Data science is an interdisciplinary field that requires coding, math, statistics and business acumen. The hardest thing beginners starting a career in Data Science struggle with is figuring out what to study, how to study these topics effectively and how not to give up in the learning process.

In this blog post, I will be giving you a practical guide that shows you how you can effectively study to become a Data Scientist and how to keep yourself motivated as you study.

How to study effectively to become a Data Scientist 

1. Covering the Bases: Coding and Statistics

Coding and Statistics are the first foundational knowledge you need to acquire to begin a career in Data Science. Programming is the most valuable skill anyone should learn in this digital world today because having the knowledge of coding alone gives you the toolkit to do just anything and this makes you very valuable in the tech space.

But how and where can you learn to code and to do stats?

What are the best ways to study these big subjects?

Study Textbooks

Never neglect the place of good books when you're learning to code.

Programming books are best for introducing concepts and deeply learning complex topics. Books contain great resources that allow you to zone in and find a state of ‘slow’ thought that video courses do not allow.

Remember to learn just enough. Most beginners make the mistake of trying to learn everything about a particular concept, especially the initial steps and they eventually get stuck. You don't need to know everything. In fact, nobody knows everything. Start with the basics, learn just enough to answer the questions you have currently and move on.

Here are some good textbooks you can pick up to start learning Python, Statistics and machine learning.

• Python: Python Crash Course By Eric Matthes
• Statistics: StatQuest
• Machine Learning: Machine Learning Simplified

Here are more textbook resources to learn Machine Learning and Statistics and apply them in computer programming:

• The Elements of Statistical Learning: Data Mining, Inference, and Prediction by Trevor Hastie, Robert Tibshirani, Jerome Friedman. (see the course: Statistical Learning on Coursera by the same group of authors)
• An Introduction to Statistical Learning with Applications in R by Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani
• Mining of Massive Datasets by Jure Leskovec, Anand Rajaraman, Jeff Ullman. (see the course: Mining of Massive Data offered by Jure Leskovec)

Take Courses

Asides from learning by reading from a textbook most people prefer to take an online course either free or paid. Studying a course designed by an experienced expert is a great way to learn Data Science.

A great course designed by an expert will cover the exact concepts you'll need at the foundational level and will help you move towards your goals faster.

Online videos and interactive classes are more accessible and enable you to start using code right away as compared to learning from a textbook. Online courses are excellent for learning programming "how-tos" and developing programming muscle memory.

Online courses and textbooks are best when paired together.

Our free Introductory course is a perfect learning program for you as a beginner to get you started on what Data Science and Machine Learning are all about and how quickly you can benefit from them. It will give you an overview of everything in the field, you can learn more about each topic in more depth on your own from other resources.

Here are also some other good websites where you can learn Python for Data Science. They cover the foundational introductory topics to Python and Data Science.

• Congnitiveclass.ai
• Learnpython.org

Learn more about the technical and non-technical skills you need to become a good Data Scientist in our O’Fallon Labs’ Learning Center.

Watch Lectures

In the beginning, it will be very hard to do things on your own. You will need to take help from experts. Watch how experts approach projects and how they solve problems. Kaggle and freecodecamp.org are great resources to watch experts solve problems. In this 12 Beginner Python Projects video from freecodecamp, they built 12 interesting Beginner Python Projects from scratch. The lecture walks you through the implementation of these projects step by step making it very easy for you to follow. Don't just watch the lectures passively. Follow along as you watch and understand what is being done.

Again, do not rush to learn how problems are being solved. Learn the basic concepts before moving on to watch how problems are being solved so you can get a grasp on what is being done and so that you don't get overwhelmed. The basic concepts you have to learn include:

• Computer Programming (preferably in Python and/or in R) (variable declaration, loops, statements,  functions, Numpy and Pandas, etc.)
• Basic Mathematics (Linear algebra, Matrices, etc.)
• Basic Statistics (mean, median, mode, standard deviation, variance, correlation, regression etc.) Nothing crazy here. Just basic high school Statistics.
• Data visualization (Seaborn, matplotlib, ggplot, etc.)

Remember, the goal is to know enough basics to be able to start doing a project. So, focus on project-based learning. This approach will allow you to learn faster and you will have something valuable in hand to talk about after you finish.

Do Assignment

The best way to learn and remember anything is by doing it.

As I have mentioned above, when you study, learn the minimum/basic concepts and then do an assignment. Your learning of the basics is just the very beginning of the learning process. It will enable you to do more as you need. Your real learning starts when you do an assignment.

Assignments prompt you to think more deeply about what you are learning and this immensely supports the learning process. A graded assignment that you do after learning a concept in Data Science  demonstrates that you have achieved the learning goals and that you can apply the specific skills or knowledge you just studied. It is a rewarding process

2. Challenge Yourself

This point is very crucial in your journey as you study. Changeling yourself fuels you to keep going. It's how you keep yourself excited and motivated in the journey.

