analytical Methods and Applications

  • Machine learning, statistical analysis, forecasting, text analytics, optimization are various analytical techniques.
  • These methods address problem-solving in diverse fields and improve decision-making.

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Data Deluge Lecture Notes PDF

Description

This quiz explores the concept of data deluge, the massive volume of data generated from various sources, and the implications it has on companies and individuals. It also covers different levels of analytics that provide insights into this data. Test your knowledge on the importance of data analytics in today's world.

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