To become a data scientist, you need to be skilled in statistical analysis and computing, processing large data sets, and mining for beginners. You also need to have a robust educational background, such as having a Master's degree in computer science, statistics, or engineering. This provides a good foundation for beginners to excel in this field.
What is Data Science in Simple Words?
Data science combines various tools, machine learning principles, and algorithms to discover underlying patterns from the raw data. You seek to attach meaning to the raw data at hand by establishing trends and correlations.
It differs from k in that the statistician in data analysis seeks to explain the data by processing it. In contrast, as a data scientist, you go beyond explanation and do the exploratory analysis to gain latent insights into the raw data.
You can identify future occurrences using a combination of advanced algorithms as provided by your computer. You can also make decisions and predictions by using predictive casual analytics, machine learning, and prescriptive analytics.
Predictive Casual Analytics
This model is used to predict the chances of a particular event occurring in the future. You can build the predictive casual analysis model using current or historical data to predict future probabilities. For instance, while evaluating a customer’s creditworthiness, you can deploy this model to determine the likelihood of future payments for that customer based on the available data.
This model has the capability and intelligence to make its own decisions and adapt to dynamic parameters. The model gives you a range of options. It predicts and provides you with advice. Google’s self-driving car uses the data collected by the vehicle to train the self-driving vehicles.
Machine Learning for Making Predictions
Machine learning for making predictions model is deployed when you want to determine future trends using algorithms. You use your data to train your machines. The model can be used in fraud detection, where you use historical data of fraud to have supervised learning of your devices to detect future fraud.
Machine Learning for Pattern Discovery
You can make predictions based on known patterns inherent in the data available to you. Clustering is the most commonly used algorithm for pattern discovery.
What is Data Science Used For?
Data scientists seek to read the future based on the data available currently. They begin with the big data, based on the three V’s; variety, velocity, and volume. They then use the data as raw material for models and algorithms.
No single textbook can summarize the transformation that the emergence of this field has occasioned. These changes cut across almost all industries. You can use the data you currently possess to predict future trends and possibilities across nearly all areas with precision.
Discussed below are areas where this science has been applied.
It debuted in the healthcare sector in 2008 when Google came up with a model that could map flu outbreaks in real-time by tracking location data on searches related to flu.
A tool such as LYNA (Lymph Node Assistant) has been developed through data science to identify breast cancer tumors. This aids early diagnosis of cancer that is difficult for the human eye to detect at an early stage. LYNA has had a success rate of 99% in identifying metastatic cancer.
Road transport burns billions of gallons of gasoline each year, contributing to climate change. Data science can optimize road routes through data-driven route adjustments. The cumulative effect of such adjustment can help save millions of gallons of gas and reduce the damage we are causing to our environment.
Data science has been used in sports, especially when a team seeks to recruit quality players at low budgets. Sports managers have resorted to using in-game statistics that other well-funded clubs ignore to predict the potential of the payer. Such teams can assemble a strong team cheaply. Oakland Athletics and Liverpool are documented to have used the technology.
Governments worldwide utilize available data-warehouses such as Google whenever they need, for instance, to get the list of active devices at a crime scene. Another area that the government uses data science is crime prediction. ICE has used facial recognition in ID databases to flush out undocumented immigrants, and IRS has upgraded its fraud detection model to curb tax evasion.
Using data science, online retailers can tailor their websites based on viewers or target customer profiles. They can have precise targeting.
Is Data Science a Good Career?
A career in data science promises you high pay and an excellent opportunity for growth. Glassdoor classified data science as the third most preferred career in America, with a data scientist earning an average salary of $108,000 against the Bureau of Labor Statistics (BLS) average of all American workers at $49,800.
LinkedIn in 2019 ranked a career in data science as one of the most promising in America. They reported a 56% jump in the job openings in this field. As a rapidly developing industry, becoming a data scientist is now easy. You can download any ebook that you may require for refer on the internet.
As increased globalization continues, demand for processed information continues to rise exponentially. Universities have become virtual, replacing a textbook with an ebook in e-learning. Data science and scientists have a crucial role in the technological dynamics playing out in the world arena.