Ever heard of the idiom “Finding a needle in a haystack”? Would you find that needle if you were asked to? You’d straight away say “No way!”. The reason obviously being that it’s too much to do. But have you ever thought about how difficult it would be to extract some information out of 2.5 quintillion bytes of data? If you’re wondering, that’s the amount of data generated every day. That’s 1 followed by 18 zeros! Sounds scary? Well, thanks to Data Science for making it easy. Over the years, Data Science has helped various industries in making sense of raw data and helping their business. And Python programming has played an amazing role in reinforcing Data Science. So, in this blog, we will talk about how Data Science with Python has and will change the data industry.
Why Get into Data Science with Python?
Data Science is a domain that helps you in extracting useful data out of raw information. It’s a combination of scientific processes, algorithms, and tools. With so much data generated every day, not all of it is useful. But not all of it is useless either. You have to decide what data is going to be useful for you and how to extract it. And obviously, manually going through and segregating data is an impossible task. That’s where Data Science comes in. When you use Data Science, you are searching for useful data based on patterns. You can also use advanced Machine Learning algorithms to do this job fast and accurately.
There’s no one right way to getting the best out of Data Science. The programming language, algorithms, and tools you use, the techniques you adopt, all of it matters. And Python has always been one of the favorites of Data Scientists’. Now, let me explain why.
Easy to Learn
Python is known to be one of the easiest programming languages that you can learn. Python’s easy-to-understand English-like syntax makes it easy even for a beginner to learn and understand Python programming. So, if you want to get into Data Science with Python, you don’t have to spend more time learning Python programming. You can rather invest your time in creating impressive algorithms.
Python has a rich source of libraries to scale your applications. Libraries like Collections, MemCache, etc., help in handling and caching data when used cleverly. To give you an example, Instagram is built mostly using Python. Hundreds of thousands of pictures are uploaded on Instagram every day and you can see how well it is working. So, you can say that Python is quite scalable.
Active Community and Immense Resources
No matter what you are learning, you will always come to a point where you get stuck. It’s the same with Python. But Python documentation is so detailed and extensive that it’s very easy for you to find out a solution. Python’s community is known to be one of the most active communities in the IT world. Because of its open-source nature, you’ll find a lot of updates and bug fixes almost as soon as there’s a release. And even if you are stuck with a custom problem, no worries! You’ll find a good number of active forums that’ll help you with a solution in no time.
One of the most important reasons why Data Science with Python programming is preferred is that Python has a library for almost everything you would need. Do you want to deal with huge data, visualize data, play around with data types, or integrate with other tools? Python has got it covered. And it’s not just limited to this. Python provides such a huge collection of libraries, that you can do almost all the tasks using Python. This is really helpful because you can use readily available methods to get your job done and don’t have to build it from scratch.
When you build your application, you can limit the platform on which it has to be used. Your customers can be using different Operating Systems and different devices. It is important for you to make sure your application runs on most of the platforms. Many languages like C/C++ aren’t easily portable. You will have to make changes to the code before running it on different platforms. But when it comes to Python, you can just write the code once, and it will execute on multiple platforms.
Python works well in small codes. So you can complete large tasks by writing just a few lines of code. Just to give you an example, let’s say you are building a library management project. If you used Java, you might have to write around 400-450 lines of code. But you could build the same project within 150 lines of code using Python. And when you have to write a smaller code, it takes less time for you to develop an application.
Python has gained a lot of interest and demand in recent years. Due to this a lot of tools have started providing integration support for Python. And similarly, you will find a lot of Python libraries for integration with other tools. So, you can use Python to build different kinds of apps and work with different systems. It’s also easy to build APIs in Python which would be really helpful for web integrations.
I hope that’s enough reasons for you to understand that getting into data science with python will make your Data Science journey easy.
When it comes to choosing a programming language for Data Science, there’s a common dilemma. And that’s what we’ll look at now.
