Dataset
A collection of data used for analysis or experimentation.
Feature
An individual measurable property or characteristic of a dataset.
Variable
A feature or attribute that can change or take different values.
Observation/Instance
A single row or data point in a dataset.
Label/Target
The variable being predicted or analyzed in a machine learning problem (often the output).
Descriptive Statistics
Techniques used to describe and summarize features of a dataset, like mean, median, variance, and standard deviation.
Inferential Statistics
Methods that infer insights or make predictions about a larger population based on sample data.
Hypothesis Testing
A statistical method to test assumptions or hypotheses about a population parameter.
Correlation
The measure of the strength and direction of the relationship between two variables.
Supervised Learning
Machine learning approach where models learn from labeled data to make predictions or classifications.
Unsupervised Learning
Machine learning approach where models find patterns and structures in unlabeled data.
Feature Engineering
The process of creating new features or transforming existing ones to improve model performance.
Overfitting and Underfitting
Overfitting occurs when a model performs well on training data but poorly on new data; underfitting occurs when a model is too simple to capture the underlying patterns.
Cross-validation
Technique to assess the generalization performance of a model by splitting data into subsets for training and validation.
Exploratory Data Analysis (EDA)
Initial analysis to understand the dataset's main characteristics through visualizations and summary statistics.
Data Visualization
Presenting data graphically to communicate patterns, trends, and insights effectively.
Histogram
A graphical representation of the distribution of numerical data.
Python/R
Programming languages commonly used for data manipulation, analysis, and machine learning.
Pandas
Python library for data manipulation and analysis.
Scikit-learn
Python library providing machine learning algorithms and tools.
Jupyter Notebooks
Interactive environments for creating and sharing documents containing live code, visualizations, and narrative text.
Business Acumen
Ability to translate data insights into actionable business strategies and decisions, working closely with stakeholders to solve business problems.
Problem-solving
Strong analytical and problem-solving skills to tackle complex issues using data-driven approaches.
Communication Skills
Effective communication is vital to convey complex findings and insights to both technical and non-technical stakeholders.
Teamwork and Collaboration
Capability to work in multidisciplinary teams, collaborate with other professionals, and share knowledge effectively.
Curiosity and Continuous Learning
Given the evolving nature of technology and data science, a passion for learning and staying updated with new techniques and tools is essential.
Education
A bachelor's or master's degree in fields like computer science, statistics, mathematics, data science, or a related field. Some roles may require a Ph.D. for research-oriented positions.
Experience
Depending on the role, companies may seek candidates with a few years of relevant work experience in data analysis, machine learning, or a related field.
Python
Widely used for data analysis, machine learning, and statistical modeling. Libraries like Pandas, NumPy, SciPy, Matplotlib, and Scikit-learn are commonly used in Python.
R
Another popular language for statistical analysis, data manipulation, and visualization, with a wide range of packages like dplyr, ggplot2, and caret.
SQL (Structured Query Language)
Essential for managing and querying relational databases.
Pandas
Python library for data manipulation and analysis, offering data structures and tools for cleaning and preprocessing.
dplyr
R package for data manipulation tasks like filtering, summarizing, and transforming data.
Apache Hadoop
Framework for distributed storage and processing of large datasets.
Apache Spark
Provides a fast and general-purpose cluster computing system for big data processing.
Scikit-learn
Python library offering various machine learning algorithms and tools for modeling and evaluation.
TensorFlow and Keras
Libraries for building and training neural networks and deep learning models.
PyTorch
Another deep learning framework used for building neural network architectures.
Jupyter Notebooks
Interactive environments for creating and sharing documents containing live code, visualizations, and narrative text.
Matplotlib
Python library for creating static, interactive, and 3D visualizations.
Seaborn
Built on top of Matplotlib, Seaborn provides more visually appealing statistical graphics.
ggplot2
R package for creating elegant and complex data visualizations.
Tableau
User-friendly platform for data visualization and analytics.
Power BI
Microsoft's business analytics tool for visualizing and sharing insights from data.
