Hi, I’m Olga! I have years of experience in data science, most recently at eBay. Now, I work as an industry mentor at Pathrise, where I help data scientists land great jobs through technical workshops and 1-on-1s. Check out my article about data science tools.
If you are looking for your first job or getting ready to find a new position in data science, you need to make sure your resume is as strong as possible. A designated skills section is a great way to emphasize your proficiency in a number of tools and technologies that are needed for success in the role. But, before you dive into your job search, it is important for you to build out this part of your resume by mastering the data science tools that will make you a great asset to any company.
We have worked with a number of data scientists on each step of their job search. Ultimately, we’ve helped them land great roles where they can turn large amounts of data into actionable insights for the company. Therefore, we outlined 4 important types of data science tools and examples of each. That way, you can be successful and land a great data science job.
1. Data gathering, processing, and visualization tools
Why is data gathering, processing, and visualization important? One of the main responsibilities of a data scientist is collecting, sorting, and visualizing data. They generally start by writing queries using tools like SQL (standard query language). These gather important information from a variety of databases. When their preliminary dataset is ready, they clean the data. This involves ensuring consistent variables, verifying that the data is accurate, and removing obvious outliers. With so much data available, data scientists are in charge of finding and curating the most useful information for solving the problem at hand.
Examples of data gathering, processing, and visualization tools. SQL, NoSQL, MySQL, Hadoop, Apache Spark, Geospatial, Databases, Redis, MongoDB
2. Statistics and data analytics tools
Why are statistics and data analytics important? Data scientists are responsible for understanding the data from user tests, internal and external processes, and algorithms. Then, they analyze the metrics to make recommendations and decisions for success. Once data scientists have fully parsed the data to glean insights, they need to be able to present their solutions to technical and non-technical team members and stakeholders. To do this, they create charts, graphs, spreadsheets, and decks.
Examples of statistics and data analytics tools. SaS (Statistical Analysis System), KNIME, Microsoft Excel, R, Java, Python, C++, CSV, JSON, TensorFlow, NPL (Natural Language Processing), Caffe2, Tableau, Ggplot, MATLAB, Python pandas, TensorBoard
3. Machine learning tools and technologies
Why is machine learning important? Some data scientists specialize in machine learning and artificial intelligence. But, others just need to know the broad strokes of the subject. This helps them make use of the tools at their disposal. This is especially true if you are looking to work at a data-driven company with very large amounts of data. The reasoning? It is nearly impossible to work through it all without the help of machine learning.
What are some examples of machine learning tools and technologies? K-nearest neighbors, Random Forests, Naive Bayes, Regression Models, PyTorch, TensorFlow, Keras, DataRobot, BigML, RapidMiner
4. Programming languages
Why is programming important? Programming helps data scientists be successful in their role. Using statistical languages like Python and querying databases with SQL, data scientists can turn large datasets into insights. Recruiters and hiring managers are always looking for knowledge of programming languages on resumes to ensure that potential team members will be able to provide impact.
What are some examples of programming tools? R, Java, MATLAB, Python, C++, Julia, CSV, JSON, Scala, TensorFlow, NPL (Natural Language Processing)
Pathrise is a career accelerator that works with students and professionals 1-on-1 so they can land their dream job in tech. If you want to work with any of our mentors 1-on-1 to help you land a great job in data science, become a Pathrise fellow.