“A Data Scientist is one who knows more statistics than a programmer and more programming than a statistician”
Domain + Coding + Statistics = Data Scientist.
Vital Data Science Skills that need to be acquired:-
# Statistics ~
Statistics is a collection of principles and parameters for gaining information in order to make decisions when faced with uncertainty.
The word "Statistics" is derived from the Latin word "status", which refers to information related to a state or a province.
It's actually said to be an ancient technique used by the kings to know the details about their state or province.
So considering traditional statistics, it has three important parameters called Mean, Median and Mode.
Fundamentally, all these three refer to one single feature called the “Central Tendency”.
The idea of central tendency is that there may be one single value that can possibly describe the data to the foremost range
# Programming Languages ~
Programming language is important because it defines the relationship, semantics, and grammar which allows the programmers to effectively communicate with the machines that they program.
Computer programming principles implemented today will likely influence how technologies such as voice-recognition, artificial intelligence, and other sophisticated technologies will change in the future and how they will be applied to our day-to-day lives.
Python ~
Python seems to be the most widely used programming language for data scientists today.
This language allows the integration of SQL, TensorFlow, and many other useful functions and libraries for data science and machine learning
# Data Extraction and processing ~
Data extraction is where data is analyzed and slithered through to get back relevant information from data sources (like a database) in a specific pattern.
Further data processing is done, which involves adding metadata and other data integration; another process in the data workflow.
The majority of data extraction comes from unstructured data sources and different data formats.
This unstructured data can be in any form, such as tables, indexes, and analytics.
# Data Wrangling ~
Data Wrangling is the process of converting and mapping data from its raw form to another format with the purpose of making it more valuable and appropriate for advanced tasks such as Data Analytics and Machine Learning.
It should provide precise and actionable data to Business Analysts in a timely matter.
# Data Exploration ~
Data exploration is the first step in data analysis and typically involves summarizing the main characteristics of a data set, including its size, accuracy, initial patterns in the data and other attributes.
It is commonly conducted by data analysts using visual analytics tools, but it can also be done in more advanced statistical software, such as R.
Data analyst uses visual exploration to understand what is in a dataset and the characteristics of the data.
# Data Visualisation ~
Data visualization is the act of taking information (data) and placing it into a visual context, such as a map or graph.
Data visualizations make big and small data easier for the human brain to understand, and visualization also makes it easier to detect patterns, trends, and outliers in groups of data.
When data scientists are in the midst of a complex project, they need a way to understand the data that’s being collected so that they can monitor and tweak their process to ensure it’s performing the way it should.
Data visualization is truly important for any career; from teachers trying to make sense of student test results to computer scientists trying to develop the next big thing in artificial intelligence, it’s hard to imagine a field where people don’t need to better understand data.
# Machine learning ~
Machine learning is a form of artificial intelligence that allows computer systems to learn from examples, data, and experience.
Through enabling computers to perform specific tasks intelligently, machine learning systems can carry out complex processes by learning from data, rather than following pre-programmed rules.
Data is the fuel for machine learning. It is the raw material from which machines can make their recommendations and predictions.
# BigData processing frameworks ~
The evolution of big data impacts every business and customer. By 2020, we are expected to have over 44 trillion gigabytes of information in the digital universe.
Information is ballooning to incredible volumes, and to be useful to business owners, it must be transformed into something meaningful. Storage is not enough.
Business leaders who use data must be able to harness it in innovative ways to create unique insights.
In order to utilize this data, automation and artificial intelligence have thus become irreplaceable parts of every business.
They must have the power to process, mine, and create data in a way that informs business leaders and researchers.
Data processing frameworks are a necessity, with Hadoop, Spark, and other solutions providing much-needed personalization.
Many are open source and in a constant state of evolution.
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