Which data science learning path should you choose? How to become a data scientist?
If you have grown up watching movies like The Imitation Game and Moneyball, then you might have an inkling of what a data scientist does or what data science is.
Until 2010, scientists and enterprises were worried about the storage of data. Everyone was frantically trying to find or develop a framework that could store data.
Then, Hadoop and many other such platforms came along and solved the problem.
From here, the path to process this stored data began and we saw the rise of data science.
Google alone observes around 74,525 search queries in 1 second. Imagine the amount of data it generates daily.
Several budding professionals, entrepreneurs, and college students want to pursue a career in data science.
However, they are often ignorant of the right method to do it, if there exists such a thing.
It is hard to find the best data science learning path.
Hence, we have prepared a comprehensive guide to help you explore the perfect data science learning path.
What Is Data Science?
Data science is a field of artificial intelligence that deals with algorithm development, data interference, and technology to solve complex analytical problems.
At the core of this analytical technique is data. The raw form of data which is directly collected from various channels every day.
Using this data, the business value is achieved and extracted.
(i) Data warehouse stores millions of data units flowing in through various digital channels every day. It is a raw, unprocessed form of data.
(ii) Data insight is the quantitative analysis of data so that business decisions can be made. Insights are strategically extracted from raw information by using various tools.
(iii) Data product means using an algorithm for regular data analysis, which learns through previous experiences. For example, recommendations on an e-commerce platform.
(iv) Business value is the value achieved by businesses with the help of useful insights, informed decisions, and enhanced customer experience.
The right data science learning path helps you explore the industry use of data science, which is through:
(i) Predictive analysis
(ii) Predictive casual analytics
(iii) Predictions through machine learning
(iv) Pattern discovery through machine learning
The Ultimate Data Science Learning Path
It is necessary to note that to learn data science or set foot on the best data science learning path means spending at least 2-3 hours of time every day to learn about it.
Not only does it involve learning but you will also have to practice concepts to achieve basic knowledge.
Below, we have given the key aspects that you need to work on during your data science journey.
1. Python Programming
The first step for every data scientist, or for that matter, any professional in the artificial intelligence field, is to learn Python programming.
There is no disputing the fact that your data science learning path should commence with Python.
Get comfortable with the syntax and basic functioning of Python and understand the art of running a program written in Python in different ways.
2. Linear Algebra and Statistics
You are on a learning path for data science and it is a concept that uses mathematics, statistics, and everything that involves calculations to offer you results.
It is not possible to move forward without having a good hold of statistical and linear algebra concepts.
Give some additional attention to descriptive statistics to gain the power to understand any data set.
3. Machine Learning
If you know Python then why not understand its best data structure, Pandas DataFrame. Learning and implementing should be achieved right after studying Python. It allows you to manipulate and analyze data.
SQL is an important step in your data science learning path as you won’t be able to retrieve data without SQL. If you are unable to retrieve data, you won’t be able to work with data.
Of course, there are advanced tools available today, but there may be situations when you might need SQL such as in the case of the relational database.
4. Pandas and SQL
Machine learning enables you to understand, create, and train algorithms, which, in turn, helps you to develop real-world data-based models.
Using this knowledge, you can interpret data or even make your algorithms learn patterns from previous data.
5. Deep Learning
Deep learning helps you create mathematical models that allow you to analyze data and predict the outcome for specific use cases.
It uses neural networks and other advanced artificial intelligence concepts.
6. RNN and CNN
RNN and CNN are both neural network concepts that require an advanced understanding of machine learning, deep learning, and artificial intelligence.
For instance, CNN helps in image recognition. To reach that stage in your data science learning path, you need to thoroughly brush up all the other concepts related to data science.
Additional Tips for Data Science Learning Path
There is endless data on data science itself. You will never fall short of quality information. We have mentioned a few requisites to help you become a successful data scientist.
1. Take One Thing at a Time
Data science is a never-ending pool of information which has too much to offer. However, you don’t have to grasp everything at once. Take one concept at a time and then move forward.
Trying to learn and understand Python, Pandas, machine learning, and deep learning simultaneously will only confuse and demotivate you.
2. Learn, Apply, and Repeat
One of the first rules of becoming a data scientist is to learn, apply, and never give up. Learn, apply, and repeat if you don’t succeed. Keep trying until you achieve success.
This step is essential as there are several concepts in data science and almost all of them are important for data scientists.
Hence, you need to understand everything. It is also good for future learning habits.
3. Choose an Industry
We often forget to choose an industry. Just like you have to choose a major in college, you need to pick an industry and focus on that.
We believe this is necessary because concepts of real estate industry may not be similar to the food industry or a B2B company.
With varying customer types and differentiating work profiles, the concepts and methods change. So, you need to know your area of interest before choosing the best data science learning path.
Learning Path for Data Science With Python
Since Python is the foundation of any data science learning path, we have separately explained the steps to learn Python.
Following this learning path for data science with Python will help you grab employment opportunities sooner than later.
Read below for Python learning tips:
1. Learn Fundamentals
To learn Python, there are various ways to start and the easiest is to join a course.
Or you can start with an online tutorial, classes, or YouTube channel.
For instance, check out this Ted Talk on data science:
An additional method for fundamental Python knowledge is to join various communities. Every news and industry updates go through these communities.
Hence, it is a great method to stay updated with emerging Python concepts.
2. Practice
At the start of your data science learning path, read the code generated by other individuals.
Try to understand the essence of this code and implement it yourself. Then, start your own journey.
Many experts advise in favor of utilizing Command Line Interface, which allows the faster execution of code. With CLI, you can experiment quicker and learn more rapidly.
3. Expand
Expand your knowledge to other technologies or concepts such as SQL. Every great data scientist, though may not use SQL as frequently, but they do know how to implement it.
This means that you can learn SQL alongside advanced knowledge.
With this, also go online such as join Facebook groups, ask questions on Quora, and read/post queries.
There is only one learning path for data science with Python, which involves exploring more and more.
4. Build
Collaborate with other professionals of the industry and understand how the processes behind several concepts run.
Acknowledge the true nature and working of daily processes that almost every data scientist utilizes. It will strengthen your basics and hold on the language.
You can use Git to control multiple versions of your projects. This will help you debug and test new approaches quickly.
5. Apply
Data science is a vast field and learning all of it so quickly is not possible, which means that you have to keep applying the concepts. The more you try, the more you will learn.
Change your perspective towards data visualization, statistics, machine learning, etc. Take all these concepts as tasks that you must apply practically.
Conclusion
Every individual who is on a data science learning path requires at least 3-12 months of constant practice to learn Python. You need to give more time to understand and learn other concepts.
However, it is necessary to remember that data science is not a ‘learn today and implement for lifetime’ type of career path.
You are required to keep learning throughout your career. This is one of the fields that is observing immense growth.
In the years to come, we will see several transformations in this technology. To keep pace, never drop your learning habit. Always keep aiming to acquire more knowledge to become successful.
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