Barely two weeks into the COVID-19 lockdown, I felt the strong impulse to learn a new skill. But how do I go about it?(what do I learn and how?).
I stumbled on the Codecademy free Pro offer. Feeling thrilled, I navigated through their website and found the career path section.
Data Science!
Feeling enlivened
I have always wanted to unleash the potentials hidden in Data. Revealing insights, patterns and oppurtunities can be a fun thing to do.
What learning strategy do I implement? In what order do I learn the skills required for Data Science? These were mind-boggling questions.
This oppurtunity gave me aha! moments. I felt a rush and I just couldn't stop learning(I still haven't).
Data Science in Simple Terms
Data Science combines two words: Data(a fact given or granted) and Science(what is known, knowledge of something acquired by study, information).
As a result, Data Science can be defined as a field that involves acquiring information or knowledge through the study of a given fact. This information helps us in making well informed decisions.
It blends various tools, algorithms and machine learning principles to reveal insights and discover hidden patterns from raw data.
A company can analyze its users' data to provide meaningful insights like predicting their behaviors.
Large amounts of data are generated everyday. This has accelerated with the growth of Internet of Things(IoT).
Up till the last decade, data storage was the main focus. This has been taken care of by frameworks like Hadoop which store large data efficiently.
Since then, the world has shifted to processing this information and making use of it.
Steps Involved in the Data science Process
The work of a data scientist is to collect data from a multitude of different sources and extract information to provide solutions to relevant problems.
These are the stages involved in the Data Science process:
- Identifying relevant questions: What problems are we trying to solve?
- Data Collection/Capturing: What data is relevant to solving these problems?
- Data Cleaning/Processing: Is our data suitable for analysis?
- Data Exploration: What correlations lie in the data?
- Data Analysis: What useful insights can be derived from the data?
- Communicating Results: What has been understood from the data? What actions can be taken?
Skills Required for Data Science
- Mathematics and Statistics:You need to be good at statistics and mathematics to be able to visualize and analyze data efficiently
- Machine Learning: This forms the heart of Data science
- Domain Expertise: To be able to understand the problems clearly
- Good Programming Skills: For implementing various algorithms
- Good Communication Skills: This helps you in presenting results efficiently.
Why I Chose to Learn Data Science
There are numerous things that can be done with real world data. This article analyzes Taylor Swift as an artiste and a person through her lyrics by using NLP(Natural Language Processing) — a subset of Artificial Intelligence.
Drake holds the record for having the most number-one singles on the Billboard Hot Rap Songs chart and the most number-one singles on the Hot RnB/Hip-Hop Songs. Is he considered a rapper or singer?
E-commerce websites recommend products for their users based on their shopping history, pattern and other set of rules
Self driving cars collect data from sensors, lasers, cameras, radars to navigate the world on their own by making use of machine learning algorithms
Training a model based on historical data to automate repititive tasks is another thing. This model gets better as you feed it with more data.
These are only few things that stir up my interest in Data Science.
Why You Should Learn Data Science
- Data Science can transform you into a crackerjack in your domain
- It will give you a great career as it bridges the gap between IT and your field. You will be able to enjoy the best of both worlds
- It will also help in your personal growth as you'll develop a great problem solving attitude.
Why I am Blogging About Data Science
I felt it was worthy to share my learning process with anyone considering to learn Data Science from scratch
I'll be explaining various concepts in simple terms that are easy to grasp even for an absolute beginner.
Here are some topics we'll be covering:
- Programming Languages: SQL and Python
- Pandas for Data Analysis
- Matplotlib and Seaborn for Visualization
- Statistical concepts for Data Science
- Data Pre-processing Techniques
- Supervised and Unsupervised Machine Learning
- Perceptrons and Neural Networks
- Natural Language Processing
You'll be able to follow along with my processes and bite-sized explanations as I'll be sharing code snippets from practice projects.
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I hope you do stick around.
Happy learning!
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