Popular in today’s high-velocity digital environment is the term Data Science. The reason why it
has grown so fast and is so popular is that it combines methods and discoveries from different
sciences to help businesses in data decision-making. Data Science is a multi-disciplinary field in
mathematics, statistics, computer science and information science. As businesses are
increasingly trying to monetize their data, the need for effective data science has become
something more than a trend.
Here in this blog post, I intend to walk you through the “road map” that takes a person to the
status of a Data Scientist. The whole journey will be divided into sections where we will define
the important processes, tools, and skills, and share our insights into how one can monitor the
developments of each stage.
The first step on your path to becoming a data scientist should involve gaining enough ground
on fundamentals. It includes the understanding of Mathematics, Statistics, and programming
languages and other things.
Mathematics: Linear algebra, calculus, and probability are therefore core courses any data
scientist should have taken before they embark on the journey of becoming one. These
concepts are rather fundamental to financial data analysis, modeling as well as algorithms.
Statistics: However, these basic statistical procedures should always hang over our heads:
hypothesis testing and regression analysis including confidence intervals. It will assist you to
make the right choices concerning data that you use when working on assignments.
Programming: It is important to know how to write program code, as data work requires it,
unlike, for example, textual work. Python and R are the two most used languages when it
comes to data science. Understanding how to use some libraries in Python (pandas, NumPy)
and in R (data.table, dplyr, ggplot2) will also be useful.
After getting a grasp on these fundamentals then, let’s go deeper and deeper into Data Science.
This is the phase of not making a general education, instead you focus on a specific area and
achieve that by attending the classes online or signing up for a course in any specific university.
Data Visualization: Discover how to design charts, graphs and dashboards with purpose and
how they can be used to convey some of these concepts.
Machine Learning: It will teach you ways to construct machine learning techniques that discover
hidden patterns and trends, even predicting them from data.
Big Data: Learn an overview of big data and practical information on how to process and
analyze large amounts of data at a fast pace.
Natural Language Processing (NLP): Find out how to handle and analyze a great volume of
natural language text data.
” It would also guide it becoming a proficient data scientist, something that Yannakis says is very
important because practical experience is important in the profession.” Indeed, engagement in
projects and internships within the health care industry can help to do this.
Projects: So, join data science projects which are interesting to you and will help you apply what
you have learnt thus far. Get some real experience by entering Kaggle competitions or creating
pull-requests in real projects on GitHub.
Internships: Look for companies that are value driven, which means they have a great
reputation and with which you can search for jobs; the best bet will be to look for companies
which are willing to tutor you by qualified data scientists and solve real life problems there.
The field of data science is still relatively young, and what was relevant before is not necessarily
relevant now. Utility of Web based material, conference attendance and contacting experts in
the field.
Online Resources: Some blogs and platforms that provide articles about the trends and
methods in data science include Towards Data Science- Medium, KDnuggets and Machine
Learning Mastery.
Conferences: Go for a data science conference including Strata Data & AI, NeurIPS and KDD in
order to increase one’s knowledge on the existing developments in the field as well as meet with
other members of the community.
Networking: Reddit’s r/datascience, Kaggle, and LinkedIn Groups associated with data science
should be subscribed to source information from experts.
The basics of an expert in this career involve an understanding of Mathematics, Statistics and
computer programming languages apart from enhancements in fields like data visualization,
machine learning, big data, and natural language processing. It is important to mention that the
main driver of learning is not theoretical classes but rather projects and internships. Lastly,
always continue learning because this field of data science is ever expanding and it very
important that one update himself or herself. If you follow this road map, we have presented
then you stand a good chance of becoming a professional as well as efficient data scientist.