Photo of what does a data scientist do: Skills and tools needed to become a data scientist

What does a data scientist do? Skills and tools needed for data science

Hi, I’m Olga! I have years of experience in data science and now I work as an industry mentor at Pathrise, helping data scientists land a great role through technical workshops and 1-on-1s.

Data science is one of the most lucrative and rapidly growing fields in the tech industry. But what does a data scientist do, exactly? With a number of job postings for data scientists, data analysts, and data engineers, it can be challenging to find the right job to kickstart your data science career. To help you get started, we have broken down what responsibilities, tools, and skills are required to land a job as a data scientist. 

Solve business problems

Data scientists use descriptive, predictive, interferential and causal models to explore, anticipate, and solve common business problems. By analyzing multiple factors, which can include variables such as age and gender, data scientists propose solutions based on real-world data in order to identify important consumer and economic trends. In addition, data scientists help the company decide whether or not they need to collect additional data. Generally, they are assigned projects that help businesses identify customer demographics, optimize the sales process, track how likely customers are to buy a service or product, and predict future demands and events.

Collect, process, and create data sets

Before solving a problem, data scientists need to collect data. While each scenario is different, data scientists generally start by writing queries. They use tools such as SQL (standard query language) to collect important information from a variety of databases. Once they have their initial dataset, they need to clean the data, which involves checking for mistakes, ensuring that the variables are consistent, 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. 

Relevant tools:

  • SQL
  • NoSQL 
  • MySQL
  • Hadoop
  • Apache Spark
  • Geospatial
  • Databases
  • Redis
  • MongoDB

Explore and analyze data sets

Once they have created their data sets, data scientists can begin analyzing the information by looking at a variety of variables, such as age, gender, geography, time, marketing campaign strategy, and more. Next, they use machine learning, statistical models, and algorithms to create predictive models to help solve the initial problem. They begin with algorithms that are skeletal and simple. As they become more familiar with each data set, data scientists continue to modify the algorithm and formulas to maximize efficiency and accuracy. Most importantly, they train and test the strength of the data to ensure that it is actually predictive. As they conduct more research, data scientists invent new algorithms, build more effective analytical tools, and continue evolving and optimizing their current strategies.

Relevant tools:

  • R
  • Java
  • Python
  • C++
  • CSV
  • JSON
  • TensorFlow
  • Natural Language Processing (NPL) 
  • Caffe2

Present data and propose solutions 

Finally, data scientists must communicate their results by engaging in data storytelling. In addition to providing information via spreadsheets, data scientists create visualizations to help their team members understand the results. Their results need to be easily understood by people across all teams, including sales and marketing, who might not have a strong technical background. Working with other stakeholders across the company, they propose solutions based on their data. Data scientists recommend changes to existing procedures and strategies to make them more cost-effective. Possible solutions include marketing to a different audience, focusing on products with a higher return on investment, and selecting certain marketing campaigns over others. 

Relevant tools: 

  • Tableau
  • Ggplot
  • MATLAB
  • Python pandas
  • TensorBoard
  • Microsoft Excel

What education and background do you need to land a job as a data scientist?

Aspiring data scientists should have a strong background in commonly used programming languages (Python, R, and Java) and advanced skills in math and statistics, including multivariable calculus and linear algebra. Many data scientists have a Master’s or a Ph.D in fields such as data science, statistics, economics, math, computer science, and management information systems.

Some data scientists come from other fields, including cognitive science, political science, anthropology, the life sciences, physics, and psychology. Students who are looking to strengthen their data science and math skills can consider enrolling in a data science bootcamp to brush up on the basics, learn in-demand data science skills, and land an entry level data science job.

If you are currently on the job market, check out our suggestions for how to get a data science job. As you start looking for jobs, it is important to optimize your data science resume, as well as begin building an effective data science portfolio, which allows you to showcase your real-world experiences and previous projects.

For people who are new to the field, we have also compiled a list of the best resources to learn data science

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 are interested in optimizing your job search by working 1-on-1 with a mentor, become a Pathrise fellow. 

Apply today.

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