Nearly 200 colleges offer some variations of a data science major or a minor. However, there can be differences between the various programs that can have real impact on a student’s career opportunities. While the various tracks in data science need some specialized knowledge and skill sets, certain basic level of knowledge across the various subjects is expected of a data scientist. In this post, we’ll help you with our analysis of what an ideal data science program should offer you as well as discuss the minimum requirements and the good to have skills.
Data science is an interdisciplinary subject that combines aspects of mathematics, statistics and computer science. A data science major can be offered through the following departments or as an interdisciplinary major:
- Computer Science
- Data Science Department (if the college has established a separate department).
Colleges can offer a Bachelor of Arts (BA), Bachelor of Science (BS), a minor, a certificate, a Track in Data Science. We are going to discuss what an ideal undergraduate data science curriculum should look like. While most colleges have a set of core curriculum, students can also take many electives. Use our guide to design a strong curriculum that will help you answer the tough data scientist interview questions and succeed in your career.
Ideal Data Science Curriculum that every student interested in data science should take:
- Differential and Integral Calculus - both single variable and multivariable calculus
- (Optionally) Good to have partial differential equations as well.
- Linear Algebra
- Probability Theory and Applications
- Statistical Inference
- Statistical Methods for Data Science
- Programming for Data Science
- Data structures and Algorithms for Data Science
- Data Mining and Machine Learning
- Data Management, working with large scale data and efficiency
Calculus and Linear Algebra courses are mandatory for any student pursuing a career in data science. The calculus topics are divided into Calculus I, Calculus II and Calculus III, of which Calculus I and II are essential for a good mathematical foundation in data science problems. Calculus III is a part of many data science curricula, but not all, as it provides a wider and deeper study of calculus which could be valuable in solving real world data science problems. If you are designing your own curriculum of study, it would be a good idea to ensure that you cover the following topics.
Calculus There are some variations in the exact topics covered amongst different colleges, but if a student takes all three levels of calculus, they will be on the same educational level in math. Calculus I focuses on single variable differential calculus with an introduction to integration. Some of the topics that should be covered in Calculus I are rules of differentiation, limits and derivatives, applications of differential calculus, exponential and trigonometric functions, mean value theorem, integration, Riemann integrals. Calculus 2 is single variable integral calculus course. The topics covered in Calculus II are methods of integration, fundamental theory of calculus, complex numbers, series and sequences, differential equations, polynomials and power series, applications of integrals, parametric equations and polar coordinates. Calculus III focuses on multivariable differential and integral calculus as well as partial differential equations. The topics covered in Calculus III are conic sections, vector functions, line and surface integrals, multiple integrals and applications of partial differential equations.
Linear Algebra: Almost all data science curriculum (should) contain a semester of linear algebra. If you are designing your path through a data science equivalent major, then please ensure that you take a semester of linear algebra. The topics covered are matrices, matrix operations, determinants, linear equations and transformations, vector spaces, eigenvalues and eigenvectors, inner product, equilibrium, orthogonality and linear equations.
Is knowledge and mastery of these math topics important for data science? Where are they used in data science?This is a very nuanced question, as the answer depends on the students end goal. Data science can be approached in various ways. A data scientist can be any of the following: a practitioner of data science, (closer to data analysis), a scientist who builds models from scratch, a data scientist who improves current models and algorithms, an academic researcher. Many companies will advertise for data scientist roles but are looking for a practitioner of data science rather than a “complete” data scientist. Other companies may be looking for full on data scientist roles, in which case they are usually looking for more experienced data scientists.
If you are looking to do more of data analysis or business analysis, then it is required to have a good foundation in algebra, trigonometry, functions, data analytic and the ability to graph the data or data visualization. It is still a good idea to take a semester of calculus just so that you are more comfortable is understanding the math behind the analysis.
If you are looking to build models, refine, optimize models or do research (e.g. neural networks), artificial intelligence (AI) algorithms, analyze distributed data and mixed data, then it is critical to have a strong math background. An example of where calculus is used: A curve fitting optimization called the cost function uses a concept called gradient descent which needs knowledge of calculus. Linear algebra is used in more computer science heavy contexts like natural language processing, computer vision, image processing. If you are looking to work in any of these fields which can overlap with data science, then linear algebra is a must.
The topics in statistics can be spread across different courses, so we are going to give you a list of topics that any student in data science should take in college. The topics should cover an introduction to statistics and probability, regression analysis, modelling, introduction to design of experiments and surveys, statistical inference, use of statistical programming languages such as R, data visualization and introduction to data mining. Some students may wish to take a course on Bayesian analysis as well and/or advanced data mining techniques such as building decision trees, cluster analysis, etc.
Why is knowledge and mastery of these topics in statistics important for data science? Where is it used?
