Data Science – 5 Tips For Getting Started

Opportunities Data Science Skills Unlock

The scope of data science holds vast promise and plentiful chances. Doing a search on Indeed for “data scientist” shows that more than 15,000 data science positions are available, most with salaries in the $90k to more than $100k bracket. Data science and artificial intelligence specialists have earned places in the top 14 and 15 positions in LinkedIn’s 2021 report of Roles that are Growing. Although data scientist was not the leading occupation in Glassdoor’s yearly ranking in 2020, it has regained the second place spot in 2021.

Data science skills can be used for various job roles aside from just the position of data scientist. Experts think that possessing data science abilities will assist people in enhancing their occupation, providing job seekers with those abilities a superior advantage versus the other candidates. If you hold a position in areas such as finance or marketing, expanding your skillset to include data science could potentially offer fresh possibilities for your career.

Eric Van Dusen, the curriculum coordinator for data science education at the University of California (UC), Berkeley, believes that data science is a necessary skill for the 21st century. “Every field. I inform students that it is necessary for them to possess this set of abilities. You will possess much greater authority in whichever profession you pursue.

How Hard Is It To Learn Data Science?

The level of challenge one faces when attempting to learn data science is dependent upon prior knowledge or experience. It is a benefit to already possess an understanding of computer science and math when transitioning to data science, similar to how knowing other languages assists when trying to learn a foreign language.

Online data science courses and programs from edX offer you an adaptable way of determining what aspects of data science you find attractive, which route to pursue, or if you’d be more pleased utilizing data science abilities in a role that isn’t data science-associated.

“The first step is the largest,” Santarcangelo said. “You’re going to make the biggest jump. Your first few steps will bring you the majority of the way there. A whole year dedicated to data science will lead to significant progress.

Can You Teach Yourself Data Science?

Data science is about doing. Download programs to begin your first programming language. Brush up on the mathematics behind data science. Play with data visualization using open-source tools. The deeper you venture into data analysis and exploration, the simpler it is to become a data scientist. In the end, you probably will require some direction.

Tips to Guide Self-Studying Data Science

1. Start Anywhere—But Start

To important things to keep in mind as you navigate your learning experience:

• Start somewhere: There is no “right way” to pursue a career or education in data science. The process itself will teach you where your strengths and interests lie. Some applicable computer science advice from David Joyner , Ph.D. Executive Director, Online Education & OMSCS, College of Computing, Georgia Tech: “I think the best way to learn is to take a computer science class, learn what’s possible and then decide, ‘Using what I’ve learned here, what could I build that would be of strong personal use to me?’ Even if it’s just a personal project.”
• You don’t have to know everything: Data scientists learn by doing, so choose a project and just dive in. For example, in IBM’s Python Professional Certificate program on edX, a project mini course is built in to provide that critical hands-on experience.

2. Pick Up a Programming Language

You cannot learn data science without learning to code. Data scientists construct programs and operating systems to operate those programs. Of the handful of popular programming languages for data science , here are a few to consider starting with:

• Python: Python is beginner-friendly , mimics English syntax, offers abundant libraries and community support, and has a wide variety of applications beyond data science. It’s a general-purpose language with enough add-ons that you can perform a wide range of data science tasks from statistical analysis to visualization and beyond.
• R-programming: R is a contender if you’re interested in or already in research and adding data science to your skillset. It uses statistician syntax, handles massive large-scale data, and communicates those results through robust and rich visualization.
• Context-specific language: There are lots of powerful and viable alternatives to learning Python or R . Find out which languages your current or ideal company uses. Choose one based on the conditions of your personal journey

3. Practice The Fundamentals

The procedure of data science is comparable to the scientific method, but it gives special attention to making certain that all the given data is top-notch. The majority of data science is data wrangling, as without proper data, the resulting observations and conclusions are worthless, or worse, wrong.

Here’s what a typical data science workflow looks like:

1. Ask the question
2. Find your data, whether it’s from in-house data, a public training dataset, or data mining you’ve done yourself
3. Clean the data
4. Analyze and explore
5. Communicate and/or visualize the results
   

4. Dive into the Technical

The technical components of data science can be advantageous, especially when using the conventional learning model. There are fundamental mathematical principles that distinguish data scientists from data hobbyists in the area. Some essential concepts for budding data scientists are:

1. Linear algebra : Training in linear algebra teaches you the very foundations of data science algorithms. Linear algebra also makes it easier to grasp deep-level calculus and statistics.
2. Calculus : Training in calculus teaches you the underlying theory of machine learning algorithms. Differential calculus looks at the way things change over time.
3. Probability : Probability and prediction are a massive part of the appeal of data science. It’s vital for analyzing data affected by chance and change, i.e., a vast majority of current data.
4. Statistics : Statistics training unlocks the underlying structure of data and gives it form for insight.
5. Regression analysis: Learning regression analysis gives you a dynamic understanding of relationships between data points. It opens up rich visualization techniques that help tell powerful data stories and prevent misleading visualizations.

