Maths World Economic Forum says that the digital universe has 40 times more bytes of data than the observable stars in the actual universe. Sounds immeasurable, doesn’t it? Data seems enigmatic with 5 billion searches every day and $1 million spent per minute on internet commodities. This amount of data necessitates a science for its ample use. Organizations need data scientists now more than ever. Their demand seems to escalate each year. A career in this field is the perfect opportunity to leverage the data-driven economy.  

What is Data Science

Data isn’t just a bunch of collected numbers, it can be as abstract as images, sound or text. Data science merges business aptitude with computer science, statistics and big data mathematics. It helps us amass, organize, analyse and extract insights from data. It’s an interdisciplinary science that solves complex data problems. Scientific principles and predictive algorithms are the key tools of a data scientist.  

Role of Mathematics in Data Science

Mathematics is a sore spot, even traumatic to some extent, for most students. However, it’s a crucial subject for data scientists. It’s applied to make sense of the data and to interpret it into a viable form. However, the amount of maths used in practice isn’t as intimidating as it might seem to you. It’s also exclusive to specific subtypes of maths. This raises the question, “what type of maths do scientists use to analyse data?” Be assured you won’t learn maths that haunted your school nightmares, nor will you be forced to crunch numbers. Instead of extensive prowess over the subject, in-depth knowledge of these four subsets will help you master data science:
  • Linear Algebra Ever wondered how Netflix suggests your next-favourite show? Or how Facebook precisely finds all those “People You Might Know”. It’s all thanks to Linear Algebra and its components. It’s an essential form of mathematics for data science. Hence, a vital skill for data scientists. Linear Algebra studies a combination of numbers in arrays and columns. It’s used to code, apply and manipulate algorithms and optimize predictive functions. It resolves complex models and analyses pertinent variables. You won’t need to write the code and create matrices by hand. Instead, sophisticated software is used for these functions. You need to gain an in-depth understanding of the subject. Focus on numerical computations and vector manipulations. Essential topics: Matrices, Matrix Algebra and Factorization, Vectors & Vector Space, Inverses, Determinants, Eigenvalues & Eigenvectors etc.
  • Statistics and Probability A remarkable command of statistics is a prerequisite in Data Science. You’ll be using its techniques day in and day out. While basic maths skills are sufficient in other topics, you need to be proficient at Statistics to excel in big data analytics. Statistics and Probability estimate the likelihood of an outcome. Probability inferences are used to predict trends and patterns from data. It further helps in formatting and testing hypotheses. It is used to examine a massive amount of data and to distribute it in a comprehensible format. Most importantly, the subject quantifies uncertainty. It assures data scientists with precise and error-free results. Essential topics: Descriptive Statistics, Sampling, Error, Variance & Covariance, Correlation, Hypothesis testing, A/B testing, p-values, T-test, Anova, Linear Regression, Data Summaries, Probability Distribution Function, Bayes Theorem, Conditional Probability etc.
  • Calculus Calculus studies the rapid rate of change of quantities. It involves derivatives and integrals. The two sub-parts of the subject assess the numbers differently. Integral Calculus collates and studies the changes in data, whereas Differential Calculus examines small parts of it. Calculus plays a significant role in the practice of Machine Learning. It enhances the accuracy of algorithms and is heavily used in optimization routines. Moreover, the subject will make it easier to grasp concepts of Linear Algebra and Statistics. Don’t be wary of Calculus, as you’ll rarely solve equations manually. Technological advancements make sure that most calculations are done on computers. However, extensive knowledge of its principles is an absolute must. Essential topics: Gradient Descent, Mean Value Theorems, Product & Chain Rules, Maxima & Minima, Functions of Single & Multivariable Calculus, Beta and Gamma Functions etc.

Other Types of Mathematics Used in Data Science

Beyond the four indispensable subsets, there are few other important mathematical concepts.  topics of maths for data science are:
  • Discrete Maths Discrete mathematics is based on non-continuous numbers. It’s widely used by analysts to form data structures. You need to know the basics of discrete maths for data science and machine learning. It’s the backbone of programming languages, algorithms, cryptography and software development. For instance, it is used to resolve the space and time complexity of an algorithm. Essential topics include Set, Subsets, Proof techniques, Countability, Data Structures, Graph functions, types of Logic, O(n) notation concepts etc.
  • Optimizations Optimization is another cardinal part of Machine Learning. It ensures minimal errors in estimates and prediction. It helps in finding the optimum solution by comparing available alternatives. Essential topics include the basics of optimization, Maxima & Minima, and Linear, Constraint & Integral Programming.
  • Graph Theory Graphs are used to arrange data in a structured and coherent manner. It’s an efficient tool to simplify and quantify abstract data. It eases the process of data visualization, a subset of data science.  It establishes a connection and studies relationships in data. For instance, graph theory is used in Google Maps to find the shortest path between two locations (nodes). Essential topics include types of graphs and their application, Cycles, Code, Centrality Measures, Page Rank, Shortest Path, Euler’s formula, Ramsey’s numbers, Koning’s Theorem.
  • Information Theory It’s a mathematical treatment used to study the communication of information. It also assesses coding, quantification and storage of data. It builds and compares the probability distribution. Most data science models employ information theory for optimization. Essential topics include encoding-decoding, Entropy, Viterbi algorithm, Mutual Information, Decision Trees, Loss Functions etc.

Tools Used by Data Scientists

There are specific tools and software that make data scientist’s work easier. Certain tools simplify intricate processes, while others fasten calculations and refine algorithms. With an inquisitive mindset, exceptional programming & maths skills you can ace these tools:
  • Predictive & Analytic Tools: SAS, SPSS, MATLAB, R and Python Programming etc.
  • Data Processing Tools: MS Excel, Apache Spark, Hadoop, SQL, Hive etc.
  • Framework Tools: Tensorflow, Keras, Caffe, PyTorch etc.
  • Data Visualization Tools: Tableau, D3, Qlikview, Power BI, Plotly, Dash etc.
  • AI Tools: Amazon Lex, Data Robot, Google Cloud Platform, Driverless AI etc.

Vital Soft-Skills for Data Scientists

Besides mathematics and other key subjects, you need to hone these non-technical skills for a promising career in data science:
  • Instil a sense of curiosity and creativity in you.
  • Build a strong business acumen relevant to your industry.
  • Refine your verbal and non-verbal communication skills.
  • Learn to work and collaborate effectively in teams.
If you want to become a data scientist, get rid of your fear and doubts about maths. It’s an integral part of data science, but it’s not the crux of it. Don’t be overwhelmed with the whole subject at once. Start with a single topic and build a solid foundation for each as you go. Invest your time mastering Statistics and Probability. Make sure to persistently practice the principles of Calculus and Linear Algebra. Don’t let your fears override your goal to become a data scientist!