--- title: Data Science description: From raw data to decisions: statistics, visualization, big data, forecasting, and causal inference. category: programming-tech url: /subcategory/programming-tech/data-science --- # Data Science From raw data to decisions: statistics, visualization, big data, forecasting, and causal inference. ## Available Subjects ### A/B Testing & Experimentation A/B testing measures the causal effect of product changes. You will learn experiment design, sample size and power, statistical pitfalls, metrics and how experimentation platforms work in practice. **Difficulty:** Beginner, Intermediate, Advanced **Resources:** 1 books, 1 courses, 1 websites, 4 papers [Learn A/B Testing & Experimentation](/subject/ab-testing-and-experimentation) ### Bayesian Statistics Bayesian statistics is a mathematical framework that applies probability to statistical problems, updating beliefs as new data is observed. You will understand how to construct prior distributions, compute posterior probabilities, and perform Bayesian inference. **Difficulty:** Beginner, Intermediate, Advanced **Resources:** 1 videos, 3 books, 2 courses, 4 websites [Learn Bayesian Statistics](/subject/bayesian-statistics) ### Big Data Big data refers to extremely large datasets that exceed the processing capacity of traditional databases. Learners will understand distributed storage, parallel processing frameworks like Hadoop and Spark, and architectures for analyzing massive volumes of data. **Difficulty:** Beginner, Intermediate, Advanced **Resources:** 3 courses, 1 websites [Learn Big Data](/subject/big-data) ### Big Data & Ethics This topic covers the ethical, legal, and privacy implications of collecting and analyzing massive datasets. Learners will understand data governance frameworks, algorithmic bias, and how to implement responsible data practices in technology. **Difficulty:** Beginner, Intermediate, Advanced **Resources:** 1 books, 1 websites [Learn Big Data & Ethics](/subject/big-data-and-ethics) ### Big Data Tools This topic explores the software frameworks designed to process, store, and analyze massive datasets. Learners will understand the architecture and use cases of tools like Hadoop, Spark, and NoSQL databases for handling high-volume data. **Difficulty:** Beginner, Intermediate, Advanced **Resources:** 2 books, 2 courses, 3 websites [Learn Big Data Tools](/subject/big-data-tools) ### Causal Inference & Experiments Causal inference is the process of determining the independent, actual effect of a particular phenomenon. You will understand how to design randomized experiments, control for confounding variables, and analyze observational data to establish cause-and-effect relationships. **Difficulty:** Beginner, Intermediate, Advanced **Resources:** 2 books, 1 courses, 5 websites [Learn Causal Inference & Experiments](/subject/causal-inference-and-experiments) ### Data Analysis This discipline involves cleaning, transforming, and modeling data to discover useful information and support decision-making. Learners will understand how to manipulate datasets, perform exploratory analysis, and extract actionable insights using structured analytical workflows. **Difficulty:** Beginner, Intermediate, Advanced **Resources:** 2 videos, 2 courses, 1 websites [Learn Data Analysis](/subject/data-analysis) ### Data Analysis (Pandas, NumPy) This topic covers data manipulation using Python's foundational libraries, Pandas and NumPy. You will understand how to manipulate multi-dimensional arrays, perform vectorized operations, clean tabular data, and execute complex aggregations efficiently. **Difficulty:** Beginner, Intermediate, Advanced **Resources:** 1 books, 1 websites [Learn Data Analysis (Pandas, NumPy)](/subject/data-analysis-pandas-numpy) ### Data Engineering Data engineering involves designing and building systems for collecting, storing, and analyzing data at scale. You will understand how to construct data pipelines, manage databases, and optimize data architectures for analytical and operational use. **Difficulty:** Beginner, Intermediate, Advanced **Resources:** 1 books, 4 courses, 4 websites [Learn Data Engineering](/subject/data-engineering) ### Data Ethics Data ethics examines the moral implications of collecting, sharing, and using data. You will understand issues surrounding privacy, algorithmic bias, surveillance, and data ownership, enabling you to design and implement responsible data practices. **Difficulty:** Beginner, Intermediate, Advanced **Resources:** 2 books, 4 courses, 3 websites [Learn Data Ethics](/subject/data-science-data-ethics) ### Data Science This multidisciplinary field combines statistics, scientific computing, and algorithms to extract knowledge and insights from structured and unstructured data. Learners will understand how to perform data cleaning, exploratory analysis, predictive modeling, and data visualization. **Difficulty:** Beginner, Intermediate, Advanced **Resources:** 3 books, 3 courses [Learn Data Science](/subject/data-science) ### Data Storytelling Data storytelling is the practice of