Statistics and Data Science: What Do You Really Study?
From raw data to insights and predictions - with the help of probability, programming, models and critical thinking.
What Do You Study in Statistics and Data Science? Core Subjects, Year by Year
What do the degree courses cover?
Open a course to learn about its main topics and see what the work looks like in practice.
What do you learn in the course?Hadova for Statistics and Data Sciences?
The course teaches to describe change, accumulation and optimization with tools that are used by probabilistic models and algorithms. You learn limits, derivatives, integrals and functions in several variables and relate them to densities, spans and model training.
Main topics
- Limits, continuity and derivatives
- Integrals and density functions
- Functions in several variables and gradient
- Optimization and approximations
What does it look like in practice?
For example, we derive a loss function in several parameters and use the gradient to understand in which direction an update will reduce the model error.
What do you learn in the course?Linear Algebra for Data Science?
Learn to represent observations and models using vectors and matrices. The course deals with systems of equations, spaces, projections, eigenvalues and decompositions that appear in regression, compression, recommendations, image processing and machine learning.
Main topics
- Vectors, matrices and systems of equations
- Spaces, bases and throws
- Eigenvalues and slope
- SVD, least squares and dimension reduction
What does it look like in practice?
For example, PCA is used to compress dozens of variables into central components and explain how much of the variance has been preserved and what each component means.
What do you learn in the course?Introduction to Probability?
The course builds a language for quantifying uncertainties before drawing conclusions from data. Learn about events, conditional probability, random variables, distributions, range and variance, and limit theorems that explain why samples behave in expected ways.
Main topics
- Events, combinatorics and conditional probability
- Random variables and distributions
- Span, variance and covariance
- The law of large numbers and the central limit theorem
What does it look like in practice?
For example, we calculate the probability of customer abandonment after a given usage pattern and update it when new information is received using a Bayes rule.
What do you learn in the course?Programming for data science?
Learn to write clear and reproducible code for loading, cleaning, processing and analyzing data. You practice variables, functions, data structures, work with tables and files, tests and visualization and develop a process that can be re-run on new data.
Main topics
- Language basics, functions and data structures
- Tables, files and transformations
- Cleaning, missing values and sanity checking
- Illustrating, testing and recreating a work environment
What does it look like in practice?
For example, build a pipeline that consolidates sales files, validates types, handles duplicates and missing values, and generates a weekly report automatically.
What do you learn in the course?statistical inference?
Learn to draw conclusions about a population from a sample and express the uncertainty explicitly. The course deals with estimators and their properties, estimation methods, confidence intervals, hypothesis testing and power and emphasizes the difference between significance, effect size and practical significance.
Main topics
- Estimates, bias, variance and traceability
- Maximum visibility and valuation methods
- Confidence intervals and hypothesis testing
- Power, multiplicity of tests and effect size
What does it look like in practice?
For example, an A/B experiment is tested and reported not only if the difference is significant but also its size, the range of uncertainty and whether it justifies a product change.
Frequently Asked Questions About Statistics and Data Science
Is a degree in Statistics and Data Science difficult?
There is no single answer — difficulty depends on your background, the institution, and your chosen study pace. According to this site's estimate, The degree is highly quantitative — mathematics and formal reasoning appear in nearly every course, and this is the part most students describe as challenging, and a large share of the reading material is in English. A good way to assess fit is to review the course map on this page: if most of the subjects interest you, the effort will usually feel worthwhile.
How long does a degree in Statistics and Data Science take?
Usually 3–4 years (Single-major or combined with an applied field). Exact duration varies by institution and depends on course load and the track you choose.
Statistics and Data Science or Computer Science — which is right for me?
The degrees most closely related to Statistics and Data Science are Computer Science, Industrial Engineering and Management, and Mathematics. Their differences are usually in emphasis rather than the overall subject, so the fastest way to decide is to compare their course maps — what you actually study in each program, year by year. You can also open the degree comparison tool to view the differences side by side.
What is the difference between statistics and data science?
There is a large overlap: statistics emphasizes theory, inference and experimental methodology, while data science adds more programming, machine learning and big data work. In Israel many programs combine both, so compare curricula course by course.
How much math is there in a statistics degree?
A lot — it is a mathematical degree at heart: calculus, linear algebra, probability and statistical inference form the base of the first two years. A strong math background is required, usually 4–5 matriculation units; gaps can be closed in a preparatory program.
What do graduates work in?
Analysts and data scientists in high-tech and finance, biostatistics in pharma and healthcare, market research and surveys, and the public sector — such as the Central Bureau of Statistics and the Bank of Israel. Demand for graduates with strong data skills keeps growing.
Do you need to know how to code beforehand?
No prior background is required: programming (usually Python or R) is taught during the degree. Prior experience helps, but the courses are built for beginners too.
Where can you study this?
The field is taught at the Hebrew University, Tel Aviv, Haifa and Ben-Gurion universities, in statistics or data science tracks, often combined with computer science, economics or management. Track names and admission requirements vary between institutions.