Podcast
Questions and Answers
Which of the following best describes the primary focus of data science?
Which of the following best describes the primary focus of data science?
- Creating social media applications.
- Developing new programming languages.
- Collecting, analyzing, and making decisions based on data. (correct)
- Designing computer hardware.
What is a key goal of data science?
What is a key goal of data science?
- To identify patterns in data through analysis and predict future events. (correct)
- To replace human decision-making entirely.
- To store as much data as possible without regard to its relevance.
- To create visually appealing charts without analyzing the data.
Which of the following is NOT listed as a potential benefit of employing data science in business?
Which of the following is NOT listed as a potential benefit of employing data science in business?
- Predictive analysis.
- Identifying hidden information.
- Better decisions.
- Hiding information. (correct)
What does the increase in demand for data scientists and data engineers indicate about the field?
What does the increase in demand for data scientists and data engineers indicate about the field?
Which of the following is represented by the '3V model' of data?
Which of the following is represented by the '3V model' of data?
In the context of the '3V model,' what does 'velocity' refer to?
In the context of the '3V model,' what does 'velocity' refer to?
According to the information, what is the approximate fold increase in data volume since 2010?
According to the information, what is the approximate fold increase in data volume since 2010?
Which of the following is an example of computer vision?
Which of the following is an example of computer vision?
Which of the following tasks is commonly associated with Natural Language Processing (NLP)?
Which of the following tasks is commonly associated with Natural Language Processing (NLP)?
Which of the following is an area where AI is applied to introduce more automation?
Which of the following is an area where AI is applied to introduce more automation?
How are neural networks inspired?
How are neural networks inspired?
What is the primary reason data science utilizes AI techniques?
What is the primary reason data science utilizes AI techniques?
Which of the following is considered an 'output' of data science?
Which of the following is considered an 'output' of data science?
What does 'analytics' in data science primarily focus on?
What does 'analytics' in data science primarily focus on?
Based on the provided data concerning height and weight, what general relationship can be observed?
Based on the provided data concerning height and weight, what general relationship can be observed?
According to the passage, what tool might be helpful in estimating the weight of a woman of 73 inches?
According to the passage, what tool might be helpful in estimating the weight of a woman of 73 inches?
What programming languages are considered important for data scientists?
What programming languages are considered important for data scientists?
Which of the following is NOT explicitly mentioned as a data skill necessary to be a data scientist?
Which of the following is NOT explicitly mentioned as a data skill necessary to be a data scientist?
Which of the following is NOT one of the 'basic skills' for a data scientist according to the material?
Which of the following is NOT one of the 'basic skills' for a data scientist according to the material?
What should a data scientist consider when addressing ethics, bias and privacy?
What should a data scientist consider when addressing ethics, bias and privacy?
What is the primary function of data preprocessing in the data mining process?
What is the primary function of data preprocessing in the data mining process?
Which of the following is the best description of 'Predictive Analytics'?
Which of the following is the best description of 'Predictive Analytics'?
Which stage of the typical data science process focuses on representing data using charts, plots, and infographics?
Which stage of the typical data science process focuses on representing data using charts, plots, and infographics?
What is the role of 'Business Application' in the data science process?
What is the role of 'Business Application' in the data science process?
What is the central idea behind machine learning?
What is the central idea behind machine learning?
When is machine learning particularly useful?
When is machine learning particularly useful?
What characterizes supervised learning in machine learning?
What characterizes supervised learning in machine learning?
What are the two main types of supervised learning?
What are the two main types of supervised learning?
When is 'classification' used in supervised learning?
When is 'classification' used in supervised learning?
What type of problem is best addressed using 'regression'?
What type of problem is best addressed using 'regression'?
How does unsupervised learning differ from supervised learning?
How does unsupervised learning differ from supervised learning?
What is another name for unsupervised learning?
What is another name for unsupervised learning?
In general, what distinguishes data scientists from machine learning engineers?
In general, what distinguishes data scientists from machine learning engineers?
What is the role of cloud computing in data science, particularly when dealing with massive datasets?
What is the role of cloud computing in data science, particularly when dealing with massive datasets?
What does 'Model Deployment' involve in the context of data science?
What does 'Model Deployment' involve in the context of data science?
Consider a scenario where an analyst is creating a predictive model for stock prices. Extensive historical data is available, updated in real-time, but the relationships between variables are constantly shifting. Which machine learning application is BEST suited?
Consider a scenario where an analyst is creating a predictive model for stock prices. Extensive historical data is available, updated in real-time, but the relationships between variables are constantly shifting. Which machine learning application is BEST suited?
A data scientist is tasked with building a fraud detection system. They identify that instances of fraud are rare (0.1% of transactions) and that fraudulent transactions often involve complex money laundering schemes. What preprocessing step would be MOST crucial before applying machine learning?
A data scientist is tasked with building a fraud detection system. They identify that instances of fraud are rare (0.1% of transactions) and that fraudulent transactions often involve complex money laundering schemes. What preprocessing step would be MOST crucial before applying machine learning?
Flashcards
What is Data Science?
What is Data Science?
Data Science is concerned with the collection, analysis, and decision-making of data.