translating data analyses into layperson terms to influence business decisions. You will understand how to combine data, visuals, and narrative structure to communicate insights effectively to diverse stakeholders. **Difficulty:** Beginner, Intermediate, Advanced **Resources:** 2 books, 2 courses [Learn Data Storytelling](/subject/data-storytelling) ### Data Visualization Data visualization focuses on communicating quantitative information through graphical representations. You will understand how to select appropriate chart types, apply design principles, and use visualization tools to make complex statistical findings accessible and actionable. **Difficulty:** Beginner, Intermediate, Advanced **Resources:** 1 videos, 4 books, 5 courses, 10 websites [Learn Data Visualization](/subject/data-visualization) ### Data Wrangling Data wrangling is the process of cleaning, structuring, and enriching raw data into a usable format. You will understand how to handle missing values, merge datasets, and transform unstructured data for efficient analysis and modeling. **Difficulty:** Beginner, Intermediate, Advanced **Resources:** 1 videos, 2 books, 3 courses, 2 websites [Learn Data Wrangling](/subject/data-wrangling) ### Descriptive Statistics This field involves summarizing and organizing features of a dataset through numerical calculations and graphs. Learners will understand how to calculate measures of central tendency, dispersion, and distribution shape to describe data accurately. **Difficulty:** Beginner, Intermediate, Advanced **Resources:** 1 videos, 3 books, 1 courses [Learn Descriptive Statistics](/subject/descriptive-statistics) ### Experimental Design This discipline covers the planning and structuring of scientific experiments to ensure valid statistical analysis. Learners will understand how to define variables, control confounding factors, and select appropriate sampling methods to establish causal relationships. **Difficulty:** Beginner, Intermediate, Advanced **Resources:** 4 books, 1 courses, 2 websites [Learn Experimental Design](/subject/experimental-design) ### Inferential Statistics Inferential statistics involves using data from a sample to make generalizations about a larger population. You will understand how to perform hypothesis testing, calculate confidence intervals, and estimate population parameters while accounting for sampling error. **Difficulty:** Beginner, Intermediate, Advanced **Resources:** 1 videos, 2 books, 1 courses, 4 websites [Learn Inferential Statistics](/subject/inferential-statistics) ### LLM Evaluation (Evals) LLM evaluation measures whether a language-model application actually works. You will learn error analysis, building test sets, code-based and model-graded checks, benchmarks and their limits, and how to monitor quality once a system is in production. **Difficulty:** Beginner, Intermediate, Advanced **Resources:** 1 books, 5 websites, 2 papers [Learn LLM Evaluation (Evals)](/subject/llm-evaluation) ### Machine Learning This discipline focuses on developing algorithms that allow computers to learn from and make predictions on data without explicit programming. Learners will understand how to apply supervised, unsupervised, and evaluation techniques to solve predictive problems. **Difficulty:** Beginner, Intermediate, Advanced **Resources:** 1 books, 6 courses, 6 websites [Learn Machine Learning](/subject/machine-learning) ### Power BI & Tableau Power BI and Tableau turn data into interactive dashboards. You will learn data modelling, DAX or calculated fields, visual design and how to publish reports that decision-makers actually use. **Difficulty:** Beginner, Intermediate, Advanced **Resources:** 2 books, 2 courses, 4 websites [Learn Power BI & Tableau](/subject/power-bi-and-tableau) ### Predictive Analytics Predictive analytics uses historical data, statistical algorithms, and machine learning to identify the likelihood of future outcomes. You will understand how to build forecasting models, analyze trends, and assess risks to guide strategic planning. **Difficulty:** Beginner, Intermediate, Advanced **Resources:** 3 books, 2 courses [Learn Predictive Analytics](/subject/predictive-analytics) ### Probability Probability is the mathematical study of uncertainty and randomness. Learners will understand random variables, probability distributions, Bayes' theorem, and how to calculate the likelihood of events to inform decision-making and statistical models. **Difficulty:** Beginner, Intermediate, Advanced **Resources:** 2 videos, 3 books, 3 courses, 2 websites [Learn Probability](/subject/probability) ### Probability Distributions This area of mathematics describes the likelihood of different outcomes in a random event. Learners will understand discrete and continuous distributions, including normal, binomial, and Poisson distributions, and how to apply them to model real-world scenarios. **Difficulty:** Beginner, Intermediate, Advanced **Resources:** 2 books, 3 websites [Learn Probability Distributions](/subject/probability-distributions) ### Product Analytics Product analytics measures