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?

There are anomalies or patterns in the data Signup and view all the answers

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

To ask complex queries and compile reports Signup and view all the answers

What primarily drives the increase in data velocity?

Streaming data feeds and point-of-sale systems Signup and view all the answers

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

Improved data processing algorithms Signup and view all the answers

Which statement about big data is accurate?

Big data signifies the low cost of storing compared to the cost of discarding it. Signup and view all the answers

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

Perform sentiment analysis Signup and view all the answers

What factor contributes to the demand for big data solutions?

Increasing requirements around real-time reporting Signup and view all the answers

Which of the following does NOT typically generate data?

Traditional paper-based systems Signup and view all the answers

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

Analytics has become a necessity for every company. Signup and view all the answers

What factors are associated with big data?

Data volume, velocity, variety, variability, complexity Signup and view all the answers

Which of the following contributes to increasing data volume?

Social media usage and automated tracking devices Signup and view all the answers

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

Digital images Signup and view all the answers

What does data variability refer to?

The flow of data changes over time and its values vary Signup and view all the answers

What challenge does data complexity present?

It complicates merging, cleansing, and transforming data Signup and view all the answers

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

Archiving historical data Signup and view all the answers

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

It increases data velocity Signup and view all the answers

Which of the following is an example of structured data?

Database tables Signup and view all the answers

What aspect of data complexity complicates its management?

Diverse data formats and sources Signup and view all the answers

What is a primary characteristic of data scientists?

They have a blend of technical skills and curiosity. Signup and view all the answers

Which programming languages are commonly used by data scientists?

SAS, R, and Python Signup and view all the answers

Which analytical technique is NOT typically associated with data scientists?

Graphic design Signup and view all the answers

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

Large amounts of unruly data Signup and view all the answers

Why are data scientists increasingly important in businesses?

They help solve business-related problems through data. Signup and view all the answers

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

Transforming data into a usable format Signup and view all the answers

Which skill is essential for data scientists regarding data handling?

Understanding statistical tests and distributions Signup and view all the answers

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

Data-related business problems Signup and view all the answers

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

Providing customer service Signup and view all the answers

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

To diversify skills and enhance analytics capabilities. Signup and view all the answers

Which characteristic is essential for a Citizen Data Scientist?

An interest in learning new methods and tools. Signup and view all the answers

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

Legal Knowledge Signup and view all the answers

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

Anomaly Detection in financial transactions. Signup and view all the answers

What motivates Citizen Data Scientists in their analytics pursuits?

Frustration with repetitive reporting and a quest for new insights. Signup and view all the answers

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

Implementing marketing strategies. Signup and view all the answers

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

To improve business efficacy through data-driven insights. Signup and view all the answers

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

Individuals who work independently with data to derive insights. Signup and view all the answers

Signup and view all the answers

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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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.