How To Study Mathematical Concepts For Data Science | A Career Transition Guide By Learnbay
Key Mathematical Concept which Will Help You Become A Data Scientist
By Trisha Manna In Learnbay
May 17, 2021
Data science is the area where core data must be played and the connection between multiple data sets assessed for better business decisions. The lucrative salary packages and ample opportunity for secure career growth have made the ‘data science career transition’ a buzzword for the 21st century. While core data science subdomains, like machine learning experts, advanced AI specialists, etc., offer more promising careers, still basic positions like data analyst hold incredible popularity. Why So? The reason is the fear of skill-shortage of complex mathematical concepts that the above data science job role demands. In This blog, I’ll discuss the process of learning the mathematical concepts for Data Science in an effective way.
But the question is, do data scientists use math?
Although mathematics is the most important skill required to build a platform towards your future in data science, a data scientist does not just have to master it.
However, It’s better to say that data science is more dependent on statistics rather than simple mathematics.
In Which way, math functions become the helping hand for a data scientist?
Data science is the study of numerous data sets for analytical purposes and to evaluate promising insight. Such insights are used to make data-driven and future proof business decisions.
And the core concepts of data analytics demands algorithmic proficiency, and here come the needs of key math formulas. Then, based on these formulas, data scientists identify the machine learning algorithms and solutions models to specific problems.
So, having a degree in math seems highly advantageous for a data science aspirant.
So if asked, can you become a data scientist without core concepts of math?
Then the answer is, ‘certainly no.’ The data analyst position can indeed be managed with the help of data science tools. But, for a sustainable career and more accurate analytical result, a fair grip over data analytics math skills become a key criterion.
So, let’s have a look at the maths required for data science.
1. Linear Algebra
While it comes to the mathematical module of a data science concept, the first thing that comes to mind is ‘Linear Algebra.’ In the last few years, appreciable linear algebra applications have shown vibrant advancement in the following issue.
● Analysis of textual entities
● Recognition of image and emotions.
Can you remember your high school math syllabus or the term ‘Matrix’? The most important linear algebra concept you need to brush up on from your high school maths is the matrix types.
I. Vector
This is the simplest type of matrix that owns a one-dimensional structure and looks like a column. Such vectors are most widely used for text analysis.
II. Scalars
It falls under the two-dimensional category of the matrix, where the order has to exceed the value of 2. The diagonal elements of such a matrix remain identical.
Other than the scalar matrix for complex problems, like image recognition, several different 2-D matrices have to be used. Such a matrix containing the same number of rows and columns. E.g., if the number of rows is y, then the number of columns will also be y.
You can program both the vectors and scalar problems in python with the help of the NumPy library.
For starting your self-paced learning, you can opt for Khan Academy Algebra Course and Introduction to Algebra by EdX.
2. Probability and Statistics: Descriptive and Inferential Statistics
Until now, what I was talking about was the foundation level math concepts required for the data machine learning approach. Now the ultimate learning point comes.
The module of probability and statistics for data science and AI is segregated into two parts.
I. Inferential
No matter how efficiently you have analysed your data. All credit goes to the concluding part. When the population does expand, and multiple complex tests have to be run, then inferential statistics becomes the savours.
The most important things you need to master under the inferential statistical module are as follows.
● Random testing of hypothesis: Starting a data science project with a hypothesis setting is the best way to ensure highly precise analytical output. Here you need to focus on learning two types of hypothesis testing.
● ANOVA assessment: To enhance the accuracy of your hypothesis, you need to carry out t-testing or an F-ratio assessment. Such testing techniques come under the ANOVA. Thus, whether you choose a data analysis or a machine learning career, you need to master ANOVA.
● Correlation building and regression analysis: This is the most crucial part of a data scientist job role. These two processes fall under the quantitative data analysis techniques. Your data analytics can’t offer the possible insight until you identify the correlations between the variables and do a regression analysis on them. Even for developing machine learning models or algorithms, you need a crisp regression analysis.
Know about the Regression Techniques in Machine Learning.
II. Descriptive
While it comes to summarising the quantitative data, then Descriptive statistics become the best options for most scenarios.
Under Descriptive Statistics, you need to focus more on the following two aspects.
● Variability: Calculation of Standard Deviations, Identification of mean value, etc., needs to measure distances between different data points. For such measurement attempts, you need to use variability.
● The tendency of centralisation: For insight generation from a graph, what we need is the values like
■ Mean
■ Median
■ Mode
Using these arithmetic values, we can dig out the central zone of a data set.
3. Calculus: The Heart of Artificial Intelligence
Again, this roams around your high-school knowledge, but at this time, you need to head to the more advanced concepts of following two types of calculus for data science.
● Differential calculus: Data optimisation is remarkably dependent on differential calculus. The most widely used part of this calculus is Chain Rule, which is a part of differential calculus.
Now can you guess why the chain rule? It’s Because of artificial neural network development.
● Integral Calculus: When it comes to the need for the coordinate geometrical application, especially for calculating the areas under different curves, you need to apply your integral calculus knowledge.
The two most popular machine learning algorithms, Naive Bayes and Random Forest, are highly dependent on integral calculus.
A Bit More
The Theory of Graph
Some business issues, such as shipping route optimisation from cost-effectiveness or providing tight security to the bank customer from credit-card fraud, need a graphical analysis approach. Hence you need to be well aware of graphical theories.
The Theory of Information
The effective application of information theory will be required for the development of machine learning theories for a personalized model of recommendation for a video app. In such instances, you must combine your information theory expertise with regression tree algorithms. You should also appreciate the importance of a fair understanding of this principle.
Where to start your learning journey for data science mathematics and statistics?
To start a flawless journey of learning statistics and applied mathematics for data science, you can join our Masters’ programme on AI and ML. In addition, we offer IBM a certified learning program on data science and AI for working professionals along with an assured industrial, real-time project experience certificate.
You can book a live demo session of Learnbay before you enrol for any course.
