If you are reading this article, I am sure you have heard the term data science at least once in your life, But did you know that it is among the Top 10 careers to have in the entire world of technology as it stands?
You can be a college student, a graduate, a professional, or a person who’s looking towards data science as well, this will help you out.
The first thing that you should always look upon is mathematics, It literally is the foundation of so many concepts that you’ll be using in the later stages of data science.
And also, did you know that machine learning works on the basis of math and statistics to a great extent. See, it’s very important to emphasize a strong foundation in mathematics should be the starting point of your flourishing career in data science.
You should have good knowledge of differential calculus, linear algebra, matrix operations, probability theory, and of course, permutation and combination are some things that you will definitely have to look at.
Step two is definitely statistics in my option, this will be complementary to the mathematical concept you’ve studied previously.
And hence, it’s a fitting choice for second place on this roadmap, Now, see, the role of statistics in data science is key, it is used to simplify the mathematical operations on real time data that we have at hand which definitely is the need of the hour in a production environment today.
And one more thing you should be aware of is that statistical thinking is an approach right rather than a concept when you look at it and of course when you plan to put it to good use.
Now, if you have a mathematics background, use it to put theory into practice. If you do not have a mathematics background, then work through understanding the logic understanding of how the calculation takes place.
So now, you will have a question saying Okay, so what All are the things that I need for statistics, if I’m working towards my data science carrier,
While you will require everything from the measures of central tendencies, like mean, median, and mode, you have measures of spread, such as the interquartile range and the standard deviation,
you will have to understand how to spot and work with a good amount of skewed data, and of course, the types of statistics as well.
And then there’s a lot even in the case of practical applications when we’re talking about statistics, and that is very important.
3. Programming language
Step three is something you definitely expected on this roadmap, Yes, we are talking about programming, ladies and gentlemen, programming is the heart of making computers understand the data and help machines work with math and machine learning.
They provide amazing results to us, So you will require fundamentals in languages such as Python and R because this will take you a long way in your data science career.
Understanding control structures and object-oriented programming concepts are a must, And of course, working with a variety of data in a seamless fashion is also an important skill that you should have to break it down.
You would require the knowledge of the basic syntax of a programming language, the data structures that are used concepts such as exception handling, and of course, you know working with multi-dimensional data.
Now, data science talks about how we can use science to go on and handle large amounts of data and use it effectively.
4. Data Handling
You have to learn about bringing in data to the organization from a variety of sources, right? We call this data ingestion, and it forms to be the most important step in the data science lifecycle.
And what comes after data ingestion, Data manipulation, of course, you have to understand how you can work with data before you can let a machine learning algorithm take over and learn from it.
Now, I said machine learning and I hope you are as excited as me about this, Even before we talk about machine learning guys do understand this.
skipping the concepts of mathematics and statistics to get to machine learning real quick will not work in the long term.
But yes, this is the most exciting step of learning, But make sure you know everything there is to know about the concepts of mathematics and statistics before you get here.
5. Machine Learning
You can always begin by understanding the terminologies and understanding how basic algorithms work before taking a step ahead in machine learning.
Now after this, your learning should be aimed at using a result-oriented approach where you can look at things such as recommendation systems, principal component analysis, which is basically PCA, and of course, trending algorithms, semi-supervised learning, and all of these concepts more.
So what we do after machine learning, you ask? Well, it’s deep learning, of course, right? Deep Learning is a key part of your data science journey.
And as of 2021, it’s in high demand as well, The fascinating concept of neural networks is something you should learn.
This is the closest that we can get machines to come across and behave like a human brain, And as always, it is very vital to understand the latest trends and the requirements to know how to go about learning.
6. Deep Learning
I would highly suggest you look at Python libraries such as TensorFlow, Keras, PyTorch, and more, So what should you be learning in deep learning or what you know, in deep learning?
Well, the current market asks that you know your way around natural language processing the concepts of NLP as it’s called your image recognition, and the foundations of CNN which is basically convolutional neural networks.
Now working with data and producing fantastic results are only 50% of the total job here, It is not always viable to show maybe 10,000 rows or columns worth of data to your board members.
7. Data Visualization
Right. Now, this is where data visualization comes into the picture, Using this we tell stories from the data and we create beautiful looking visualizations.
It is a combination of mathematics, art, data organization, and creativity, This is what makes you a data scientist, at least in my opinion, if I’m being honest.
And one way to go about thinking is that you know you have to make the end result understandable to a child if that is needed.
That is the aspect of a good data scientist, Now understanding data streaming dashboarding tools, a variety of charts, plots and A Business Intelligence tool like Tableau will add a lot of value to kickstart your career in data science.
8. Big Data
We live in a world that generates too much of data every single day, And to data science, data is the fuel, So it is extremely vital that you have the capacity to understand the current offerings to help you with the data requirements, and how best you can use them.
Thinking about solving the world’s big data problem is also a part of a data scientist role, You can always begin by learning about the Apache ecosystem and also knowing about the offerings such as Spark pi, And more.
Here I have mentioned one of the most recommended routes by us experts that will help you get started real quick, and of course, take you a long way if not all the way to your destination of us becoming proficient data scientists.
On this note, I wish you all the very best for your data science carrier ahead.