With data science aspirants inundated with a wide collection of online courses to choose from, it can get a little overwhelming. More so, when they have little idea on where to begin.
A cursory search of the internet would tell you that an understanding of mathematics, machine learning algorithms, and programming languages are a must. But what is the most logical order for you to acquire these skills?
This article seeks to simplify this journey for you by breaking down the extensive skills you need in order to succeed in this field and will act as a guide for as you navigate your journey in data science.
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Math & Statistics
Data Science involves math, and proficiency in basic algebra (linear and multivariate), probability, and statistics will hold you in good stead. Mastering statistics and strengthening your understanding of it, in particular, is key here. This is because data scientists essentially analyse data, and this will require deep knowledge of different statistical techniques, a view held by Chief Data Scientist at Netcore Solutions, Hrishikesh Rajpathak.
Both descriptive, as well as inferential statistics, will help you make better business decisions from data. What is more, mastering statistics will also help you comprehend ML methods. However, the diverse concepts in statistics — types of data variables, population and sample, random variables, probability distributions, hypothesis testing, etc. — can be difficult to grasp. These books will help you break down these concepts in a manner that is easy to understand.
Once you have a good command over these, you can eventually learn to implement these on actual data sets. But before you move to this step, you need to learn some programming languages. Ideally, you should learn this in parallel with statistics.
Also read: Five Free Data Science Courses For Beginners
Languages like R and Python will help you implement the statistical techniques you have learned to analyse data sets. While you can learn many languages, one of the popular ones that are used in machine learning (ML) and predictive analytics is Python.
As elaborated here, it is easy to learn and read, facilitates scale and offers access to a wide variety of data science libraries. Another reason for its popularity is its community, which is continuously creating additional data science libraries. This, in turn, drives the creation of modern tools and techniques available today, explaining why most people prefer Python for data science.
In order to present the analysed data in a format that is comprehensible to business analysts or corporate executives, you need to communicate your findings effectively through graphical means. Using tools like Tableau, you can easily comprehend complex findings.
This takes various forms, including graphs, charts, infographics, and other visuals to help convey key insights. While this offers a hands-on guide to Tableau, other popular visualisation tools such as PowerBI, QuickSight, can also help you make visualisations.
This is the final skill set for accomplished data scientists. By learning machine learning algorithms, you can try and build predictive analysis models. This is where the business application of data science comes to the fore. Leveraging machine learning, data scientists can make predictions about the future, driving successful outcomes in an enterprise.
There are three types of machine learning – supervised, unsupervised and reinforcement learning. In supervised learning, models are trained using labeled data and it needs supervision to train the model. Unsupervised learning, on the other hand, does not need any supervision and finds patterns from unlabeled input data on its own.
Reinforcement learning is quite different from both. Simply put, it learns from its mistakes to make less in the future. It enables learning by trial and error using feedback from its own experiences.
Frequently used algorithms include Linear Regression, Logistic Regression, Decision Tree, SVM, Naive Bayes, KNN, K-Means, Random Forest, Dimensionality Reduction Algorithms, Gradient Boosting algorithms, GBM, XGBoost, LightGBM, CatBoost, and neural network frameworks.
Neural Networks (NN) are a group of ML techniques that is modelled on the human brain. Data scientists can use this approach to identify and extract hidden patterns within data, such as images, video or speech.
This is one of the most powerful algorithms used in the field of machine learning and artificial intelligence today. As data scientists, you could take this approach to solve a business problem using large amounts of data.
Although this would be enough for you to acquire the right skills to become a data scientist, you cannot stop here. You need to learn and practice how to implement your newly acquired knowledge on public data sets, or on platforms like Kaggle and GitHub.
This is particularly important since the field of data science demands that you are just as adept at applying these skills, and these projects can also be used to showcase your skills to potential employers.