Becoming a Data Scientist: A Complete Roadmap

What is Data Science?

Data science combines programming skills, domain expertise and knowledge of mathematics and statistics to collect meaningful insights from data. By applying machine learning algorithms to numbers, text, images, video, audio, and more data scientists develop AI systems so that to perform tasks usually requiring human intelligence. Afterwards, these systems generate useful insights which analysts and business people can turn into a business value. Data science takes out knowledge from big data sets and applies the knowledge and insights from data to solve problems in a wide range of application domains.

By applying machine learning algorithms to numbers, text, images, video, audio, and more data scientists develop AI systems so that to perform tasks usually requiring human intelligence. Afterwards, these systems generate useful insights which analysts and business people can turn into a business value.

Simply put, data science identifies, represents, and extracts meaningful information from complex data sources to predict future patterns and behavior and, in this way, is used for decision-making purposes. Data scientists bring structure to large quantities of unstructured and potentially incomplete data sources and make analysis possible.

Why Become a Data Scientist?

The demand for data scientists is rising as companies look to get valuable insights from big data to meet their business goals. Data scientists have unique skill sets and industry experience and play a prominent tech role in both business and IT sectors. So there are many reasons to become a data scientist. First of all, data science is now one of the most in-demand and highly paid careers globally. According to Glassdoor, the average salary for a data scientist is $117,212/yr. in the United States. Secondly, data scientists are very hard to find and hire, meaning that they are given a very competitive market for their services. After all, skills needed for data science are constantly changing, so data scientists learn a variety of new skills on their career path and can have a significant impact on specific company projects.

Data Scientist vs Machine Learning Engineer

While there are many similarities and overlaps in skills between machine learning engineers and data scientists, there are basic differences at the core. A data scientist analyzes data and gets insights from the data. A machine learning engineer focuses on writing code and deploying machine learning products.

Generally, data scientists focus on the modeling side, and machine learning engineers work on the model deployment. So, data scientists focus on the ins and outs of the algorithms; they build the model and concentrate on studying the statistics and analytics involved, while machine learning engineers analyze the code and its shipping into a production environment that will interact with its users.

Steps to Learn Data Science

Here are some resources to start with learning data science:

Learn Python

The roadmap for data scientists begins from learning Python.

To learn data science from scratch, the first thing one needs to do is learn how to code and have expertise in at least one programming language. Python is one of the widely used programming languages in the field of data science. Python provides a rich set of libraries to implement complex machine learning algorithms, visualization, and data cleaning. In addition, Python is a highly versatile programming language; it has a very clean and simple code syntax. Python can be a good choice for developing a web service to enable other people to upload datasets and find outliers or to create a tool or service that uses data analysis.

Learn Python – Free Interactive Python Tutorial

Python Tutorials

Learn Data Science

After a better understanding and some programming experience in Python, it is time to start learning the basics of data science. You can start with learning how to use libraries like NumPy and Pandas for data analysis and visualization libraries like Matplotlib and Seaborn. Also, you can get to study the basics of machine learning, how the different machine learning models work, and how they are implemented in Python.

Datacamp: Machine Learning Fundamentals with Python

Udemy: Python for Data Science and Machine Learning Bootcamp

Learn Statistics

Learning statistics is essential to gain a better understanding of data science. This is because statistical concepts are used to get insights from data and make decisions. Due to this, a strong background in statistics is crucial to becoming a data scientist. The most important concepts in statistics are the different probability distributions, standardization, descriptive statistics, random sampling, hypothesis testing, and the central limit theorem.

Introduction to Statistical Learning

Khan Academy Statistics and Probability

Coursera Statistics Course

FreeCodeCamp Statistics Course

Learn Data Cleaning

Data cleaning is supposed to be a foundational element of basic data science. In most cases, data is not clean and formatted for use; it has become more sophisticated. So data cleaning is a skill data scientists need to identify and correct incomplete, messy, or irrelevant parts of the data from a database.

Data Cleaning course by Kaggle

Blog — Cleaning Data Using Python

Learning Pandas By Kaggle

Resources to learn data science

Data science is one of the most promising jobs today, and the must-have skills you need to pursue a career in this field are programming skills, a strong background in math and statistics, data analysis, and machine learning. To get started, you can use the above-stated resources, including FreeCodeCamp, Udemy, DataCamp, Kaggle, YouTube videos, and other online resources to gain a general understanding of fundamental data science concepts, grow your skills and reach your next career objective.