Introduction
Called the “Sexiest Career of the 21st Century“ by Forbes, Data Science is one of the new fields for technologists. According to data scientists, their role gives them such happiness. Thus, numerous people, with or without technical knowledge in IT want to dive into the world of Data Science. But still, there are some factors that make Data Science very confusing for some people, especially for beginners. What should you know if you want to kick start a career in Data Science?
Actually, the term of Data Science itself is a bit confusing because it is a combination of Machine Learning, Artificial Intelligence, and Analysis. Moreover, the definition of Data Science, and especially the role of a data scientist depends on the enterprise or firm in which you are working. Most importantly, you have to set your mind up when it comes to what Data Science is actually or not.
What Data Science Is Not
It is important to know what is involved in the field of Data Science and specifically the pros and cons. In just a few points, here is what Data Science is NOT:
- Data Science is NOT statistics.
- Data Science is NOT programming.
- Data Science is NOT about MS Excel or SQL only.
- Data Science is NOT communication.
What Data Science Is
The keyword in “Data Science” is Science and it can be defined as the science through which insights can be extracted from raw data to make better decisions at all the levels: social, health, economic, just to name a few. Also known as Data-driven Science, Data Science is an interdisciplinary field about scientific methods, processes, and systems to extract knowledge, or insights from data in various forms, either structured, semi-structured, or unstructured. It is a field that puts together statistics, programming skills, communication skills, and some business sense. To put it in a nutshell, Data Science consists of using data to predict future events and using those predictions to drive business decisions.
General Skills a Data Scientist Should Have
Do you want to become a data scientist? Then, as a beginner, it is crucial to know the skills to be known by a data scientist. Basically, a data scientist has programming and communication skills, statistics knowledge, and specific domain knowledge (the space in which the data scientist works. It can be in healthcare, energy, or agriculture.)
- Mathematics Fundamentals:
- Descriptive Statistics,
- Inferential Statistics,
- Linear Algebra,
- Single and Multivariate Calculus.
- Programming Languages: Depending on the programming language chosen by the learner (Python or R) the following topics should be covered:
- Data Types and Data Structures,
- Control Structure,
- if-else statement,
- Loops,
- User-defined functions,
- OOPs (Object Oriented Programming) Concepts,
- Module Creation,
- Exception Handling.
- Understanding Databases: It is recommended to learn about SQL Scripting.
- Data Collection & Wrangling: This part is very important especially when the learner is a beginner. He or she will learn more about:
- Data Cleaning,
- Data Manipulation,
- Web Scraping for Data Collection.
- Exploratory Data Analysis & Visualization/Storytelling: The beginner will acquire skills in:
- Exploratory Data Analysis,
- Data Visualization Techniques and their Use,
- Data Visualization Tools such as Matplotlib, Seaborn, and Plotly,
- Dashboards Development using Excel, Power BI, and Tableau.
- Machine Learning (ML) & Artificial Intelligence (AI): Topics that are covered here are:
- Machine Learning Types – Supervised, Semi-supervised and Unsupervised,
- Classification and Regression Problems,
- Bias-Variance Trade off,
- Under-fitting and Over-fitting Problems,
- Imbalanced Datasets and how to deal with it,
- Model Evaluation Techniques for Classification and Regression.
NOTE: All these skills can not be mastered at 100% by the same individual. Although they are the basics skills, the learner should later focus on one of the different roles in Data Science and work on it deeply.
Different Roles Involved in Data Science
There are many roles in the field of data scientist and what is awesome with that is it depends on the employer/enterprise needs; you can have many titles with the same skills. So, depending on the companies and their needs, Data Science roles can be:
- Data Analyst,
- Data Scientist,
- Data Engineer,
- Business Intelligence Analyst,
- Machine Learning Engineer,
- Decision Scientist, or
- Research Scientist.
Four Ways to Start a Career in Data Science
- Academia: Even if it seems to be too expensive for some people, an academic degree is one the best ways to get started with Data Science. As a benefit, this can definitely add credibility to resumes.
- Bootcamps: An awesome way to start a career in Data Science is to sign up for bootcamps. The benefits are the availability of practical projects on which the learner can work and the presence of a mentor who is there for guiding.
- Industry Pivots: It is about people who are changing careers or may have been introduced to Data Science because of their past works. In these cases, it appears very important to look for data-focused roles at organizations that leverage data in industries similar to your past roles.
- Self-Taught: This may seem too long for the learner but with great determination and a learning schedule, it’s possible. YouTube, MOOCs as well as personal projects can be of nice help in achieving your learning goals.
Summary
As a beginner in Data Science, there are some myths and knowledge you have to learn about before going far in your journey. This article basically highlights some aspects of Data Science such as the definition, the different skills, the roles involved, and the ways through which someone can learn Data Science.
