Becoming a data scientist does not necessarily require a master’s degree. There is a significant shortage of data scientists, and some employers are comfortable hiring people who lack a degree, but have the experience needed.
The majority of employed data scientists have a master’s degree, but over 25% do not. If you have the experience, a degree is not an absolute necessity to become employed as a data scientist. (If you are genuinely good at statistics, this may be a job for you. If you are not, by nature, good at statistics, this is probably not a job for you.)
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Data scientists process large amounts of data, often with the goal of increasing a business’ profits. Ideally, a data scientist has a strong understanding of statistics and statistical reasoning, computer languages, and business. They process and analyze large amounts of data to provide useful, meaningful information to their employers.
These interpretations are used for decision-making. To provide this information, data scientists often work with messy, unstructured data, coming from emails, social media, and smart devices. Primarily, they work with big data, gathering and analyzing large amounts of unstructured and structured data.
Data can be considered raw information, with data scientists using a combination of computer algorithms and statistical formulas to find trends and patterns within the data. Then they interpret those patterns and apply them to real-world situations.
There are many, many statistical techniques available, and a data scientist must research and find the most appropriate statistical formulas for the situation. Listed below are some very basic statistical techniques, which a data scientist should understand, and which provide a foundation of understanding for other statistical techniques:
- Basic Statistics: The most basic concepts in statistics for Data Science include probability, variability, central tendency, and probability distribution.
- Probability Distribution: This gives the probability of one result occurring out of a range of possible outcomes. Weather predictions provide a good example of probability distributions, for example, a calculation of the chance it will rain over the next three days.
- Dimension Reduction: It can reduce the amount of random variables through “feature selection” and “feature extraction.” This process simplifies data models and will streamline the process of working with algorithms.
- Over and Under Sampling: Sampling techniques are used when there is too much data being used for classification purposes. Data mining algorithms often have limitations on how much data they can analyze.
- Bayesian Statistics: A technique which assigns “degrees of belief,” also known as Bayesian probabilities, to statistical models. Probabilities are calculated by including the “reasonable expectation” of an event occurring, which will influence circumstances and/or people’s behavior. For example, predictions of whether or not at least 150 customers will visit a restaurant each Sunday, over the next six months, would be influenced by a nearby Sunday art show starting in a few weeks. Including this information with historical averages would be a form of Bayesian statistics.
There are a large variety of programming languages useful for Data Science. Programming languages are formal languages made up of instructions that produce various kinds of output from a computer. They are used in computer programs to carry out algorithms. A data scientist should have learned and mastered at least one programming language — mastering two or three would be even better.
It is considered by many to be the most popular Data Science programming language used today. Python is a general-purpose language that is object-oriented and easy-to-use. It is an open-source language, and began being used in 1991.
Python supports multiple paradigms, ranging from structured to procedural to functional programming. It is more scalable than many languages and has a huge variety of Data Science libraries available for use.
Because Python is open-source, it comes with a fair amount of support from enthusiasts and continues to evolve. It is easy to learn, and Python experience is in high demand. (Python is named after the British “Monty Python” comedy troupe.)
Python can be used for a large variety of applications, such as machine learning, artificial intelligence, and financial services. A variety of websites such as Google, Instagram, Pinterest, and Netflix use Python. (Python does not work well for developing mobile applications.)
This programming language is extremely popular for building interactive websites. It is an object-oriented programming language popular with data scientists, and is also used in developing mobile applications.
It is an open-source programming language developed by statisticians. R is typically used for graphics and statistical computing, but it also comes with several Data Science applications and multiple useful libraries. R can be used to research data and conduct data analyses, as needed. This language is, however, more complex, and harder to learn than Python.
R is used heavily for statistical analytics, as well as machine learning. This language runs on many operating systems and is extensible. Many large companies have adopted R to analyze massive data sets. Programmers who know R are in great demand.
This programming language was developed in 2003, and was originally designed to resolve problems with Java. It has applications that range from machine learning to web programming, and is good for working with big data research, in part because it is scalable. Scala supports both object-oriented and functional programming.
Structured Query Language is a very popular programming language for managing data and is commonly used by a variety of businesses. SQL tables and queries are helpful for data scientists when working with Database Management systems. This language is extremely useful when storing, retrieving, and working with data in relational databases.
Business and Data Science
Future Market Trends: Collecting and analyzing massive amounts of data can help in identifying emerging market trends. Researching search engine queries, following celebrities and influencers, and tracking purchase data can reveal the products people will be interested in.
For example, the trend of clothing upcycling has been rising as a way for the environmentally conscious to replace their clothing. The clothing retailer Patagonia, who has used recycled plastic since 1993, realized this emerging trend and launched Worn Wear, a website that is designed specifically to help customers upcycle their used Patagonia products.
Customer Insights: Data about a company’s customers can reveal information about their preferences, habits, demographic characteristics, and aspirations. For instance, a customer’s data can be gathered each time they visit the company’s website (or brick-and-mortar store).
Whenever a customer completes a purchase, adds an item to their shopping cart, or opens an email from the company, that data can be recorded for future evaluation (or real-time evaluations). After ensuring the data is accurate, the data can be combined in a process that is called data wrangling. By combining the data, conclusions can be drawn that (hopefully) will identify trends in customers’ behavior.
Internal Finances: A business’ financial team can use Data Science for creating reports, analyzing financial trends, and generating forecasts. Data on a business’ assets, cash flows, and debts are collected constantly, allowing financial analysts to algorithmically (or manually) find trends regarding financial growth or decline. Additionally, a risk management analysis can determine whether or not certain business decisions are a good idea, or potentially damaging.
Streamlining Manufacturing: Data Science can be used to locate and identify conflicts and slowdowns in the manufacturing process. Sensors on manufacturing equipment can gather data from the production process.
In situations when the data collected is so massive a human cannot be expected to manually analyze it, algorithms can be created to clean and sort the data quickly and efficiently to provide insights into streamlining the manufacturing process.
Increasing Security: Data Science can also be used to increase a business’ security and protect its sensitive information. For instance, many banks use complicated machine-learning algorithms for detecting fraud because of deviations from a user’s normal behavior. These algorithms catch fraud much faster and more accurately than a human is able to.
Free Data Science Courses
Class Central has provided a list of free Data Science courses (789) from a variety of sources, ranging from John Hopkins offering a course in R programming to the University of Illinois offering a course titled “Pattern Discovery in Data Mining.”
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