Python vs. R for Data Science
“Python or R? Which one should I choose for Data Science?” This is one of the most frequent questions people ask. Both Python and R, are open-source and have a huge collection of libraries for data handling and visualization. They are both good at what they do. The main difference is that R focuses on Statistical analysis of data. But when it comes to Python, it’s more of a general approach. But Data Science is more than analyzing, handling, and visualizing data. Data Science has advanced so much over the years and AI/ML has become a huge part of it. R is good for statisticians and researchers, but Python is a development-ready solution. So, deciding which is better for you completely depends on your scope of work.
How Python programming makes Data Science easy?
I’ll skip the pep talk and jump right into examples to prove how Python programming makes Data Science easy.
import pandas as pd import seaborn as sns # Why sns? It's a reference to The West Wing import matplotlib.pyplot as plt # seaborn is based on matplotlib sns.set(color_codes=True) # adds a nice background to the graphs %matplotlib inline # tells python to actually display the graphs
auto = pd.read_csv('Automobile.csv')
Plotting univariate distributions
The most convenient way to take a quick look at a univariate distribution in seaborn is the distplot() function. By default, this will draw a histogram and fit a kernel density estimate (KDE).
Visualizing pairwise relationships in a dataset
To plot multiple pairwise scatterplots in a dataset, you can use the pairplot() function. This creates a matrix of axes and shows the relationship for each pair of columns in a DataFrame, it also draws the histogram of each variable on the diagonal Axes:
sns.pairplot(auto[['normalized_losses', 'engine_size', 'horsepower']]);
Drawing Boxplots with ease
Another common graph is a boxplot(). This kind of plot shows the three quartile values of the distribution along with extreme values. The “whiskers” extend to points that lie within 1.5 IQRs of the lower and upper quartile, and then observations that fall outside this range are displayed independently.
sns.boxplot(auto['number_of_doors'], auto['horsepower'], hue=auto['fuel_type']);
Drawing multi-panel categorical plots
sns.catplot(x="fuel_type", y = "horsepower", hue="number_of_doors", col="drive_wheels", data=auto, kind="box");
We can plot the mean of a a dataset, separated in categories using the barplot() function. When there are multiple observations in each category, it uses bootstrapping to compute a confidence interval around the estimate and plots that using error bars:
Bar plots start at 0, which can sometimes be practical if zero is a number you want to compare to
sns.barplot(auto['body_style'], auto['horsepower'], hue=auto['fuel_type']);
Isn’t it amazing how just a few lines of code do so much work for you? These are very basic examples using Pandas and Matplotlib libraries. When you have proper data and you have to search for patterns, build algorithms based on your requirement, it gets really interesting. You can also use AI/ML libraries NumPy, SciPy, TensorFlow, etc., to make your code smarter.
A Career in Data Science with Python
Data Science has been one of the top 10 technologies in the past couple of years. With the ever-increasing demand for Data Science, you can say that it’s going to stay on the top for at least the next decade. According to the U.S. Bureau of Labor Statistics, there will be an increase of 27.9% in demand for Data Science jobs. Top companies like Google, Facebook, etc., focus a lot on Data Science and have multiple openings. If you’re someone who likes puzzles and brain games, loves to play with data, or solve complex programs, a career in Data Science is the perfect choice of career.
Now that you are aware of what the future of Data Science looks like, it’s safe to say that this is going to be a secure career path, and hence a smart move. There’s a lot of demand for Data Science professionals and you’d see regular recruitment processes for the same. Despite this fact, the demand is not being fulfilled. One of the reasons why Data Science job positions remain vacant is because people applying for these jobs lack required skills. According to a survey, an average of 35-40% of applicants are under skilled for the current Data Science jobs. This can be advantageous for you if you improve your data science skills. So, where do you start? You master a skill by practice, but you start by learning it. If you’ve read the blog so far, it shows that you’re an aspiring Data Scientist. To get the right start, check out the data science career program by Springboard where you can gain the required skills to excel at that Data Science job.
People also read