Avoid Mathematics: Learn Data Science and Machine Learning

By ridhigrg |Email | Jan 25, 2019 | 8955 Views

Today, the most desirable career option seems to be a data scientist and machine learning, as every individual either it is a college-going student or a professional is looking to switch their career onto data science. The most leading thing which comes in mind while going into this field is that you should have the depth knowledge of mathematics for making a great way for this career. But there are people who are weak in mathematics what should they do? Should they stop thinking of going to this field of machine learning or data science? This article will give you the way, and tell you whether you have the chance to go in this field without mathematics or not. 
There are several reasons why mathematics is a prerequisite. The one who has the capability of grabbing things more fastly and early can easily enter into the field of data science. With rigorous math, the fast techniques could give you an appropriate learning curve. Other is that the data scientists are essentially statisticians, and most of them have the knowledge of math and statistics. Most of the employers use the standard tools like the logistic regression, decision tree or the confidence. Due to which, hiring managers are looking for candidates with a strong math background. Forgetting the data science job which can match your standard, this is the primary reason you really need to be good in mathematics, so sticking to standard math heavy training and standard tools work for people interested in becoming a hardcore data scientist.

Is Serious Mathematical Knowledge Required?
?¢??¢ The set of techniques which covers all the machine learning aspects, the main statistical view behind the data science does not use any mathematics or the theory which is statistical.
 ?¢??¢ Data science can be learned by anyone very accurately and easily if the person is having a strong background of data and programming. 
?¢??¢ there is a set of techniques which is developed by the mathematically oriented by the data scientists that do not prefer statistical upgrading.
 ?¢??¢ These techniques work just as well and some of them have been proved to be equivalent to their math-heavy counterparts with the added bonus of generally being more robust.

MathFree Techniques Covering A Good Chunk Of Data Science
Advanced Machine Learning with Basic Excel: this method is mainly the implementation of the technique which has the basic Excel in it which are very easy to understand.  This is mainly available in Python, Perl, Julia, and R. It?¢??s currently available in Python, Perl, Julia, and R. This method will also support an SQL implementation in the future.
ModelFree Confidence Intervals One needs to have a basic understanding of random variables and probability distributions to know the concept of confidence interval. These confidence intervals methods are based on percentiles which are very easy to understand, math-free and highly reliable to use for predictive analytics.
Tests of Hypotheses One of the difficult topics for students taking stats classes. Here, it has been replaced by a simple variant of my confidence intervals, so understanding the concept is direct.
Jackknife Regression with Excel: These regression techniques are so simple and efficient that it can be easily implemented in Excel or SQL.
Jackknife RegressionTheory There is no statistical theory, behind it not even the linear algebra. Yet it comes with confidence intervals. In this method even after using few meta parameters, the loss of accuracy compared with classic regression is a bare minimum. The methodology works well in the presence of outliers, highly correlated features, or other violations of the assumptions that must be satisfied by one?¢??s data set when using traditional regression.
Indexation, Cataloguing, and NLP This method is a math-free approach to supervised clustering.
Fast Combinatorial Feature Selection: In this method, some techniques which are traditional are based on the variant principle reduction where understanding the concept of the random variable is required. 
Variance, Clustering, and Density Estimation In these methods, no mathematics is involved.

Source: HOB