Data science is a fast-evolving field offering unlimited opportunities for savvy and career-minded students. If you find that you’re drawn to this exciting area of study, and you’re ready to challenge yourself—data science could be an ideal career path for you.
Before you decide whether to pursue a career in data science, you should be aware of what awaits you, and how you can prepare yourself. This article addresses key aspects of any data science job that beginners should understand before committing to the field.
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What Is Data Science?
Data science involves the extraction of nuanced insights from data using statistics, algorithms, scientific methods, math, and other techniques. In simple terms, data science means using data to help with problem-solving and decision-making. Data scientists collect raw data and convert it into meaningful information.
This process involves multiple steps that can be divided into the following subdomains:
- Data Acquisition: The first step involves collecting data from various sources like databases and the internet. The data is then filtered and cleaned to remove errors and potential duplicates. The data is typically stored in a data warehouses, which allow the easy extraction of information.
- Data Preparation: This is the stage that data scientists spend a lot of their time in. Data that is unfit or erroneous is converted into meaningful data, and inaccurate or null data is cleaned—known as data cleansing. This stage also includes data transformation, where data is converted into a usable format that ensures accuracy. It is then compiled together with data from different sources.
- Data Mining: Data mining involves analyzing patterns and relationships in the data to make decisions and draw conclusions. The purpose is to find connections between data that may not be apparent at first glance. Data mining aims to predict future trends and customer patterns, detect fraud, and generally help in business decision-making.
- Model Building: Data scientists build models with the use of machine learning algorithms. A supervised learning algorithm is used to predict the output from labeled input data. If the data is not labeled, then an unsupervised learning algorithm can be used to model the underlying or hidden structure in the data. Data scientists are also responsible for maintaining the accuracy of the model.
Skills Required to Become a Data Scientist
- Statistics: Having a background in statistics or a degree in the field can be really helpful. You should also develop experience in descriptive statistics, percentiles, probability, random variables, exploratory data analysis, and cumulative distribution function.
- Programming: You’ll need to become an expert in at least one of the main programming languages. Knowing R or Python is a prerequisite in many companies for data scientist positions.
- Machine Learning: For many companies, machine learning is not a prerequisite but it’s certainly a preferable skill that they’ll look for in a candidate. Knowledge of machine learning—including logistic regression, decision tree, reinforcement learning, and supervised and unsupervised machine learning—will distinguish you from your competitors.
- Data Visualization: Visual representation of data is extremely important in data science. You should know how to use various data visualization tools such as Kibana, Datawrapper, Tableau, ggplot, d3.js, and Matplottlib. These tools are used to convert complex data into a format that is interesting and easily understandable.
- Apache Spark: This is currently one of the most popular big data technologies as it helps data scientists to run algorithms much faster. It can also help you deal with unstructured data sets. Using Apache Spark, you can easily prevent data loss and carry out your projects smoothly.
- Teamwork: Apart from technical and computer skills, you’ll need to work on your communication and teamwork skills, because a data scientist cannot work in isolation. You’ll often work with multidisciplinary teams involving product managers, strategists, marketers, product designers, and other professionals.
Online Tools for Learning Data Science
There are many paid and free tools available on the internet that can help you kickstart your data science career. Below is a snapshot of some of the more popular tools.
- Matplotlib: This is an online library for learning the programming language Python, which is a crucial tool for every data scientist to master.
- Tidyverse: This is a collection of R packages designed for data science. Tidyverse is one of the best tools for aspiring data scientists wanting to learn R programming. It includes packages like the dendrochronology program library in R (dplr) for data manipulation, ggplot2 for data visualization, and many more that will allow you to clean, process, model, and visualize data efficiently.
- Apache Hadoop: This is an open-source framework that has the capability to store tons of data. You can use it for data processing and high-level computations.
- RapidMiner: RapidMiner is one of the most popular tools for data scientists. it’s a platform that allows you to learn data processing and build machine learning models.
- Tableau: Tableau is the perfect tool for data visualization. The platform allows you to present your data in an engaging manner and facilitates quick decision-making. Features include advanced data calculations, a responsive user interface, and a fully secure system.
- Knime: This is a multi-purpose tool that can be used for data reporting and analysis. It also integrates functions like data mining and machine learning. Best of all, minimal programming knowledge is required.
How Much Do Data Scientists Get Paid?
Data science is both in-demand and a highly technical field requiring rigorous training and advanced skill sets. Salaries are competitive and depend on your skill level, qualifications, and experience.
For comparison, below are a few national average salary levels for related job titles:
How to Become a Data Scientist
The following are the steps that can help you kickstart your career in data science:
- Read up about the field
It helps to be aware of what you’re getting into. Reading books, blog posts, online articles, and journals about data science will help you understand the field and the opportunities available to you. It’ll help you figure out if data science is the right career for you.
2. Choose an academic path
If you’re sure you want to become a data scientist, then you’ll need to select the right academic path. There’s no specific degree that you need, so you’ll need to decide whether you want to study a bachelor’s degree in statistics, mathematics, computer science, IT, or another relevant stream.
A master’s degree in a related field will be even more helpful. If you don’t opt for a degree, you can always choose an open data science online course or bootcamp that can train you to be a data scientist.
3. Choose an area of specialization
There’s no use studying each and every aspect of data science. It’s best to choose a specialization that encompasses your key interest areas. For instance, you may choose to focus your expertise on business solutions or data visualization. You can then select a specialized course to train you in that area.
4. Get certified
Certifications in the right skills, tools and areas of expertise increase your chances of getting hired because they provide an easy reference point for employers that you have the right knowledge.
For example, you can become an SAS Certified Predictive Modeler using SAS Enterprise Miner 7. There’s also a wealth of online certifications available at your fingertips. All you need to do is pick the data science certification that fits your needs and get started.
5. Find a job
Landing a great job isn’t always easy, but if you have the right skills, qualifications and experience, your chances are much higher. There are plenty of forums, job boards, and recruitment networks, such as Kaggle and iCrunchData, that can help you to find and land a job that’s right for you.
If you follow each of these steps, you’ll be on your way to a successful career in data science. Want to find out more before you invest in a new, data-focused education? Check out our four must-read tips for launching a career in data science. Or get a glimpse of what’s ahead: find out what a day in the life of data scientist looks like.
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