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Top 5 tech skills data scientists need, and how to learn them – TechRepublic

Data science job candidates can boost their resumes by honing these skills through these free or low-cost resources, according to Indeed Prime.

Top 5 questions a data scientist should be able to answer Tom Merritt asks the five questions every data scientist should have the answers to.

Data scientists remain in high demand, but those interested in pursuing a career in the field must have the right skillset to land a job with a top salary, according to a Thursday report from Indeed Prime.

Demand for data science professionals continues to rise as more companies seek to collect and analyze data and draw business insights from that information. Data scientist job postings have increased by 256% since December 2013, and median base salaries have reached $130,000, according to Indeed data.

As more companies adopt data-driven approaches, data scientists must keep their skills current based on what employers need, the report noted.

SEE: How to build a successful data scientist career (free PDF) (TechRepublic)

Indeed Prime analyzed the most asked-for skills in Indeed job postings. Here are the data science skills in highest demand, and how and where to develop them to improve your resume.

1. Machine learning

Machine learning is a subfield of artificial intelligence (AI) that involves computer systems using data and algorithms to teach themselves to make predictions without being programmed to do so. The field will be key for advancing technologies including self-driving cars and increasingly personalizing the customer experience in areas like retail, the report noted.

Machine learning combines data science, math, and software engineering, so it requires an extensive skillset to learn. Machine learning skills include computer science fundamentals, programming, probability and statistics, data modeling and evaluation, algorithms and libraries, and software engineering and system design.

Resources to learn machine learning: Kaggle has a community of data scientists and machine learning engineers who work together to publish datasets, build models, and compete to solve data science problems, which can be a good place to start, according to the report.

2. Python

Python is a general-purpose, object-oriented programming language that runs on most operating systems, and has been one of the fastest-growing and most popular programming languages in recent years. It is also a powerful data and visualization tool, with a set of libraries that include a number specific to machine learning, including NumPy, SciPy, scikit-learn and Pandas, the report noted. Python is also the most frequently-mentioned skill found in data science job postings.

Resources to learn Python: You can practice Python on your own with resources like PyBites, and Real Python‘s online tutorials.

3. R

R is an open source statistical software package that simplifies the analysis of large data sets, and includes features such as linear and non-linear modeling, clustering, and time-series analysis. R continues to grow in popularity, and, along with Python, is one of the most common skills listed in data science job postings.

R also allows data scientists to perform statistical and predictive analysis on real-time data, and then create interesting visuals to communicate that information to the business side, the report noted.

Resources to learn R: R for Data Science is a good book resource that can be read online or in print, the report noted.

4. SQL

SQL is a domain-specific programming language that makes retrieving data possible, and gives data scientists a way to access and manipulate large amounts of information found in a relational database management system, according to the report. SQL commands can capture and break down data, as well as edit database tables and indexes to improve accuracy. SQL skills are fundamental in the data science field, Indeed noted.

Resources to learn SQL: SQL Fiddle is a free, interactive tool that lets users test and share SQL queries in their browser.

5. Hadoop

Hadoop is a software framework that stores and processes large volumes of data across clusters of computing devices. It is flexible, scalable, and helps companies identify trends and predict outcomes to improve decision-making, the report said. While possible to get a data science job with limited Hadoop experience, a solid understanding of the framework is a strong selling point that can lead to more opportunities and better pay, it noted.

Resources to learn Hadoop: Free courses in Hadoop fundamentals and programming are available from Cognitive Class, which give badges for your portfolio upon successful completion, the report noted.

“Virtually every industry is collecting data with the intent to drive value and growth. And it’s clear that data scientists with the right mix of skills can best interpret this information,” the report said. “So whether you’re breaking into the data science field or giving your tech skills a boost, it’s a good idea to tailor your learning based on employer demand.”

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