Data Scientist Flashcards
What is a data scientist?
A data scientist is a professional who uses statistical, machine learning, and programming skills to analyze and interpret complex datasets and to develop and deploy predictive models and algorithms that can be used to solve business problems.
What skills do data scientists need?
Data scientists need statistical modeling and analysis skills. They also use programming languages like Python, R, and SQL to process and analyze data.
What are the functions of a data scientist?
The functions of a data scientist can vary depending on the industry and organization but may include data analysis, data visualization, data cleaning, A/B testing, machine learning, data storytelling, collaboration, experimentation, and continuous improvement.
What is A/B testing?
A/B testing is a method used to compare two versions of a product or service to determine which version performs better.
What is data analysis?
Analyzing large and complex data sets using statistical and machine learning techniques to identify patterns, trends, and insights that inform decision-making
Example: Analyzing sales data to identify factors influencing customer purchasing behavior
What is data visualization?
Developing visualizations such as charts, graphs, and dashboards to communicate insights and trends to nontechnical stakeholders
Example: Creating a dashboard to display key performance indicators for a marketing campaign
What is data cleaning?
Cleaning and preparing data for analysis, which may involve data cleaning, normalization, and feature engineering
Example: Removing duplicates and filling missing values in a dataset
What is A/B testing?
Designing and conducting A/B tests to evaluate the effectiveness of different strategies or interventions; A/B tests are statistical methods used to compare two versions of a product or service to determine which version performs better
Example: Testing two different website designs to see which one leads to higher conversion rates
What is machine learning?
Building machine learning models to automate decision-making processes, such as recommendation engines, fraud detection systems, or chatbots
Example: Training a model to predict customer preferences based on past behavior
What is data storytelling?
Developing narratives and presentations that explain complex technical concepts and insights to nontechnical stakeholders clearly and concisely
Example: Creating a data-driven story to illustrate the impact of marketing campaigns on revenue
What is collaboration in data science?
Collaborating with data engineers, software developers, and business stakeholders to understand data requirements and design solutions that align with business goals
Example: Working with a software developer to integrate a machine learning model into a customer relationship management system
What is experimentation in data science?
Designing and conducting experiments to test hypotheses and validate assumptions within the data
Example: Conducting A/B tests to determine the impact of a new pricing strategy on customer engagement
What is continuous improvement in data science?
Continuously monitoring and evaluating the effectiveness of their solutions and processes to improve decision-making outcomes over time
Example: Analyzing feedback from users to refine a recommendation algorithm for an e-commerce platform
