Data science is among the most demanded careers in 2020. In the tech world, everyone has questions that must be answered by data. From businesses to government institutions, there is a vast amount of information that can be analyzed, interpreted, and applied for a wide range of purposes.
Because there is much information for the average person to process and use, data scientists¹ are trained to gather, organize, and analyze data, helping people from every corner of industry.
Data scientists come from a wide range of educational backgrounds, but the majority of them will have technical schooling of some kind. Data science degrees include a wide range of computer-related majors, but it could also include areas of math and #statistics. Training in business is also common, which bolsters more accurate conclusions in their work.
While you may have the skills needed to become a data scientist straight out of college, it is not uncommon for people to need some on the job training before they are off and running in their careers. This training is often centered around the company’s specific programs² and internal system, but it may include advanced analytics techniques that are not taught in college.
The world of #datascience is an always-changing area, so people working in this field need to constantly update their skills. They are continually training to stay at the leading edge of information and technology.
Data science is needed by nearly every business organization, and agency in the country and across the globe, so there is certainly the chance for specialization. Many data scientists will be heavily specialized in business, often specific segments of the economy or business-related fields like marketing or pricing.
For example, a data scientist may specialize in helping car dealerships analyze their customer information³ and create effective marketing campaigns. Another data scientist may help large retail chains determine the perfect price range for their products. Some data scientist work for the Defense Department, specializing in the analysis of threat levels, while other specialize in helping small startup businesses find and retain customers.
Main Roles of a Data Scientist
Data Scientists use mathematics, programming tools and techniques, software, and statistical methods to derive insights from data.
In interviews with several Data Scientists, some of the things they reported doing day-to-day included:
-Extracting salary figures from job announcements, storing, and analyzing them
-Simulating the spread of an epidemic
-Leveraging industrial psychology to create better HR models
-Dissecting data to obtain risk groups for low-socioeconomic status students
-Using data, models and analytics to make decisions on how to sell products more effectively
Data Scientists vs. Statisticians
Statisticians have worked in all sorts of industries for many years, while #datascientists are primarily found in the tech industry⁴ or in companies with a well-developed IT component.
The prevalence of data scientists in the tech industry is likely due to the ability of tech companies to collect, store, and make sense of huge volumes of data; a capability that many traditional companies have not yet been able to master. While it may be true that data scientists and statisticians often do similar kinds of work, data scientists, receive much higher financial compensation for doing so.
The Skills You Should Learn
Let us explore the core skills you should learn as you begin your data science journey
The amount of mathematical skill required to be an effective Data Scientist is hotly debated. Some argue that deep mathematical knowledge⁵ is required, while others argue that since most statistical analyses are carried out via programming libraries like NumPy anyway, math knowledge is less important than you would think.
DataScienceWeekly offers this list of the minimum mathematical concepts you should be comfortable with in order to be a successful Data Scientist:
-Linear algebra, including multivariate calculus. You can learn Linear Algebra for free at Khan Academy.
-Regression, including the ability to handle both linear and nonlinear models appropriately. You can learn about Linear Regression at Coursera.
-Probability theory, including Bayes’ Law and Central Limit Theorem. You can learn about probability and data at Coursera.
-Numerical analysis, including time series analysis and forecasting. You can learn about time series forecasting at Udacity.
-Core machine learning methods, including clustering, decision trees, and k-NN. You can learn about #machinelearning free via Stanford University’s course on Coursera.
2. Programming Tools and Techniques
The ability to program helps data scientists in a variety of ways. They can write scripts to automate one of the most time-consuming tasks in data science: cleaning and preparing data for analysis. They can write scripts to transform data from one format to another, such as transforming the result of an SQL query into a neatly formatted CSV report, or the opposite, persisting CSV data to a relational database.
In most cases, data analysis is carried out using purpose-built libraries that abstract away many of the repetitive or complex calculations involved, such as pandas. Matplotlib can be used to visualize the results of a data analysis. Being comfortable with both R and Python⁶ is ideal, as each language and its associated ecosystem of libraries have different strengths and weaknesses.
3. Machine learning
Machine learning is finding increasing application in the world of data science. Machine learning is the means by which computers can learn tasks without being explicitly programmed. Machine learning techniques can be used to make decisions and predictions based on data, and has many applications in the field of data science.
You could use machine learning to tackle the same problem by using records of both fraudulent and non-fraudulent transactions as training data to build a model. Using this model, the algorithm⁷ can identify patterns in fraudulent transactions that might be more nuanced and complex than human pattern matching could identify.
A machine-learning #algorithm might detect patterns in variables that a human could miss, such as the time of day when fraudulent transactions are most likely to occur. Most powerfully, the algorithm can be adapted to rapidly predict whether an incoming transaction is likely to be fraudulent. Your software engineering team could use this prediction to handle the transaction accordingly, by freezing it and flagging it for review.
Structured Query Language, is a language used for interacting with relational databases. Worldwide, the majority of data is stored in relational databases. To work with this data, you need to be able to query the database to extract the data you need. This is why understanding the fundamentals of SQL⁸ is essential as a Data Scientist.
Software packages used by Data Scientists include Tableau, Microsoft Excel, RapidMiner, and KNIME. You may be surprised to see Excel on this list, but CSV reports are sometimes the only common language between Data Scientists and business at large. If you are trying to become a Data Scientist, the only software package that you must be comfortable with is Excel. This is simply because it is guaranteed to be used at any given company you might apply to, while other software packages, such as Tableau and RapidMiner, may not be.
6. Statistical Methods
A strong understanding of statistics is probably the most important skillset for Data Scientists. Simply put, all of the #programming, mathematical, and software skills in the world will not help you if you do not understand how to analyze and report on statistics accurately and fairly.
Join Data Science Communities
You will need to network and make connections within the data science community, regardless of whether that is at a local Meetup occasion or a bigger gathering like O’Reilly Strata. It is significant that you begin networking and becoming acquainted with what opportunities lie in data science, and it is imperative to begin discovering individuals you can team up with and learn from.
You will need to begin building relationships with individuals at recruiting organizations or who have data science needs: you may even consider freelancing as a data scientist if you can come up with projects at an expert level.
The Good and Bad Side of Data Science
There are many benefits to becoming a data scientist, and it does not all center around pay. The job is a unique yet challenging career that offers a wide variety of daily tasks, and this variety is often cited as one of the main benefits.
As a data scientist, you may work for a wide variety of companies, coming up with solutions and information related to customer retainment, marketing, and new products. This means you get to engage in unique and interesting topics and subjects that give you a wide perspective on the economy and world at large.
Just like any career, there are some clear drawbacks. While the extreme variety of subjects gives you new challenges, it can also mean that you never get to dive into a specific topic.
The technologies that you use will be constantly evolving, so you may find that the systems and software that you just mastered are suddenly obsolete. Before you know it, you need to learn a new system. This can also lead to lots of confusion, as determining which systems are the best for specific jobs is very tough.
Will the Current Data Science Growth Continue?
Anyone working in the field of data science can expect a solid job security. Not only will they earn an income well above the national average, they can also expect their field to continue to grow over the coming decade.
The demand for data scientists is well above national average and 50% higher than that of software engineers and data analysts. The number of data scientists doubled over the last four years and some even quote the growth at 300%.
As more and more businesses rely on hard information for their decisions, the need for people who can not only compile the information, but can organize it, store it, interpret it, and discover trends, will be all the more important. Data collection by businesses will continue to grow, and data analysts should expect to be in high demand for years to come.