Basic statistics is absolutely necessary for any data science or data analytics job. Statistics can be descriptive or inferential. Descriptive statistics describes, summarizes and visualizes the data contained in a particular data set. Some of the important tools for descriptive statistics are mean, mean, mode, variance, standard deviation, skew, histograms, graphs, charts. Analysts use packages and programming toolkits such as Excel, SQL, R to extract relevant and actionable information from the data.
However, beyond analyzing the data and understanding patterns, statistics can also be used to design experiments, ask questions and try to infer patterns and dependencies of a population larger than the sample set. While inferential analysis also uses many of the same tools as descriptive analysis, many more tools such as regression analysis, time series analysis, clustering and predictive modelling are also used quite frequently.
Some of the projects where statistics is used quite heavily are in the analysis of user behavior, user retention, conversion analytics, financial math, healthcare, ad analytics and more. Most jobs in data analytics, business analytics and data science need a solid understand and practical working knowledge of various statistical packages. It is less used by data engineers and machine learning specialists, but even they should have a good working knowledge of statistics. We would venture to say that statistics is one of the cornerstones of data science.
Data science programs should have a strong component of computer science. While the courses may vary across the colleges, the following topics should be a part of your data science curriculum. Introduction to computing, discrete structures, data structures, programming languages like Python and Java, algorithms, machine learning for data science should a part of the curriculum. Introduction to computing helps students get comfortable with programming and is often the first course students take when pursuing a computer science (related) degree. Students learn the basics of programming usually through Python and/or Java, the “art” of debugging, learn about algorithms and how to write algorithms. It is a course designed for students with no prior exposure to programming and can also be a good refresher course for a student who has programmed before. If a student is a proficient programmer, then they may get credit for the course and move onto the higher level courses such as learning about data structures.
Discrete structures teaches students about the mathematics underlying many computational topics such as search algorithms, sorting algorithms, cryptography, security protocols, social networks etc. The topics covered are set theory, functions, number theory, probability, logic and graph theory. This is a critical course as it teaches students to understand look underneath the hood and gain hard math skills that powers many of the computational systems today.
Data structures teaches students about the fundamentals of manipulating and structuring data in different languages for various computational scenarios. Some of the topics that are covered are binary trees, stacks, linked lists and arrays, building hash tables, priority queues and their implementation in languages such as Java and C++.
Data Management and Visualization: Data science begins with cleaning the relevant data and then analyzing the cleaned data to find patterns within the data. Often times real world data contains missing data, noisy signals and duplicated data. Data cleaning is one of the most time consuming parts of a data science job. After cleaning the data, a data scientist/analyst using various tools and techniques to analyze the data and report their findings. Using a visual format to report the findings makes for a better presentation and can make story behind the numbers come alive. Learning the techniques for cleaning the data and working with data visualization tools and packages is important for both data scientists and data analysts. While many of these techniques can be learned on the job, it might be a good idea to learn these techniques while in college. It is important to comfortable with many of the data visualization and predictive modelling packages such as Scikit-learn, TensorFlow, PyTorch etc.
While traditionally not defined as a part of data science, most data scientists find that they need to learn a lot about data pipelines, database management and data engineering. Taking a class or two in any one of these disciplines would be helpful for a student looking to work in data science or data analytics.
Machine Learning for Data Science: Usually these are more advanced courses in computer science and are not strictly necessary for a data science career. However, with many companies focusing on artificial intelligence and machine learning, it might be worth your while to look at this concentration. Some of the use cases for AL and ML are in natural language processing, pattern recognition, recommendation systems, etc. While machine learning is a very buzzy space, in reality most companies do not use machine learning to solve their data science problems. This is a nice to have, enjoyable to learn kind of a course rather than a need to have course.
Why is knowledge and mastery of these topics in computer science important for data science? Where is it used?
Knowledge of some of the above topics in computer science is essential in a data engineer, data science or even a data analytics career. While most of the jobs will not involve model building right from the get go or an entry level position, they will include knowledge of data management, data analysis, visualization and in some cases data engineering. As you progress in your data science career, you may be required to work on modelling, optimization, inferences, pattern matching, trying to answer business questions, all of which will require knowledge of computer science, regardless of the domain. The knowledge of computer science is quite essential for a career in data science, though the depth and breadth of computer science knowledge will vary depending on whether you are looking at a data engineer or machine learning engineer position or a data analyst or a data scientist position.
Is it enough to have a strong grounding in math, statistics and computer science to be successful in data science?
The answer is both a yes and no. It is important to have a strong grounding in math, statistics and computer science. It is also essential to have some domain knowledge where your skills in math/statistics/computer science can be used to solve problems.
Will a bachelors in data science help me get the entry level job in data science?
The entry level job market for data science can be quite fierce. While a bachelors in data science, computer science, statistics and math helps a student be a competitive candidate and equips them with the technical skills, often a bit more is needed to get the foot in the door and get an offer. Read more about the “extra bit” that is needed to get the job offer in our next post on data science.