Home > Why becoming a Data Scientist is NOT easier than it seems?

With the growing popularity of Artificial Intelligence and Machine Learning, being a data scientist is turning into a status symbol. But often we underestimate the hard work and skill set required for becoming a data scientist.

Every fourth individual after completing Andrew Ng's machine learning course claims to be a data scientist. However, in reality, a data scientist requires a much larger skill set than a rudimentary understanding of a handful of learning algorithms.

To be a successful data scientist one not only needs a deep understanding of the various mathematical tools which are being used to perform statistical analysis as a part of ML but also a thorough understanding of several other frequently required tools.

Most companies have their servers written in languages such as Java, Ruby, Python, etc. These languages offer various powerful tools to perform mathematical operations to generate statistical analysis, used in machine learning.

Python has libraries like Scipy, Numpy, and Scikit-learn that are helpful in solving numerical problems. Java also has several libraries like the Mahout math library.

One needs to have a proper understanding of the large and pre-existing code bases to avoid reinventing the wheel every time they need to implement an operation while designing their algorithms.

Algorithms such as Bayesian Learning used for Naive Bayes algorithms, Decision trees, random forest algorithms, and logistic regression are few algorithms which are regularly used in ML application for classification of data. Every data scientist must possess a good understanding of how these algorithms work before designing a model.

Extracting good features requires a deep understanding of the problem, the underlying distribution of the data, and/or familiarity of how the data is being generated. It might help to know about Convolution, Wavelets, Time Series Analysis, Digital Signal Processing, Fourier Transforms, etc.

Real-world data is ugly and unstructured. Every data scientist should have a good understanding of how to perform data cleaning operations in the programming language they are working with. This helps reduce the computational load later. Furthermore, this helps maintain a good level of clarity which never hurts.

Most of the ML courses available online barely scrap the surface of statistics. But to design efficient and accurate algorithm, sound knowledge and understanding of statistical tools such as mean, standard deviation, confusion matrices is a must.

Plotting the results obtained in the form of graphs add a whole new dimension, in terms of clarity in your project. Studying the results in the graphical form can reveal patterns which may not have been visible earlier. Programming languages such as Python provide an array of visualization tools that every aspiring data science engineer should be well acquainted with.

Tools such as Conjungate Gradients, Partial Differential Equations, Numerical Analysis, Lagrange Multipliers, Numerical Linear Alegbra, Convex Optimization, Vector Calculus, Stochastic Processes are important for the optimization and debugging of the final code.