what is data science ibm coursera week 1 answers
Data Science: The Sexiest Job in the 21st Century
Practice Assignment
1. Why are companies looking for well-rounded individuals when hiring data scientists?
- Because data scientists need to be artistic
- Because data scientists are responsible for conducting biological experiments
- Because data science requires a combination of skills, including subject matter expertise, programming, and communication abilitie
- Because data science jobs are purely focused on statistics
Explanation:
Data scientists need to combine technical skills like programming and statistics with subject matter expertise and strong communication skills to effectively extract insights and present findings to stakeholders. These diverse skills are essential for solving real-world problems.
2. Why is there a growing demand for data scientists and analytics professionals in various industries?
- Because data scientists are primarily involved in managing customer analytics initiatives
- Because data scientists are mainly responsible for convincing C-suite executives about the benefits of data and analytics
- Because of the digital revolution and the need to analyze big data for effective decision-making
- Because data scientists are primarily focused on conducting biological experiments
Explanation:
The digital revolution has led to the generation of vast amounts of data. Companies need data scientists to analyze this data and extract actionable insights, which are critical for making strategic and informed decisions in a competitive market.
3. Due to the shortage of data scientists, employers are willing to pay top salaries for their talent, with an average base salary for data scientists reported as $112,000.
- True
- False
Explanation:
The demand for skilled data scientists far exceeds the supply, making it a highly lucrative profession. Reports indicate that data scientists often earn high salaries due to their specialized expertise and the critical role they play in organizations.
Practice Quiz: Defining Data Science
Practice Assignment
4. Imagine you’re working for a retail company that wants to optimize its product offerings and marketing strategies. In this scenario, you would use Data Science for:
- Conducting biological experiments to enhance the quality of retail products.
- Creating artistic visualizations for in-store displays to attract customers.
- Analyzing customer purchase data to identify trends and tailor product recommendations.
- Developing algorithms to predict future stock market trends for investment decisions.
Explanation:
Data science in retail focuses on analyzing customer purchase data to identify patterns, optimize product offerings, and create personalized recommendations. This data-driven approach helps improve marketing strategies and drive sales.
5. What is the role of data analysis in Data Science and how does it contribute to decision-making?
- Data analysis involves gathering insights from data and helps make informed decisions.
- Data analysis is a recent concept leveraging computing power for pattern recognition.
- Data analysis is no longer relevant due to advanced data visualization tools.
- Data analysis focuses on formulating business questions for organizations.
Explanation:
Data analysis is essential in data science as it helps derive meaningful insights from data. These insights guide decision-making, whether it’s about business strategies, product development, or customer targeting.
6. In a healthcare context with patient data, medical histories, and treatment outcomes, Data Science can be applied to:
- Creating artistic visualizations of patient data for aesthetics.
- Automating patient diagnoses and treatment decisions.
- Predicting future medical advancements using patient data.
- Analyzing patient data for personalized treatment plans.
Explanation:
Data science in healthcare helps analyze large datasets, such as patient histories and treatment outcomes, to identify patterns that can guide personalized treatment plans and improve patient care.
7. Considering an individual with a marketing background transitioning to data science, how might their marketing experience contribute to their data science journey?
- Their marketing skills could enhance their ability to perform complex statistical analyses.
- Their marketing knowledge could help predict future data science trends.
- Their marketing background might assist in interpreting data to generate actionable insights.
- Their marketing expertise could replace the need for data analysis, given its relevance to business.
Explanation:
A marketing background gives individuals an understanding of customer behavior and business needs, which is invaluable in interpreting data and translating insights into actionable strategies for businesses.
8. You have just started your career as a data scientist. Which of the following skills should you develop to succeed as a data scientist? You should:
- Dedicate your efforts to becoming proficient in technical skills such as mathematics and statistical modeling.
- Focus primarily on mastering coding languages and analytics tools to excel in data analysis.
- Cultivate curiosity, develop strong positions, and learn to communicate insights effectively through storytelling.
- Prioritize industry-specific knowledge above all else to establish a competitive advantage as a data scientist.
Explanation:
Curiosity and the ability to communicate data insights are critical for a data scientist. While technical skills are important, the ability to present findings clearly and engage stakeholders through storytelling is key to making an impact.
Graded Quiz: Defining Data Science
Graded Assignment
9. You are a data scientist about to start a new project. What would one of your key roles be?
- Focusing solely on data visualization
- Asking questions to clarify the business need
- Designing data collection methods
- Collecting vast quantities of data from varied sources
Explanation:
A key role of a data scientist is to ask insightful questions to understand and define the business problem. This guides the project and ensures the analysis aligns with organizational goals.
10. When did the term "data science" come into existence and who is credited with coining the term?
- Early 2000s, led by business analysts
- 1990s, DJ Patil and Andrew Gelman
- 1960s, no specific person credited
- 2009-2011, DJ Patil or Andrew Gelman
Explanation:
The term “data science” gained prominence around 2009-2011, with individuals like DJ Patil and Andrew Gelman contributing to its popularization. It represents the intersection of statistics, programming, and domain expertise to extract insights from data.
11. As an aspiring data scientist, what primary qualities should you possess to succeed in the field?
- Extensive experience with data analysis software.
- Proficiency in analytics platforms and software.
- Curiosity and storytelling skills.
- Strong expertise in a specific industry.
Explanation:
Curiosity drives exploration and hypothesis generation, while storytelling skills help communicate insights effectively to stakeholders. These qualities complement technical skills and are crucial for success in data science.
Graded Quiz: What Data Scientists Do
Graded Assignment
12. You are a new data scientist. You have been tasked with coming up with a solution for reducing traffic congestion and improving transportation efficiency. How would you go about it?
- Suggest implementation of strict speed limits and traffic fines
- Gather and analyze streetcar operations data and identify congested routes
- Suggest implementation of surge charges for ride-sharing services.
- Suggest creating more parking lots and garages in the city
Explanation:
Using data science, you can analyze transportation data to identify patterns and problem areas, such as congested routes, and propose evidence-based solutions to improve efficiency and reduce congestion.
13. Imagine you take a taxi ride where the initial fare is a fixed amount, and the fare increases based on both the distance traveled and the time spent in traffic. Which concept in data analysis does this scenario closely resemble?
- Nearest neighbor algorithm
- Regression analysis
- Unstructured data extraction
- Data visualization with R
Explanation:
Regression analysis is a statistical method that models relationships between variables, such as the fixed base fare (constant) and the charges based on distance and time (variables).
14. You have to pick a file format which meets the following conditions: a) is self-descriptive for internet-based information sharing b) readable by both humans and machines c) Facilitates easy data sharing between different systems. Which file format would you pick?
- Microsoft Excel Open XML Spreadsheet (XLSX)
- Delimited text file formats (CSV/TSV)
- Extensible Markup Language (XML)
- JavaScript Object Notation (JSON)
Explanation:
XML is a markup language designed for storing and transporting data. It is self-descriptive, easy to read by both humans and machines, and widely used for data sharing across platforms.