How do you challenge yourself?

Do End-to-end Data Science Projects

Learning the basics is just the very beginning of your learning process. Your real learning starts when you begin a project.

Doing projects is the absolute best way to learn.

You're probably wondering why this is, right?

Research revealed that practicing something yourself or doing projects is the best way of getting knowledge and skills into your head. You learn faster, more deeply and you can retain this information longer.

When you do end to end projects after learning, you're engaging more parts of your brain than just sitting in a corner and passively consuming information.

Learning is a process and oftentimes it might get overwhelming. Project is how you scoop down and process the information you're getting into a manageable chunk and cement your knowledge. Because there are a lot of things you have to learn in Data Science.

Why you should build projects

• Learning to code by watching someone code in a  tutorial video or reading a textbook is not the same as learning to code by actually writing the code in an IDE and debugging it for errors. Doing end-to-end projects is more engaging and it is the best way to learn anything! Do it yourself, test it if it works and figure out the bugs and errors. Apply the knowledge you have learnt from textbooks, lectures and courses into building a project.
• Apart from the fact that you gain knowledge and experience when you build projects it also helps you in building your portfolio and work samples for job opportunities.
• Doing a project makes what you learn concrete and also gives you a great sense of accomplishment and completion when you finish.

Hire A Mentor

The need for a mentor in your data science career path cannot be overemphasized.

If you don't want to give up as a newbie in Data Science, the best thing is to find an expert in the field that can mentor you. This is one of the best things you can do for your career. Data science is hard and you will get overwhelmed at some point in your self-learning path, but a mentor can guide you.

The variety of skills necessary to be a competitive data scientist can be overwhelming. Having a mentor reduces some of the stress associated with this circumstance. You can get in touch with them and establish a rapport with them so that you feel comfortable posing queries that at first glance may appear obvious but which ultimately have a significant impact on the caliber of your job.

So you need one. At O'Fallon Labs we offer 1 on 1 mentoring for individuals beginning or transitioning into Data Science. You can never go wrong with a mentor. It's a journey, you'll need a guide!

Enroll in Bootcamps

Enrolling in a Data science boot camp is a worthwhile investment. They are short, immersive educational programs that prepare graduates for entry-level positions in only three-to-six months of intensive study. After a brief three- to six-month program, you as a participant become empowered and eligible for high-paying Data Scientist positions. Boot camps provide networking opportunities where organizers bring in guest speakers from top tech companies and well-connected industry pro instructors. 

Through these networking events, you get to connect with students around you that are also aspiring tech professionals and they can help you form the beginnings of your professional network in the field.

Finish Kaggle Competitions

So much of Data Science is learning from what other Data Scientists have built and then working on top of what has already been built. Because you're new in the field the best way to practice is by working through someone else's project first.

An example is the famous Titanic Data set on Kaggle. Pick one of the highly rated notebooks and work through it. It is a good start to get some ideas to start working on your ideas.

Don't go copying other people's code; instead, try and understand what each line of code does. With this, you'll gain an understanding of how to approach your own projects whenever you begin doing them.

The fastest way to learn an unfamiliar concept is to start by doing what someone else has done and then applying that knowledge to your own projects

3. Communicate with the Outside World

 Join Communities

No one thrives well in isolation. You need the support of people. Tech isn't just about coding. The tech space is more about interacting with a community of people, sharing, building and collaborating. So get involved with a data science community, and join discord communities. Be active in these communities. Ask questions, share ideas, tell your story, celebrate wins and interact with other people that are on the same path as you. Communities can be a great source of motivation and also provide great networking opportunities.

Build Your Own Network

Once you have mastered the skills and you have developed a strong portfolio, the next stage is to set up a means of sharing your work with a broader audience by building a personal brand or network. Networking is about building a relationship where both parties are rewarded. Offer value. The best way to build mutually fulfilling relationships is to have something of value to offer in return. Building a network can help build your accountability.

Lastly, actively apply for roles that interest you and go to job interviews. It's time to start applying for data science jobs now that you have all the necessary data science skills and a fantastic data science portfolio. This is where building a network becomes a highly rewarding activity. Connecting with recruiters at job fairs or recruiting events, on LinkedIn, through peers, or through other networking opportunities like conferences or meetups is the best way to find an entry-level data science position.

In order to be selected for your first data science interview, networking is a requirement. Engage in various technical discussions in data science groups, share any new data science certifications or projects you complete, and be active on LinkedIn. This is the first thing you should do to persuade potential employers to call you in for an interview.

As soon as you've established a connection with the recruiters, the next step is to demonstrate and explain how your data science expertise can assist their organizations in solving the issue. After that, your first interview will occur. Go for these interviews, interact, get feedback and ensure you implement this collected feedbacks.

In this article, we have walked you through valuable tips to follow in order to study effectively to become a Data Scientist in 2022 and beyond. When you implement these tips and with the guidance of an experienced professional ( mentor) your success is guaranteed.