how people use software. You will learn event tracking, funnels, retention and cohort analysis, north-star metrics and how to turn usage data into product decisions. **Difficulty:** Beginner, Intermediate, Advanced **Resources:** 4 websites [Learn Product Analytics](/subject/product-analytics) ### Python for Data Science Python for data science focuses on using the Python programming language to extract insights from data. You will understand how to write clean code, use core libraries, and implement workflows for data manipulation, visualization, and modeling. **Difficulty:** Beginner, Intermediate, Advanced **Resources:** 2 books, 3 courses, 2 websites [Learn Python for Data Science](/subject/python-for-data-science) ### Recommender Systems Recommender systems rank items for users at Netflix, Amazon and Spotify scale. You will learn collaborative filtering, matrix factorisation, deep and sequential models, evaluation and the feedback-loop pitfalls. **Difficulty:** Beginner, Intermediate, Advanced **Resources:** 2 books, 1 courses, 1 websites, 5 papers [Learn Recommender Systems](/subject/recommender-systems) ### Regression & Modeling Regression analysis is a statistical method for estimating relationships between variables. Learners will understand how to build, evaluate, and interpret linear and logistic regression models to predict outcomes and analyze data trends. **Difficulty:** Beginner, Intermediate, Advanced **Resources:** 1 videos, 2 books, 2 courses, 3 websites [Learn Regression & Modeling](/subject/regression-and-modeling) ### Regression Analysis This statistical method estimates the relationships between dependent and independent variables. Learners will understand how to build, interpret, and evaluate linear and logistic regression models to make predictions and identify trends in data. **Difficulty:** Beginner, Intermediate, Advanced **Resources:** 2 books, 2 courses, 2 websites [Learn Regression Analysis](/subject/regression-analysis) ### Spark, Airflow & Data Pipelines Modern data platforms move and transform data at scale. You will learn Spark for distributed processing, Airflow for orchestration, dbt for transformations and the design of reliable batch and streaming pipelines. **Difficulty:** Beginner, Intermediate, Advanced **Resources:** 1 books, 1 courses, 2 websites [Learn Spark, Airflow & Data Pipelines](/subject/apache-spark-and-data-pipelines) ### Sports Analytics Sports analytics uses data to evaluate players and tactics. You will learn the key metrics across sports, statistical modelling, tracking data and how teams use analytics for decisions. **Difficulty:** Beginner, Intermediate, Advanced **Resources:** 3 books, 1 courses, 2 websites [Learn Sports Analytics](/subject/sports-analytics) ### Statistical Modeling Statistical modeling is the mathematical representation of relationships between variables within a dataset. You will understand how to formulate hypotheses, fit regression models, and interpret statistical outputs to make valid inferences from data. **Difficulty:** Beginner, Intermediate, Advanced **Resources:** 1 books, 2 courses, 1 websites [Learn Statistical Modeling](/subject/statistical-modeling) ### Statistics This subject introduces the fundamental concepts of collecting, analyzing, presenting, and interpreting data. Learners will understand basic probability, descriptive measures, and the foundational principles of statistical reasoning used to make data-driven decisions. **Difficulty:** Beginner, Intermediate, Advanced **Resources:** 1 videos, 7 books, 3 courses, 1 websites [Learn Statistics](/subject/introduction-to-statistics) ### Statistics & Probability Statistics & probability provide the mathematical foundation for analyzing data and uncertainty. You will understand probability distributions, hypothesis testing, confidence intervals, and descriptive statistics to make mathematically sound decisions based on empirical evidence. **Difficulty:** Beginner, Intermediate, Advanced **Resources:** 2 books [Learn Statistics & Probability](/subject/statistics-probability) ### TensorFlow & PyTorch TensorFlow and PyTorch are the leading open-source frameworks for deep learning. You will understand how to construct neural network architectures, manage computational graphs, train deep models, and deploy machine learning solutions for complex tasks. **Difficulty:** Beginner, Intermediate, Advanced **Resources:** 2 books, 3 websites, 1 papers [Learn TensorFlow & PyTorch](/subject/tensorflow-pytorch) ### Time Series & Forecasting Time series analysis involves analyzing data points collected sequentially over time. Learners will understand how to identify trends, seasonal patterns, and noise, and apply forecasting models to predict future values in temporal datasets. **Difficulty:** Beginner, Intermediate, Advanced **Resources:** 1 books, 1 courses, 3 websites [Learn Time Series & Forecasting](/subject/time-series-and-forecasting) --- *This content is part of Dantes.io - Your Treasure Map to Knowledge* View this page online: https://dantes.io/subcategory/programming-tech/data-science