Hey Guys, this article is all about Data Science. You will get to know what is data science, what you have to learn to be a data scientist, what skills do you need, the job opportunities, and how you can become one. It will help you decide whether the Data Science field is for you or not.
So, without wasting any time, let’s dive in.
What is Data Science?
Data Science, in simple words, is a study of data. You use various tools and technologies to generate useful insights and information from the raw data. Though, it is not as simple as it sounds. To become a data scientist, you need to know your way around mathematics, programming, statistics, and patience.
What does a Data Scientist Do?
A data scientist has to identify issues use data to solve them to help the company in decision making. They typically analyze, process, and model data to interpret the results.
As a data scientist, you have to gain insights into the data, and for that, you may have to build algorithms, do statistical analysis, data mining, and data visualization so that you will be able to create solutions to increase business performance.
As a data scientist you will be making programs that include creating various machine learning-based tools or processes within the company, such as suggestion engines or lead scoring systems. You should also be able to perform statistical analysis.
The Path to Data Science
There are various things that you need to learn to become a data scientist.
Programming Languages
First, you need to learn Programming Language to communicate with the computer.
Python, R, and SQL are mainly used in Data Science. You can also use other languages for this, but these are widely used languages because they have a large number of libraries especially for this purpose. Like for python, there’s NumPy, Pandas, Matplotlib, Seaborn, SciPy, SkLearn, etc. These libraries make the job ten times easier. There’s a library for python for everything you need. You need graphs to visualize data? There’s matplotlib and seaborn. You need DataFrames, there’s Pandas. You need to perform vectorized operations on a big data set really fast? There’s NumPy for that.
The same is the case with R. With other languages, there are not many libraries and hence, Python and R are preferred over other languages.
We also have free python tutorials. Learn Python
Collecting Data
To do anything with data first you need to have data.
Data can be obtained through various sources. You will have to find them yourself but gathering and converting that raw data into useful data is something that you need to need to learn how to do. Sometimes, data is directly provided to you by the company/client you are working for, but most of the times you have to gather the data yourself.
There are some python libraries which can make the process easier. For example, suppose a website is providing public data in json format and you need that data to do work, now to use that data you first need to convert that data into the format that can be used for manipulation. To do that you need requests library. It helps you read and work with Json Data.
Similarly, there are cases where the website isn’t just giving out the data for free and you have to fetch the data, this is the part where web scraping comes into play. For example, suppose you have to fetch articles from Wikipedia, now Wikipedia doesn’t just give data in JSON format or RSS feeds, so to read the data, you need libraries like Scrapy, Beautiful Soup, etc. Learning how to automate the browser can also be handy to fully automate the process of fetching and converting the data for future uses.
Data Analysis and Visualization
Once you have the data you can start analyzing the data. Convert the raw data into dataframes using Pandas library.
Use libraries like Matplotlib and Seaborn to draw graphs. Make heatmaps, bar graphs, scatter plots. These graphs will help you understand the relationships in the data. What values are relevant, what values are irrelevant, what values can help related to the output you need. There are also tools like Power Bi by Microsoft and Tableau which can make the data visualization process easier.
You can check out these articles on NumPy and Pandas to get started with Data Science.
Python: Beginners Guide to NumPy Arrays
Python Pandas: Overview of DataFrames and Series
Machine Learning
Data Science doesn’t involve hardcore machine learning. That’s the job of a machine learning engineer. But you will be expected to know the basics and be perfect with algorithms. Having a great understanding of Neural Networks is, however, a plus point.
You have the data and you have already analyzed it. Now you need to make models that can be used to make predictions or perform particular tasks based on the data. All this comes under the machine learning territory. In most cases, as a data scientist, you will be required to make models that can predict some values based on the previous data that you provided. For that, you need to learn how to identify the type of problem(what type of problem you are solving) and the working of various algorithms. There are numerous algorithms that can be used to solve a particular problem but you need to choose the best one and for that, you need to be 100% sure about the type of problem and the category to which it belongs.
There’s no particular algorithm that works for all types of problems. An algorithm that works well with one dataset may not work with other datasets even if they are of the same category. Now I can go on and on about Machine Learning and algorithms but the post is not about that. We will go into detail when we talk exclusively about machine learning.
Mathematics
Yes, you need mathematics to become a full-fledged data scientist. Some people may argue that you don’t need mathematics to be a Data Scientist. It’s halfway true. You don’t need mathematics at the start of your career but you will need it as you grow. Simply put, to just learn and apply things, you don’t need mathematics but to understand the data on a deeper level you need to know mathematics. It will help you understand the relationship between the variables, whether they are linear or not, whether they will affect the outcome or not.
You need to be good in Statistics and Multivariate Calculus & Linear Algebra.
Mathematics allows you to understand what is happening with your data once you apply an algorithm. It can help you understand the working of the algorithm better and make the algorithm selection process easier.
Career Opportunities in Data Science
The world is generating 2.5 quintillion bytes of data everyday! This simply means that the need of data scientists is only going to increase in future.
There’s a requirement of Data Scientist in almost every industry.
According to Glassdoor, a Data Scientist can make anywhere around $113,000/Year in US or ₹985K/Year in India on an average which is still one of the highest paying career paths.
Data Scientist isn’t the only job role in the data science industry. Here are some of the most popular data science job titles along with their average salary (According to Indeed and Glassdoor).
- Analytics Manager – $118,072
- Data Architect – $108,278
- Data Engineer – $102,864
- Research Analyst – $83,713
- Research Scientist – $98,933
Final Thoughts
Data Science is not only the most lucrative career but also an interesting one to go after. As a data scientist, you will be able to experiment with data and machine learning techniques. It is really a fun job which also requires critical thinking skills and a business mind. Yes, it requires you to have certain skills like statistics, calculus, machine learning, etc, that may seem like a daunting task but once you perfect them, it is one of the best career paths you can take.
I guess this is at least enough to help you decide whether data science is for you or not.
