Today I
bring to you “Roadmap
to learning Data Science for Beginner“. This article will focus
on beginners and intermediate learners.
Data Science has been a buzzword in recent times and Data
Scientist was the sexiest job of the 21st Century.
With all
companies big or small rushing to use this technology in their businesses. This
has caused a stir in the atmosphere with huge opportunities and
high-paying data
analyst jobs.
In this article, I will show you the roadmap to learn data
science as a beginner. Nope! you do not need Ph.D. in data science. You can be
from any background and still learn data science.
There is overwhelming information on the internet and as a
beginner, it will be confusing. If you are thinking about why you should listen
to me, then guess what I am a beginner too. This article will be the one that I
will follow.
What Is Data Science?
Data
science is an interdisciplinary field that uses scientific methods, processes,
algorithms, and systems to extract knowledge and insights from many structural
and unstructured data [Source].
In data science, you will be working with a huge amount of data
generated by the business. You will be extracting, cleaning, and analyzing it
to extract valuable insight and information from it. These insights are then
used by businesses to make important decisions.
How To Learn Data Science For Beginner?
As with learning any other subject, you will need the interest
to learn and put in some effort. Yup, it is that easy.
Learning
data science will depend more on the type of profession or job opportunities
that you are interested in. In my other posts, I shall be discussing Data
Analyst,
Data Engineer, and Data Scientist.
1. Mathematics, Statistics, And Probability
Maths skill is pretty essential in data science. You do not need
to be a maths expert, however, the knowledge of linear algebra, calculus,
statistics, and probability is a must-know.
When you are building your data science portfolio or working on
some project, these skills of mathematics will help you understand what is
happening inside those projects.
You can
take a course on Khan Academy. It is one
of the websites where you can learn for free and they are good content with the
simplest form of explanation.
2. Python And Important Libraries
If you are not from a computer science background, then you
might be overwhelmed by seeing programming language here. Don’t be!
Programming language is a tool that would make your
understanding much better. Again, this will depend on the type of job you are
looking for. For instance, if you get already cleaned data, then even the
knowledge of excel or Power BI, or Tableau would be essential.
I am a programmer and I use Python on daily basis for my tasks.
So, I would be following this path. Alternatively, the R programming language
is also a popular choice.
So, as I have chosen Python, here are some of the popular
libraries that are a must-know.
- Numpy:
Numpy is a numerical library for python and this is the first library you
must learn for data science in python. This library makes it easy to do
numerical operations with Python. Numpy helps you in working with linear
algebra.
- Pandas:
Pandas is a Python library built on top of Numpy for faster data analysis,
data cleaning, and data pre-processing. Once you have learned Numpy, next
you should learn Pandas.
- Matplotlib and Seaborn: With the above two libraries, you will have the
final data ready. Next, you will need to analyze those data with visual
charts. This process is called the visualization of data. The tools you
will most often use for data visualization are Marplotlib and Seaborn.
Matplotlib is a comprehensive Python library that helps to create static,
animated, and interactive visualizations. Seaborn works similarly to
Matplotlib but has several other capabilities. It provides a high-level
interface for drawing attractive statistical graphics.
3. Machine Learning Algorithms
You got data, cleaned it, and could visualize it. Now what?
Machine
Learning is
required to analyze these data and extract meaning out of them. The ML
Algorithms are important to make predictions or find relations among data that
you have.
You can
read my article on How
I started Machine Learning in 2022.
Among several Machine Learning Algorithms available today, you
need to focus on are Linear Regression, Logistic Regression, K-Nearest
Neighbors, Support Vector Machine (SVM), Decision Trees, Random Forests, Neural
Networks, etc.
When you get into Machine Learning the python libraries such as Sci-kit Learn will be a must-know.
Sci-kit Learn comes with all the algorithms
I mentioned above. As you advance on Machine Learning skills you will come
across libraries such as TensorFlow, Keras, and PyTorch. These will be used for
Deep Learning.
I will be writing about Machine Learning as I proceed with it in
the future.
Conclusion
Data Science is a huge field. This is just a scratch of it. I am
just getting started with Data Science, the interest in this was long-standing.
So, I do not know lots of it, however, I am here to share my journey with you.
To summarize the 3 steps of the roadmap to learn data science:
- Mathematics,
Statistics, and Probability
- Python
and Important Libraries
- Machine
Learning Algorithms
The next step for you would be to look for specific skills that
you need. This will include the type of position or industry you want to apply
to.
I hope this article on Data Science for Beginner was helpful to
you. If so, let me know in the comments down below. Also, feel free to let me
know your doubts or queries.
I would appreciate it if you would be willing to share this
article. It will encourage me to create more helpful articles like this one.