How to Start with Data Science in 2020?
As we round the decade into 2020, technology has unpredictably transformed our world. To prepare our careers for the coming transformations, we need to adapt to these new technologies and grow ourselves in these new fields. Data Science has become the most in-demand job of the 21st century and is also named the sexiest job of the 21st century by Harvard Business Review. The top companies are looking for candidates with knowledge of data science because of the gains in efficiency and profits that companies are seeing. This blog will explore what data science is, the different fields of data science, why people should invest time in it, how to get started in it, and how it is changing. I want this blog to motivate you to begin your adventure into the fascinating world of data science.
Data science is a field that takes data and manipulates it into meaningful information, which is used to make decisions and solve problems. It is also multidisciplinary in that it uses tools and techniques to manage the data so that you can find something new and meaningful. In other words, data science is a “concept to unify statistics, data analysis, and their related methods” in order to “understand and analyze actual phenomena” with data.[1] It uses techniques and theories drawn from mathematics, statistics, computer science, domain knowledge, and information science.
So, what skills are most valuable to the Data Scientist, and what should I learn? Being a Data Scientist requires:
1. Strong knowledge of programming languages like Python or R
2. Hands-on experience in database coding, like SQL, MongoDB, etc.
3. The ability to work with unstructured data from various sources like video, voice, and social media
4. Understanding analytical functions
5. The ability to code and creates machine learning programs
So, let’s explore the data science prerequisites.
Critical Thinking: Understanding the problem you’re facing is the first step. Without the ability to think through the issues logically, you won’t be able to solve them. It helps in finding multiple new ways to solve the problem efficiently.
Mathematics: Mathematics adds a new lens to a data scientist, which allows them to see a dataset pattern. So, Mathematics is a required skill for data science. Some important mathematics topics are Linear Algebra, Multivariable Calculus, Statistics, and Mathematical Modeling.
Resources for Mathematics
Programming: Programming is the bread and butter of data science. Programming has become an essential and necessary part of being a Data Scientist; no longer are the days where a Data Scientist leaves building machine learning algorithms to AI Engineers. These jobs have become forever joined. Data science should have in-depth knowledge of at least one programming language. We recommend Python or R, as these are the most widely used programming languages for data science.
Secondly, programming is another essential thing data scientists should know. There are many programming languages, but the most popular for data science in Python and R. Starting with programming, you shall focus on one language. I would suggest Python because it’s easy to learn. For people coming from a Mathematics background for them, R is the best choice to start. Then they can move towards Python. Here are some resources for programming
Resources for Programing
Database: The depth understanding of databases such as SQL, MongoDB is essential for data science to extract the data and to work with it. You cannot work with data unless you first know how to store it and clean it.
Resources for Database
- Choosing the Right Database
- Databases 101: Introduction to Databases for Data Scientists
- Databases and SQL for Data Science
- Getting Started with SQL: A Hands-On Approach for Beginners 1st Edition
Communication: Communication plays a vital role in data science because, after solving the problem, you have to communicate with the company or teams to get feedback. Then, you implement changes and align with the customer’s wants and needs.
Completing these courses will prepare you with the necessary maths and programming requirements. Then you can deep dive into Data Science, Machine Learning, and Deep Learning.
Resources for Data Science, Machine Learning & Deep Learning
- Python for Data Science
- Data Visualization in Python
- Machine Learning by Andrew Ng
- Hands-On Machine Learning with Scikit-Learn & TensorFlow by Aurelien Geron
- The Elements of Statistical Learning
- Deep Learning by Ian Goodfellow, Yoshua Bengio, and Aaron Courville
While learning, I always try to do projects relevant to the subject I’m studying. Projects will help you in gaining hands-on experience and allow you to build a portfolio. As Data Science and Ml are very specialized subjects, you need at least a master’s degree. At the same time, you Can get a job without a degree; you need a solid portfolio and an above-average understanding of the material, which is difficult to overcome. Building a portfolio is essential; getting to know what’s trending in the market is also necessary. There are many podcasts, blogs, and meet-ups. Just spend some time on podcasts, and you will learn a lot about what is going on in the industry. Another excellent resource for learning current trends is speaking to an industry expert, many of whom are willing to help newbies.
Competitions and Trend
- Towards Data Science Podcast
- Chai Time Data Science
- Machine Learning Guide by OCDevel LLC
- Kaggle.com
This blog aimed to give you a head start on data science in this unpredictable year, 2020. I provided a simple overview of what data science is, why you should be interested in learning it, and resources that help you excel in 2020. Recently, companies have shifted their view of data science as a profession. It has transformed from a profession to skill because employers are demanding more from these professionals and see an opportunity to cut costs. I believe that the data scientist is becoming a part of the software engineer profession, as companies expect you to have good experience in building end-to-end products. Just as the data industry has changed dramatically in the previous decade, it will continue to change dramatically, with technological innovation expanding at a tremendous pace. This means that you have to continue to be flexible and continuously learning new technologies to stay relevant in this continually evolving field.
[1] Hayashi, Chikio (1 January 1998). “What is Data Science? Fundamental Concepts and a Heuristic Example”. In Hayashi, Chikio; Yajima, Keiji; Bock, Hans-Hermann; Ohsumi, Noboru; Tanaka, Yutaka; Baba, Yasumasa (eds.). Data Science, Classification, and Related Methods. Studies in Classification, Data Analysis, and Knowledge Organization. Springer Japan. pp. 40–51. doi:10.1007/978–4–431–65950–1_3. ISBN 9784431702085.