A Complete Guide to Become a Data Scientist
November 28, 2017
Data scientists are often called “big data wranglers.” Being data scientists, they deal with messy data points (unstructured and structured) using their skills in math, programming, and statistics to clean, manage and organize them. Then they unveil hidden solutions to business challenges by applying their analytical powers that include knowledge of the industry, contextual understanding, and skepticism of present assumptions.
According to the definition, “a data scientist is a person who is better at statistics when compared to a software engineer and better at software engineering when compared to a statistician.”
Eight Skills to Get You Hired
This post covers the core set of Data Science competencies you need to develop to be a data scientist:
Basic Tools: Tools and Technology
The demand for data scientists is increasing, and hopefuls must know how to use the tools of the trade. A data scientist should know statistical programming languages like R or Python and SQL. Also, a certification in R language or python increases their chance at getting hired; quite obvious due to the fact that most Data Science training comes with mastery of R or Python.
Knowledge of Statistics
You need to know the statistics and math when working in data science, but you may feel obscured by the fact that you are just using functions and writing code. The more you will understand that underlying process, the better and faster you will be at coding. You should be skilled enough to figure out the statistically significant variations to make bigger assumptions and conclusions that can help your organization to make better decisions. With your continuous approach to learning, you will be able to derive accurate results from a given dataset.
Data Science includes both practical and theoretical skills. No organization would like to hire a data scientist without any idea of how kernel methods function or what dimensionality is to be implementing support vector machines and expecting logical interpretation of results from them. But, at the same time, the requirement for someone who can simplify these concepts along with detailed explanations ad nauseam but the inability to implement an SVM classifier is very low. At that time, learning of implementations depending upon environments becomes necessary.
For any data scientist, data is the main product that includes sales numbers and user figures that a tech product generates in bulk. So, your programming skills should be strong enough to develop programs that can process large volumes of data in a short time and translate the data into actionable insights.
Contrary to popular notion, only a few data science jobs are purely related to data, unless you are in research. A data scientist is a full-time job, and you are responsible for data gathering, modeling, and developing applications to display data. When it comes to pulling data on your own from different sources, you can not rely on an engineer to retrieve it as everything from analysis to building predictive models will be your responsibility.
Try to build your Python skills if you have just started out as it has great libraries for data management and you will need it to implement out of the box machine learning algorithms.
Data Visualization and Communication
To make data-driven decisions, particularly for startups, data visualization and communication are really important. Data visualization means developing useful insights from data that prompt to take actions and communication means explaining the findings to others. The importance of Data Visualization today is further highlighted by the attention Tableau, one of the leading Data Visualization companies, has given to this field.
You need to have familiarity with data visualization tools like d3.jsa and Ggplot. It cannot be simply basic knowledge, but also the complete familiarity with the principles of visually encoding data and its communication.
Here, hacking does not mean breaking into computers. This refers to the tech programming culture of hacking that requires ingenuity in using technical skills and creativity to solve problems. Yes, hacking is important because data scientists use technology to wrangle large amounts of datasets and work with complex algorithms, and it requires more sophisticated tools than Excel.
You need to have proficiency in coding-prototype quick solutions and integrate with complex data systems. Core languages that data science include SQL, R, Python, and SAS, and getting certified in SAS is particularly helpful as it is used by most industries. Others on the periphery are Scala, Java, Julia, and others. Apart from familiarity with language fundamentals, a data scientist is a technical expert with an ability to navigate their way through technical challenges.
A data scientist is also a tactical business consultant. As data scientists work closely with data most of the time, they are positioned to gather, understand, manage and reshape data in ways no one else can. This puts the responsibility to translate observations to shared knowledge and helps in developing strategies to solve business problems.
One of the important takeaways of this article is that it is important for data scientists to learn new skills and constantly upgrade their portfolio. As long as they do this, getting a job in this dynamic market should not be too hard.
Keep Up with Trends and Tools in Data Science
Data science is a fast-evolving and a highly technical field. From programming languages to new algorithm methods and applications to other tools, it has many things to learn. If you are an aspiring data scientist, then keep your skills sharp so that you can better serve your employees and resumes well.
With the following ways, you can go about it:
- Read publications related to data science industry publications; keep your focus toward recently launched products, employer surveys, along with annual lists of the in-demand skills.
- Try to get in touch with well-known data scientists and subscribe their blogs.
- Get your hands on the latest research and studies by reviewing academic journals.
- Join a professional community that promotes networking among data scientists.
- You can even invest in new professional certifications.
- Consider postsecondary certificates.
(This is a guest article from Danish Wadhwa.)