Technical Knowledge and Skills Development
21 Technical Resources for Data Science
Technical Resources for Data Science
Data science is an interdisciplinary field that combines techniques from statistics, computer science, mathematics, and domain expertise to extract valuable insights and knowledge from data. It involves various processes such as data collection, data cleaning, data analysis, and data visualization to understand patterns, make predictions, and inform decision-making.
Here’s a breakdown of the main components of data science:
- Data Collection: Gathering data from various sources, which can include databases, web scraping, IoT devices, social media, and more.
- Data Cleaning: Preparing data for analysis by handling missing values, outliers, and inconsistencies to improve data quality.
- Exploratory Data Analysis (EDA): Understanding the data’s structure, distribution, and main features through visualizations and statistical summaries.
- Statistical Analysis: Applying statistical techniques to interpret and draw inferences about the data.
- Machine Learning: Building models using algorithms that allow computers to learn from data and make predictions or decisions without being explicitly programmed.
- Data Visualization: Presenting data findings in visual formats like charts, graphs, and dashboards to make complex information easier to understand.
- Communication: Translating data-driven insights into actionable recommendations, often by creating reports and presentations for stakeholders.
Data science is applied across various industries, including healthcare, finance, retail, and tech, where it helps in applications like fraud detection, customer segmentation, personalized recommendations, and predictive maintenance.
The following free Codecademy course will help you prepare for an interview with a focus on data science.
The LinkedIn learning tutorials below can help you prepare for an interview with a focus on Data Science.
Learning RThe lessons explains how to get started with R, including installing R, RStudio, and code packages that extend R’s power and how to leverage this tool to explore and analyze a wide variety of data. Learn More |
R Essential Training: Wrangling and Visualizing DataThis training series provides a thorough introduction to R, with detailed instruction for installing and navigating R and RStudio and hands-on examples, from exploratory graphics to neural networks. |
R for Data Science: Lunchbreak Lessons (Intermediate)Review language basics, discover methods to improve existing R code, explore new and interesting features, and learn about useful development tools and libraries that will make your time programming with R that much more productive. |
R for Data Science (Intermediate)This training series provides a thorough introduction to R, with detailed instruction for working with R and RStudio and hands-on examples, from exploratory graphics to neural networks. |
R for Excel UsersData scientists who use Excel realize that R is emerging as the new standard for statistical wrangling (especially for larger data sets). This course serves as the perfect bridge for the many Excel-reliant data analysts and business users who need to update their data science skills by learning R. |
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