Learning data science can seem intimidating. You need to know statistics, programming languages, and machine learning algorithms to excel.
But don’t worry! You don’t have to be a computer science major to master data science.
In this post, we’ll cover the skills you need to know to prepare for a career in data science. We’ll also include places to learn the techniques you need to become a data scientist!
How to Learn Data Science?
Many people learn with the help of a long list of textbooks. However, the best learning is by trying and building things. A proactive approach can take you places.
When you learn by doing, you develop the desired skills much faster than any other approach. With data science, this point becomes all the more relevant. So how do you initiate your process of learning the subject?
First and foremost, learn to love the field you want to pursue. Consistent motivation is an essential key in becoming proficient in something as challenging as data science. Now let’s go over the points.
Step 1 – Understand What You Need to Learn
Data science might seem like a scary and overwhelming field at first glance. A usual advice people will give you before getting to data science is to master many things.
From linear algebra, statistics, programming, calculus to machine learning, deep learning, and natural language processing, you will be told to master them. However, is that the right way?
No!
You need to understand what the essence of data science is. It is the process of asking intriguing questions with the help of data. In a usual scenario, here is how data science workflow looks like:
- Ask a question
- Collect data that might be useful in answering the said question
- Clean the data
- Analyze, explore and visualize this data
- Evaluate and build a machine learning model
- Spread results
In such a workforce, you don’t need to be a master of deep learning or advanced mathematics. It doesn’t require any skills you are usually asked to learn before starting with data science.
It requires an in-depth knowledge of a programming language and working with data using that language. Yes, you will need a certain degree of mathematical frequency to be a pro at data science.
However, all you need is some basic understanding of mathematics to get started, and you are good to go. The same goes for the other skills. You will feel their need as and when you progress as a data scientist.
But to start your career, your passion and abilities play a significant part. You can start right now!
Step 2 – Work With Python
R and Python are both fantastic programming language options for data science. While Python is better received in the industry, people prefer R more in academia. However, both languages are great when it comes to supporting the data science workflow.
We’d recommend you not to take too much on your plate by trying to learn both languages. Focus your energy on one language and its ecosystem of data science packages.
If you want a few courses to get started on Python, here are some of our favorites:
- Google’s Python Class is ideal for you if you have previously tried your hand at programming. It consists of lecture videos plus downloadable exercises
- DataCamp provides an interactive, short course for starting with Python
- Python Jumpstart by Building 10 Apps is an excellent, insightful video course. Michael Kennedy, the host of the “Talk Python To Me” podcast, teaches this course
- Introduction to Python feels like an interactive textbook and is a comprehensive course for starting Python
Step 3 – Learn by Doing
The best way to learn anything in data science is by working on projects. When you work, you will gain skills that are useful and applicable. In the real world, data scientists have to see projects from start to end. A significant chunk of the work includes cleaning and managing.
By working from an early stage, you also get the opportunity to build a strong portfolio.
Now, your next question might be – how can I find a suitable project? The first step to that should be to look for a data set that you find interesting.
To help you out, we have listed some good places to find data sets free of cost:
- 19 places to find free data sets
- UCI machine learning repository
- Data sets subreddit
Another technique that could probably be useful is looking for a serious problem, predicting the stock market, easily broken down into small steps.
Through such a process, you can get a hold of filtering data, visualizing data, merging datasets, cleaning messy data, handling unorganized data, and so much more. You will learn as you do and thus won’t be overwhelmed.
Learning without application is tricky because you tend to forget it easily. Once you apply the skills, there are very few chances of you forgetting them. Thus, your efficiency also increases.
Step 4 – Understand Machine Learning Well
Machine learning is quite a complex field. You might have a lot of questions when you start with it. Machine learning models aid you in predicting the future or automatically extracting insights from data.
You might be confused about selecting the perfect machine learning model for your datasheet, interpreting and evaluating the results of your model, and so on. To clear those questions, you need to do an intensive study of the subject.
Here are some of the resources that might be useful to you:
- An Introduction to Statistical Learning: This book helps gain a practical and theoretical understanding of multiple essential methods for classification and regression. You don’t need to worry even if you don’t have a good hold over advanced mathematics
- OpenIntro Statistics: This resource is an excellent help to brush up your knowledge about statistics and probability
Step 5 – Learn to Communicate Insights
As a data scientist, your value is dependent on the results of your analysis that you will present to others. If you do this well, you rise from being an ordinary data scientist to an extraordinary one.
The data analysis you make is only of value if you can convince the other party that it is of use to them. Thus, communicating data is essential.
So, what does communicating insights include? First of all, you need to understand the theory and topic. To explain something to others, you need to have a firm grip on the subject yourself. Moreover, you need to organize your results.
The final straw is being able to explain your analysis lucidly. Okay, now that you know what is vital for good communication, how do you efficiently? Here are a few suggestions:
- Write a blog. You can post your results here. Alternatively, find an authentic platform and submit your pitch there
- Try to convince your friends! Yes, teach your less tech-savvy family and friends about data science concepts. Teaching can be a fantastic tool to help you understand complex topics yourself
- Be confident and try to talk at meet-ups
- Make use of GitHub, where you can host and share all your analyses
- Be as active as possible on communities like the machine learning subreddit and Quora
Step 6 – Learn From Your Peers and Keep Practicing
You indeed learn best from your friends, peers, and your colleagues. Teamwork is essential in data science. Most data scientists prefer working in small teams to solve problems. The flow of ideas and brainstorming gives the best results.
Finally, it would help if you constantly challenge yourself. Never get too comfortable with a project because that means you are stagnant. Practicing is essential to become a great data scientist.
Giving yourself more challenging tasks will ensure that you are always at the top of your game. Here are some of the suggestions to increase your difficulty level:
- Work with larger data sets
- Try to make your algorithm faster
- Understand the theory of your algorithm well
- Try to teach an amateur what you are doing
As we mentioned, teaching is the best way to upskill oneself. Thus, the last one can help you enhance your skills quite efficiently.
Summing Up
Contrary to popular belief, there is no universal way to start learning data science. The most important thing is the passion for learning data science and the dedication to stick by it.
This is not an exact how-to to start learning data science. However, the best data scientists do swear by these techniques. You can only learn by doing. You can read theory all you want; however, learning by application is the best.
You have fewer chances of forgetting concepts and have the advantage of learning skills immediately. Of course, you need to learn Python and Machine Learning as you delve deep into the world of Data Science.We have listed a few resources to help you get started with those. Also, it would help if you learned how to communicate your results. Finally, learn as much as you can from your peers and practice consistently to get better every day.
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