From maps that update in real time to shopping apps that know the products we want before we do, innovative companies use technology to make our lives easier. One way they do this is by collecting massive amounts of data on our online actions and spending habits. They can then use this data to make optimal business decisions to further improve their products and services.
At the heart of the process are talented data scientists. They use the latest analytical tools to solve problems and make insightful discoveries. Whether it’s recommending movies based on our viewing history or laser-targeting a certain demographic based on sales figures, data scientists are masters of big data.
The role requires a range of different skills. In this article, we’ll discuss what you need to learn to become a data scientist. We’ll also look at how you can learn these fundamental skills through specialized online programs designed to get you on the right track.
What Does a Data Scientist Do?
Data scientists focus on gathering and analyzing large sets of structured and unstructured data. A data scientist’s role is a combination of various fields including computer science, statistics, and mathematics. First, they process, analyze, and model data. Then, they interpret the results to develop actionable plans for either their company or their clients.
Data scientists are experts in analysis and use their skills in both technology and social science, to break down data and identify trends. Using their industry knowledge, contextual understanding, and skepticism of existing assumptions, they come up with original solutions to business challenges.
They’re responsible for understanding huge chunks of haphazard, unstructured data, from multiple sources. They must cleanse and aggregate data from smart gadgets, social media platforms, and emails, for example, that isn’t already nicely packaged in a database.
Effective data scientists are proficient at identifying relevant questions, gathering data from a wide collection of sources, organizing data, and translating the results into actionable solutions.
They’re also required to communicate their findings to company stakeholders. Since these skills are required in almost all industries, skilled data scientists are becoming increasingly valuable to companies worldwide.
Essential Skills for Data Scientists
Here are some skills you should focus on, to make it in the data science field:
- Statistics: Defined as the study of the collection, analysis, interpretation, presentation, and organization of data. This perfectly aligns with the responsibilities of a data scientist which is why a thorough knowledge of statistics is essential for the role. An understanding of descriptive statistics along with probability theory will help you make better business decisions with the data you’re working on.
- Data Extraction, Transformation, and Loading (ETL): Let’s assume you have multiple data sources such as a MySQL database, a MongoDB, and a Google Analytics report. You first have to extract data from these sources. Then transform it into an appropriate format, or structure, for querying and analysis. Finally, you must load this data into a data warehouse, where you can analyze it. ETL is an important process that needs to be mastered by data scientists.
- Outline Business Targets: It’s the most important step in defining the critical business problem that you want to address. Also known as ‘defining the use case’, it involves bringing together the necessary decision-makers and data scientists to clearly define the objectives of a new project. This approach ensures that the data scientists select the correct method and resources to meet the requirements of the stakeholders. The stakeholders can then use the results of the data model to make major business decisions.
- Identify Data Needs: Data scientists need to understand what kind of raw data is available to them in the initial phase of the project. They also need to decide if the information included in the data is sufficient to design a solution for the business problem.
- Tools and Techniques: There’s a wide range of different tools available to data scientists. From statistical analysis tools like SAS and Excel to database technologies like MySQL and MongoDB. It’s important that data scientists are up-to-date with all the latest packages. The tools must be powerful enough to not only handle current data sets but increased data as the volume grows over time. Using the correct tools will lead to the empowerment of data scientists and increased team efficiency.
- Rely on Data Over Instinct: Very often, data scientists can discover insights that might seem counterintuitive. It’s critical to be unbiased and logical while analyzing results, and take necessary actions to acknowledge them. Experienced data scientists let the data speak for itself and use an objective approach.
- Secure Your Data Storage: In recent years, there’s been a steep rise in the number of cyber-attacks on organizations worldwide. It’s become mandatory that organizations employ adequate safety features. Data encryption, hashing, two-factor authentication, and other such features can help protect your data from potential hacks.
- Promote Collaboration: A project which is entirely new for one data scientist may have already been explored by someone else. It’s important to ensure the team doesn’t waste time by unnecessarily starting from ground zero. Companies should incorporate a culture of collaboration where everyone learns from one another to avoid reinventing the wheel.
- Act on Data: The true essence of data science isn’t about deciphering massive sets of data and discovering intriguing patterns. Its real value comes from making implementable decisions, based on the results of data. Organizations need to have a clear plan of action to identify the key insights and drivers which their data will impact. The findings should be presented with clear visualizations and should meet the original business objectives. This is a key factor in helping stakeholders follow through on important data insights.
- Testing and Validation: Testing and validating models and results regularly is vital to ensure they stay relevant. Testing also checks the readiness of the model, so it can be updated when needed. The underlying patterns of a model, that were once critical assumptions, can change suddenly due to external reasons. Consequently, this would render the earlier results invalid.
Different data science projects will always have diverse requirements and unique aims. These suggestions are presented as a general, informative, and non-exhaustive list of required data scientist skills. Studying the areas mentioned above will help you succeed in your journey to become a data scientist.
Examples of Data Science in Action
Some examples of how businesses can benefit from data science include:
- Predictive Analysis of Customers: Predictive analysis techniques are all about estimating the lifetime value of customers. This is performed by incorporating data science to identify optimal ways of customer acquisition, customer retention, up-selling, and cross-selling.
- Social Sentiment Analysis: By analyzing data from social media platforms and product review sites, a company can read honest opinions of their products and services. This is a powerful technique used to get accurate feedback and can help firms fix common issues.
- Customer Purchase Intent: Based on past purchase patterns, browsing behavior, and online activity, companies can predict future intent. By analyzing this data it’s possible to learn the demographics of potential buyers, which products they’re likely to be interested in, and when purchases will take place.
- Scam Detection: To identify if fraudulent or malicious activity is taking place, companies can monitor financial transactions and the data related to it. For example if a user from the same IP address creates multiple accounts a scam detection system can flag them as suspicious.
Soft Skills Required for Data Science Roles
Hard technical skills alone are not enough to succeed as a data scientist. You’ll also need social and interpersonal skills. Some examples include:
- Communication skills to effectively convey your findings.
- Intellectual curiosity to help you find answers in big data.
- Business acumen and awareness of current industry trends.
- Critical thinking which is essential for any analytical role.
- Product understanding to offer actionable insights that can help improve product quality.
- Teamwork and the ability to listen to other team members and use their input to your advantage.
How to Learn Data Scientist Skills
Online data science bootcamps have been growing in popularity in recent years. They provide aspiring data scientists with the opportunity to learn all the skills necessary to land a well-paid big data job.
Unlike degree programs which are expensive and can take up to 4 years to complete, bootcamps are intensive, laser-focused, and affordable learning courses. Enroll in our data science bootcamp today to start your accelerated program of online classes. You’ll benefit from one-on-one mentoring from leading data experts. As part of the course you’ll build a professional portfolio to wow prospective employers and help launch your big data career.
A Rewarding Career in Data Science Awaits
Whether you’re a fresher in the field of data science or a seasoned data scientist, how efficiently you leverage the enormous amount of data around you will decide how successful you are in this field. If you have an analytical mind and a passion for big data, a future in data science could be perfect for you.
Maybe you’ve realized it’s time to brush up on MySQL, or highlight your stellar communication skills in your next cover letter. In one way or another, hopefully we’ve inspired you to level-up your data career. Check out our data science blog for further insight into the field, like whether data science degrees are actually worth it.