A step by step guide, with 50+ resources to make 2021 your year of meaningful Data Science.
Authors: Rohith Paul
“Listening to the data is important… but so is experience and intuition. After all, what is intuition at its best but large amounts of data of all kinds filtered through a human brain rather than a math model?” — Andrew Lang
Step 1: Math & Stats for Data Science
One of the most important steps as Data Science is a quantitative domain and core mathematical foundations will serve as a base for your learning.
Probability is the measure of the likelihood that an event will occur. A lot of data science is based on attempting to measure the likelihood of events, everything from the odds of an advertisement getting clicked on, to the probability of failure for a part on an assembly line.
Once you have a firm grasp on probability theory you can move on to learning about statistics, which is the general branch of mathematics that deals with analyzing and interpreting data.
The studies of vector spacing and linear mapping between these spaces. It is used heavily in machine learning, and if you really want to understand how these algorithms work, you will need to build a basic understanding of Linear Algebra.
Python is an interpreted, high-level programming language. Python allows programmers to use different programming styles to create simple or complex programs, get quicker results and write code almost as if speaking in a human language. It was named after the comedy troupe Monty Python in 1991 and is one of the official languages at Google.
R is one of the best programming languages for analysis and visualization with its expansive community and interactive visualization tool and packages like ggplot2 making it one amongst the most used languages in Analysis and Data Science
Before working on a Machine Learning process your data needs to be clean for modeling. Often neglected but one of the most important skills. Here are some resources that will help you in data preprocessing:
To better understand the data it is important to visualize the data to find out the correlation between different variables. Here are some resources that can get you started with data visualization:
One other domain whose knowledge is essential for a Machine Learning project is Cloud Computing because Machine learning systems tend to work better on cloud computing servers. This is because of the following reasons — low cost of operations, scalability, and huge processing power to analyze the huge amount of data. So, the blend of machine learning with cloud computing is beneficial for both technologies. If you want to get started with cloud computing here are some resources which you can refer to:
Work with collaborators all over the world solving real-world problems such as Hunger, Sexual Harassment, Forest Fires, and PTSD while further boosting your skills in teams of 40 to 50 collaborators per AI Challenge.
PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other.
5. Attend (online) conferences
One of the best ways to learn about the latest developments is by attending conferences in the space. Besides helping professionals gain knowledge through hands-on workshops, these events and conferences also provide a platform to network with industry peers and understand the latest development in this space.
Here are some amazing conferences, which you can attend online or hopefully offline soon.
Big data refers to extremely large data sets that may be analyzed computationally to reveal patterns, trends, and associations, especially relating to human behavior and interactions.
If you’re interested in getting a little closer to the hardware used in deep learning, there are some good courses that introduce programming for specific architectures. All require proficiency in C and are relatively advanced:
Performance Optimization on Intel Architecture — covers optimization techniques for Intel Xeon Phi architecture, from Intel/Colfax Research/Coursera
Introduction to Parallel Programming — CUDA programming on Nvidia GPUs from Nvidia/Udacity, covering things like parallel scatter/gather and manually implemented kernels
And if you want to build your own deep learning server from scratch,
Build and Setup Your Own Deep Learning Server from Scratch
Step 7: Stay up to date
The following websites will make sure you don’t miss any important updates.