QlikView/Qlik Sense
Platforms for data visualization, business intelligence, and data discovery.
OpenRefine
Tool for cleaning and transforming messy data.
Trifacta
Platform for data wrangling and preparation tasks.
Amazon Web Services (AWS), Microsoft Azure, Google Cloud Platform (GCP)
Cloud services offering various tools and resources for data storage, processing, and analysis.
Descriptive Questions
What are the key characteristics or trends in the dataset? How is the data distributed across different categories or groups? What are the summary statistics for the variables in the dataset?
Diagnostic Questions
What factors are contributing to a particular outcome or phenomenon? Are there any anomalies, outliers, or patterns that need further investigation? What is the root cause of a specific problem in the dataset?
Predictive Questions
Can we predict future outcomes based on historical data? What variables are most predictive of a certain event or outcome? How accurate are our predictions using different models or algorithms?
Prescriptive Questions
What actions or interventions can be recommended based on predictive models? How can we optimize a process or system to achieve better outcomes? What changes can be made to improve a specific metric or result?
Exploratory Questions
Are there any hidden patterns or relationships in the data? Can we identify clusters or groups within the dataset? What variables are most correlated with each other?
Causal Questions
What is the cause-and-effect relationship between variables? Can we establish causation based on observational or experimental data? How does changing one variable affect another in the dataset?
Comparative Questions
How do different groups or categories in the dataset compare to each other? What are the differences or similarities between subsets of the data? Are there significant differences in outcomes between different treatments or conditions?
Primary Sources
Data collected firsthand for a specific purpose. It includes surveys, experiments, observations, interviews, and focus groups.
Secondary Sources
Data that already exists and is collected by someone else for their own purposes. This includes books, articles, official records, databases, and previously conducted research.
Tertiary Sources
Compilations or summaries of primary and
Predictive Analysis
Using historical data to forecast or predict future outcomes or trends.
Diagnostic Analysis
Identifying reasons behind certain outcomes or patterns by investigating cause-and-effect relationships in data.
Prescriptive Analysis
Recommending actions or strategies based on analysis to optimize or improve future outcomes.
Variables
Containers for storing data values in programming languages.
Functions
Reusable blocks of code that perform specific tasks.
Control Structures
Statements that determine the flow of execution in a program.
Data Types
Categories of data that determine the kind of values that can be stored and manipulated.
Modules
Files containing Python code that can be imported and used in other programs.
Libraries
Collections of modules that provide additional functionality for specific tasks.
Lists
Ordered collections of items in programming languages.
Tuples
Immutable ordered collections of items in programming languages.
File Handling
Manipulating files in a program, such as reading from or writing to files.
Dictionaries
Key-value pairs used to store and retrieve data in programming languages.
Plotting
Creating visual representations of data using graphs or charts.
Data Manipulation
Modifying or transforming data to make it suitable for analysis.
Visualization
Presenting data in a visual format to gain insights or communicate information effectively.
Data Cleaning
Preprocessing data by handling missing values, duplicates, outliers, and normalizing or standardizing data.
Model Accuracy
Techniques to improve the accuracy of a predictive model, such as cross-validation, ensemble methods, and increasing the quantity and quality of data.
Data Formats
Different ways in which data can be structured and represented, such as CSV, Excel, or JSON.
Data Science Applications
Examples of how data science can be used in different domains, such as student performance analysis in education or audience insights in the movie industry.
Python Skills
Proficiency in the Python programming language for data analysis and modeling.
Online Platforms
Tools like Jupyter Notebook or Google Colab for creating and running data science models.
Importing Datasets
Steps to import datasets into a data science environment using tools like Google Colab.
Creating Datasets
Generating or creating custom datasets for analysis using existing data sources.
Data Visualization
Techniques for visualizing data, such as box plots, histograms, and pie charts.
Necessary Libraries
Key libraries in Python for data analysis and modeling, such as pandas, scikit-learn, geopandas, and matplotlib.
Exporting Work
Methods to save or export the results or outputs of data analysis or modeling tasks.