Goal of Data Science
Goal of Data Science
To identify patterns in data through analysis and predict future events.
Benefits of Data Science
Benefits of Data Science
Making better decisions, predictive analysis, and identifying hidden information.
Why is Data Science important now?
Why is Data Science important now?
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Velocity (3V model)
Velocity (3V model)
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Volume (3V model)
Volume (3V model)
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Variety (3V model)
Variety (3V model)
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Computer Vision
Computer Vision
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Voice Recognition
Voice Recognition
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Natural Language Processing
Natural Language Processing
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Robotics (AI)
Robotics (AI)
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Neural Network
Neural Network
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Data Science & AI
Data Science & AI
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Analysis vs. Analytics
Analysis vs. Analytics
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Data Gathering
Data Gathering
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Data Analysis
Data Analysis
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Data Preprocessing
Data Preprocessing
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Predictive Analytics
Predictive Analytics
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Knowledge Extraction
Knowledge Extraction
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Data Visualization
Data Visualization
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Machine Learning
Machine Learning
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Supervised Learning
Supervised Learning
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Classification (ML)
Classification (ML)
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Regression
Regression
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Unsupervised Learning
Unsupervised Learning
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Data Scientist Skills
Data Scientist Skills
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Skills: Data Scientist
Skills: Data Scientist
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Study Notes
- Data Science involves the collection, analysis, and decision-making related to data.
- The goal of data science is to identify patterns in data through analysis and predict future events.
Employing Data Science
- Businesses can make better decisions.
- Predictive analysis can be used to identify what is going to occur next.
- Data science helps find patterns in data and identify hidden information.
Importance of Data Science
- Analysis of data requires competent practitioners to provide actionable insights.
- Many industries use data science, including banking, consultancy, healthcare, and manufacturing.
- Demand for data scientists and data engineers has tripled, rising 231% in five years.
The 3V Model for Data
- Velocity refers to the speed at which data is accumulated.
- Volume is the size and scope of the data.
- Variety is the array of data and types (structured and unstructured).
- The increase in size of data is a 50-fold volume increase from 2010.
Acritical Intelligence
- Includes Vision, Voice recognition, Natural language processing, Robotics, Neural Network.
Computer Vision
- Branch of AI which uses digital images, videos, and other visual inputs to allow computers and systems to extract useful information.
Voice Recognition
- Voice recognition has become more popular and useful using AI.
- Ex: Amazon's Alexa and Apples Siri.
Natural Language Processing
- Helps computers in understanding how people write and speak,
Robotics
- Robotics' goal is to build intelligent robots using AI.
Neural Network
- A technique in Artificial Intelligence that trains machines to process data inspired by the human brain
Data Science vs Acritical Intelligence
- Data science uses AI because of the volume of data that can't be handled; machine learning is utilized.
- Data Science Output can be analysis (past or present) or analytics (predicted).
Analysis
- In the dataset, the values are sorted.
- This dataset contains the sorted list of heights and weights of people
- Heights range from 58 to 72, weights range from 115 to 164
- Data indicates weight increases with height.
Analytics
- Machine learning algorithms can be used to predict the weight of a person given their height.
Skills for Data Scientists
- A strong knowledge of basic statistics and AI is needed.
- Computer science skills are needed to handle complex datasets with programming languages like R or Python.
- The ability to visualize and express data and analysis in a meaningful way is needed.
Ethics, Bias, and Privacy in Data Science
- Issues can be traced back to the origin of the data, requiring considerations of collection methods and intended use.
Requirements to be a Data Scientist
- Data Skill: Database, SQL and Hadoop or Spark
- Programming Skills: Python, R
- Other Requirements: Cloud computing, Data pre-processing, Data visualization, Deep learning, Machine Learning, Model deployment
Typical Data Science Process
- Data Gathering collects and analyzes data from multiple sources to find insights.
- Data Analysis extracts meaningful information from data for making conclusions.
- Data Preprocessing involves cleaning, converting, and combining data to prepare it for analysis.
- Predictive Analytics predicts future events using data analysis, machine learning, AI, and statistics.
- Knowledge Extraction extracts knowledge through machine learning, natural language processing, and data mining.
- Data Visualization uses standard images such as charts, plots, infographics, and even animations.
- Business Application helps businesses in obtaining comprehensive market, competitive, and consumer information.
Machine Learning
- An AI application that uses statistical methods for computers to learn and make decisions without being programmed.
- Used when: Human expertise doesn't exist, humans can't explain expertise, solutions change in time, solutions need to be adapted.
- Consists of Supervised learning and Unsupervised Learning
Supervised Learning
- Supervised learning uses well-labeled training data to predict outputs.
- Two types: classification and regression.
Classification
- Used when output is categorical with two or more classes.
Regression
- Regression is used when the output variable is a real or continuous value.
Unsupervised Learning
- Machine learns from unlabeled data to discover patterns known as Clustering.
Data Scientists vs Machine Learning Engineers
- A Data Scientist has Machine Learning knowledge to some extent but Machine Learning Engineers have it as domain knowledge.
- Data scientists should have extensive domain knowledge in the fields in which they work.
- Machine Learning Engineers work under the vision of Data Scientists.
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