| What is the first step to understanding data science work? | Understand the business problem. |
| Who in the meeting asks relevant questions to clients? | Emma. |
| What is a trait of a good data scientist? | Being curious and asking wise questions. |
| What follows understanding the business problem? | Data acquisition. |
| From where can data be gathered? | Servers, logs, databases, APIs, online repositories. |
| Is finding the right data an easy task? | No, it takes time and effort. |
| What comes after data is gathered? | Data preparation. |
| What does data preparation involve? | Data cleaning and data transformation. |
| Which step is the most time-consuming in data preparation? | Data cleaning. |
| What are some challenges in data cleaning? | Inconsistent data types, misspelled attributes, missing values, duplicate values. |
| What happens in data transformation? | Data is modified based on defined mapping rules. |
| Name two ETL tools used in projects. | Talend and Informatica. |
| What does EDA stand for? | Exploratory Data Analysis. |
| Why is EDA important? | It helps understand data structure and choose the right feature variables for model development. |
| What could happen if EDA is skipped? | The wrong tables might be chosen leading to an inaccurate model. |
| After EDA, what does a data scientist proceed with? | Data modeling. |
| Name three machine learning techniques Emma applies. | K-Nearest Neighbors (KNN), Decision Tree, Naive Bayes. |
| On what is the model trained? | Training data set. |
| Which language does Emma prefer for data modeling? | Python. |
| What are other languages/tools for data modeling? | R and SAS. |
| After modeling, what step does Emma proceed to? | Visualization and communication. |
| Which tools can Emma use for visualization? | Tableau, Power BI, and Click View. |
| Why does Emma meet clients after visualization? | To communicate business findings. |
| What does Emma do after communicating findings? | Deploys and maintains the model. |
| Where is the model tested? | In a pre-production environment. |
| Is being a data scientist fun? | Yes, it has many interesting aspects but also its own share of challenges. |
| How is data science impacting genomic data? | It provides a deeper understanding of genetic issues and drug reactions. |
| Which logistic companies have benefited from data science? | DHL and FedEx. |
| How have logistic companies benefited? | They have discovered efficient routes, delivery times, and transport modes. |
| What can data science predict regarding employees? | Employee attrition and the key variables influencing turnover. |
| How do airline companies benefit from data science? | They can predict flight delays and notify passengers beforehand. |
| List some roles offered to data scientists. | Data Analyst, Machine Learning Engineer, Deep Learning Engineer, Data Engineer, Data Scientist. |
| What is the range for median base salaries for data scientists? | $95,000 to $165,000. |
| How is data science changing the world? | It helps in understanding genetics, improving logistics, predicting employee attrition, enhancing travel experiences, etc. |
| What should one do if they want to become a data scientist? | Start learning and getting involved in the field. |
| What's the concluding message of the notes? | The world of data needs you. |
| What should viewers do if they want more content? | Subscribe to "Simply Learn" for latest updates on such videos. |
| What is the viewer urged to comment about? | The next topic they want to learn. |
| How did MIC improve his financial situation? | He switched to an industry super fund. |
| How did the individual decide to switch funds? | By comparing the pair. |
| What was the individual's sentiment after making the change? | They never looked back. |
| Are both individuals in the comparison of the same age, income, and starting balance? | Yes. |
| How has data science impacted genomic data? | Provides a deeper understanding of genetic issues and drug reactions. |
| How do logistic companies benefit from data science? | They can find the best routes, delivery times, and transport modes leading to cost efficiency. |
| How can airline companies improve passenger experience with data science? | By predicting flight delays and notifying passengers in advance. |
| What are the implications of not doing proper EDA? | You might choose the wrong variables leading to inaccurate models. |
| What is the role of ETL tools in data science? | They are used to perform complex data transformations. |
| Why is data cleaning considered time-consuming? | It involves handling multiple complex scenarios like inconsistent data types, missing values, etc. |
| How does Emma handle communication with stakeholders? | She uses visualization tools like Tableau to present findings effectively. |
| In what setting is the selected model tested before full deployment? | In a pre-production environment. |
| How is data science impacting the medical field? | It helps in understanding genetic issues and how individuals react to certain drugs. |
| How do logistics companies like DHL and FedEx benefit from data science? | They can optimize routes, delivery times, and modes of transport. |
| How can companies benefit from data science in terms of employee management? | They can predict employee attrition and understand variables influencing turnover. |
| What impact does data science have on the travel industry? | Airlines can predict flight delays and improve the overall travel experience for passengers. |
| List potential roles in the data science domain. | Data Analyst, Machine Learning Engineer, Deep Learning Engineer, Data Engineer, and Data Scientist. |
| What is the potential salary range for a data scientist? | It can range from $95,000 to $165,000. |
| What is the importance of understanding the business problem in data science? | It sets the foundation for the entire data science process, ensuring the right problems are tackled. |
| How crucial is data acquisition in the data science process? | It is vital as it's the step where relevant data is gathered from various sources. |
| What challenges might one face during data cleaning? | Dealing with inconsistent data types, missing values, misspelled attributes, and duplicate values. |
| Why is EDA considered one of the most important steps? | It helps in understanding the data structure and determining what can be done with the data. |
| Why does Emma prefer Python for data modeling? | It's versatile and widely used in the data science community, but the notes don't specify her exact reasons. |
| How do visualization tools help Emma in her data science process? | They help in presenting and communicating findings to stakeholders in an understandable manner. |
| What does the deployment and maintenance phase involve? | Testing the selected model in a pre-production environment and ensuring its proper functioning. |
| How can data science benefit companies in terms of cost efficiency? | By optimizing processes, such as determining efficient logistics routes and modes of transport. |
| How does data science help improve customer experience in the airline industry? | By predicting potential flight delays and informing passengers beforehand. |
| What are some of the challenges of being a data scientist? | Understanding complex business problems, handling diverse data sources, and ensuring accurate model deployment. |
| What is the concluding sentiment of the provided notes? | The field of data science is vast, impactful, and needs more individuals to dive in. |
| What does the "compare the pair" section refer to? | Comparing two individuals' financial situations based on their decisions regarding super funds.
|
Comments
Post a Comment