By receiving top-notch tuition, you can gain an understanding of the mathematical and statistical principles underpinning data science, allowing you to find interesting ways of manipulating and interpreting data, as well as presenting results.

5. Delve Into More Advanced Topics

Truly excelling in data science necessitates developing your basic data science capabilities beyond just conducting data analysis. Exploring advanced topics can provide inspiration for your data science specialization:

• Neural networks: Building machines that can learn without serious human intervention involves building machines that behave like the human brain. The study of all three neural networks—artificial neural networks (ANN), Convolution Neural Networks (CNN), and recurrent neural networks (RNN)—is the study of putting human cognition into the mind of machines.
• Machine learning: Machine learning applications involve building algorithms that can process data and learn from it, getting better over time without much human intervention. This has applications in a variety of industries and is a hot topic for employers.
• Deep learning: Going one step beyond machine learning, deep learning uses several layers of algorithms to get closer to human cognition.
• Natural language processing: Building machine cognition involves machines understanding human communication and the ability for machines to communicate back in human-like language.

Bear in mind that if you want to continue in data analysis or become a data analyst, you might not have to go over all the artificial intelligence subjects.

How to Develop This Skill

There are many online classes offered on programs like Coursera and EdX and a big selection of YouTube movies about data science matters. Rather than looking for “programming courses,” it may be best to search for “data science courses,” so that the material learned is pertinent to the field of data science. There are several useful data science textbooks which can be found online at no cost.

In my estimation, the best cost-benefit pick is a digital platform that allows you to create code and execute it while you are studying. Dataquest, the place I work at, and Codecademy are examples of this type of platform, and the clearest advantage they provide is giving you the chance to immediately use and write code to test out everything you’re learning.

Data science programming can be obtained from many different sources. Bear in mind that just viewing someone code is not the same as having the competence to do it yourself – if you are opting for an online tutorial, make sure you set aside plenty of time to carry out what you are studying by truly writing and executing code.

How to Demonstrate It in Your Job Search

It is important to demonstrate your programming abilities in your resume and GitHub. You should list them in the skills section, but also highlight them in the data science projects you feature on your resume and GitHub account. It is essential to make sure that the code on your GitHub is in an organized and easily understandable state, with the proper annotations—you want any potential employer who views one of your projects to be able to quickly recognize that you have outstanding programming skills.

It is probable that your programming abilities will be examined during the job interview. Every job is different, but it’s likely you’ll encounter one of the following:

• A technical interview that tests your coding skills (which may entail answering questions verbally, or writing real or pseudocode on a whiteboard).
• An on-site project where you’re asked to complete a coding task in a set period of time.
• A take-home project where you’re asked to complete a coding task and return it by a specified date.

Be prepared to respond to 7 inquiries during any data science interview.

No matter the programming language you opt to utilize, knowing SQL is also necessary. SQL can either be said as “S.Q.L.” or “sequel”, and it is a type of query language. Fundamentally, it is a type of computer coding used to ask for and narrow down data from a database.

SQL often gets overlooked by aspiring data scientists. Compared to something more exciting like deep learning, this language is quite antiquated and tedious. Do not get it wrong, possessing SQL abilities is indispensable for any data science job due to the fact that most companies store the information they have in a SQL-based database. It is a fact that in the year 2020, SQL was employed by data scientists and data analysts to a greater degree than either Python or R.

How to Develop This Skill

The same way that one can program, there is a broad selection of online tools available to teach SQL, ranging from instructional videos, books, and engaging web pages. Mode Analytics has a free SQL tutorial which has received great reviews and can be completed by someone with no prior experience. Many websites that offer instruction in data science programming and other data science abilities will also have lessons concerning Structured Query Language.

How to Demonstrate It in Your Job Search

Put examples of SQL-based projects on your resume and GitHub profile. If you have an interview, take the time to brush up on SQL since employers are aware of its significance and it could be included in a technical interview or an interview assignment. You may be presented with a testing of your knowledge of SQL fundamentals such as the structure for an inner or outer association, or you could be requested to create and execute actual queries or draw them on a whiteboard.

This is actually a catch-all phrase that encompasses a couple of similar but distinct abilities.

Data sanitation is an essential skill for anybody who hopes to work with data. Data cleaning is the transformation of a data set in order to prepare it for analysis. This can involve tasks such as formatting, correcting typos and removing duplicates. Many may not enjoy it, but data cleaning is a necessity. And don’t worry! You will be performing all these purification tasks by using your coding proficiency, not sifting through spreadsheets manually.