Data Science Can Be Fun

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Blog: A blog about the power of data and all the fun things you can do with it.

Data Science Can Be Fun: A blog about the power of data and all the fun things you can do with it.

Machine learning & Data Science is definitely changing the world in ways that are beyond our wildest dreams. Take a glance around, and you will see that you are immersed in the realm of data science. Take Alexa, for example, a wonderfully designed user-friendly AI by none other than Amazon, there are more such AIs like Google Assistant, Cortana, etc.

So, how were they created, and most importantly, why were they produced in the first place?

Many people believe that data science is simply a superset of machine learning. Those folks are partially accurate, since data science is nothing more than a massive amount of data that is then subjected to machine learning algorithms, methodologies, and technologies.

Machine learning provides strategies to extract data, then appends multiple methods to learn from the acquired data, and finally predicts future patterns from the data using well-defined algorithms.

The Data Science Process consists of the following steps: Discovery, Data Preparation, Model Planning, Model Building, Operationalize, and Communicate Results.

Google is the ultimate machine learning example because GOOGLE monitors the number of searches you have performed and then offers comparable searches when you google anything in the future. Similarly, AMAZON suggests products based on past searches, as does NETFLIX, which makes recommendations based on TV shows or movies you’ve viewed.

It is no longer a secret that the Machine Learning domain is expanding quickly throughout the world, therefore if you want to pursue a career in this sector, there are a few abilities you must master.

Good understanding of computer fundamentals.
Programming abilities that are well-honed.
A solid understanding of probability and statistics.
You will also need to enhance your Data Modelling skills.

What Is the Role of a Data Scientist?

The majority of data scientists in the business have advanced degrees and training in statistics, mathematics, and computer science. Their knowledge covers a wide range of topics, including data visualisation, data mining, and information management. Previous experience in infrastructure architecture, cloud computing, and data warehousing are quite prevalent.

Important Data Scientist job roles are:

Data Scientist
Data Engineer
Data Analyst
Statistician
Data Architect
Data Admin
Business Analyst
Data/Analytics Manager

Python 3:

Python is a programming language widely used by Data Scientists.

Why you might ask?

Python is a beginner’s language that is interpreted, interactive, and object-oriented. It’s simple to learn, read, and keep up with. It has all of the newest sophisticated programming language features, such as portability, extendibility, scalability etc.

Python has in-built mathematical libraries and functions, making it easier to calculate mathematical problems and to perform data analysis.

Python 3.0 (also known as “Python 3000” or “Py3k”) is a new version of the programming language that is incompatible with previous versions of the language. The language is basically the same, but many aspects have changed significantly, particularly how built-in objects like dictionaries and string functions, and many deprecated features have finally been eliminated. The standard library has also been restructured in a few key areas.

Python is compatible with a wide range of operating systems, including Windows, Linux, and macOS. The installation process is simple (for Windows):

Go to Python’s Windows Download page. (https://www.python.org/downloads/)
Look for the link to the Latest Python 3 Release.
You’ll discover links to numerous operating systems and their versions under the Files section.
Choose between the 64-bit Windows x86-64 executable installer and the 32-bit Windows x86 executable installer.
All you have to do now is run the installer after downloading it. Make sure to Add a Path.

Let’s have a look at some Python libraries for data scientists:

Data Cleaning and Data Manipulation

Pandas
NumPy
Spacy
SciPy

Data Gathering

Beautiful Soap
Scrapy
Selenium

Data Visualisation

Matplotlib
Seaborn
Bokeh
Plotly

Data Modelling

Scikit-Learn
PyTorch
TensorFlow
Theano

Image Processing

Scikit-Image
Pillow
OpenCV

Audio Processing

pyAudioAnalysis
Librosa
Madtom

Takeaway-

Python is an excellent choice for data science and machine learning positions. It’s simple to learn and master, and it’s utilised by thousands of businesses across the world.
Python offers a plethora of libraries that meet the needs of every discipline. It has a number of libraries, each of which focuses on a specific subject.
These python libraries for data scientists are highly beneficial since they help in decision making. The collection of libraries is powerful enough to operate with enormous amounts of data.

The Top Platforms for Data Science and Machine Learning –

A good data science and machine-learning platform should provide data scientists with all of the assistance they require while doing data and analytics activities. Visualization, interactive exploration, deployment, performance engineering data preparation, and data access are all part of these activities.

It helps data scientists to develop strategy, find meaningful insights from data, and disseminate those ideas throughout an enterprise from a single platform. That’s why it is important to have a centralised location where data science teams can collaborate on projects.

Here are a few good platforms for Data Science and Machine learning:

Alteryx Analytics
ai
KNIME Analytics Platform
Weka
Apache Spark
BigML
Excel
RapidMiner
SAS
MathWorks’ MATLAB and Simulink
TIBCO Software
Databricks Unified Analytics Platform
Domino Data Science Platform
Microsoft’s Azure Machine-learning Studio

Some applications of data science:

1.Digital Advertisements (Targeted Advertising and re-targeting): From display banners on various websites to digital billboards at airports, virtually all of them are determined by data science algorithms. This is why digital commercials have a far greater CTR than conventional ones. They might be targeted based on the user’s previous behaviour.

2.Recommender Systems: This technology is used by Amazon, Twitter, Google Play, Netflix, Linkedin, imDb, and many more companies to improve user experience. The recommendations are based on a user’s prior search results. They assist you in locating relevant goods among the billions of products available to you.

3.Image Recognition: You share a photograph with your pals on Facebook, and you begin receiving recommendations to tag your friends. Face recognition method is used in this automated tag recommendation function. Similarly, while using WhatsApp Web, you scan a barcode on your web browser using your cell phone.

4.Speech Recognition: Google Voice, Siri, and Cortana are some of the greatest examples of voice recognition software. Even if you are unable to compose a message, your life will not come to a halt if you use the speech recognition option. Simply say the message aloud, and it will be turned to text.

Conclusion:

Data science may offer value to any organisation that can effectively utilise its data. Data science is essential to every firm in any industry, from statistics and insights throughout processes and recruiting new applicants to assist senior employees in making better-informed decisions.

If you come from a technical background and have a knack for data, Data Science is your real calling. What’s the best part? There is so much to learn and discover in and around data science. It’s an umbrella word for a variety of tools and technologies, understanding any of which will make you a valuable asset in the ever-expanding industry of Data Science.

The Best Way to get into Machine learning and Data science is through Courses + Hands-on Practice

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