Which is better for Data Science, Python or R?

Both of these tools can get the job done, and you’ll get a different answer depending on who you ask. While R is historically dominant, Python has emerged as the programming language for data science, and you’ll see more and more companies with job listings that allow for, if not prefer, Python. Python has a robust set of data science libraries like Pandas, Matplotlib, Scikit Learn, and Seaborn, all of which you’ll learn at Codeup.

What is the difference between supervised and unsupervised machine learning?

Supervised and unsupervised are two groups of machine learning methodologies. In supervised learning, a data scientist teaches the algorithm what conclusion to arrive at using a known set of possible outputs. In unsupervised learning, a computer identifies patterns without human guidance. More often than not, data scientists work with supervised learning algorithms.

What is the difference between Data Science, data engineering, and data analysis?

Whenever you see “data” in a job title, you’re working along the data pipeline ranging from capturing high volumes of data to building machine learning automations. A data scientist spans this spectrum, while other roles focus on one phase or another. A data engineer, for example, focuses on capturing and storing data sets for others to work with. A machine learning engineer only works with automation and model deployment. A data scientist might work on data collection, data processing, future event prediction, machine learning, and more. Check out this blog to learn more about the data science pipeline and the different career roles within it!

What is a data visualization?

Data scientists do complex work, which non-technical co-workers might struggle to understand. A data visualization is a visual representation of data that is easy to digest.

What is the Data Science process?

The data science process has about 7 steps: 1) data wrangling (getting and cleaning data); 2) exploratory data analysis, statistical inference, and data visualization; 3) feature engineering; 4) development of a predictive model (training, evaluating, optimizing, testing); 5) model deployment; 6) delivery of results (report, story, visualization); 7) model maintenance. For a visual of this process using credit card fraud detection as an example, click here.

What is machine learning?

Machine learning (ML) is what it says – your machine learns as it works! ML algorithms allow your computer to generalize decision-making beyond the specific data set it has worked with, and allows for broader automation. Check out our blog post “What is Machine Learning” for a more in-depth description!

What is deep learning?

In its simplest term, this is a family of machine learning methods based on learning data representation, as opposed to task-specific algorithms. Deep learning allows an automatic feature detection in place of manual feature engineering.

What is ‘Big Data’?

Big data differs from in-memory data, in that it is too large and complex to manage on a local computer. It’s defined by the big V’s: Velocity (data that’s collected at great speed), Volume (large amounts of data), Variety (different forms of data), and Veracity (uncertain quality of the data).

What are the pre-requisites for the Data Science program?

Our admissions process includes assessments to gauge your understanding of basic statistics and Python programming. However, these skills are something you can build during the admissions process. If you’re interested in data science, we encourage you to go ahead and apply. From there, our Admissions Team will work with you to figure out where your skills currently are and how to prepare for the program.

What makes a successful Data Scientist?

We interviewed dozens of employer partners and practitioners to build our program, and discovered something surprising: the ability to communicate your work is one of the most important skills for a data scientist! Of course, you need to be competent in math, stats, Python, and other tools/technologies. However, what separates a successful data scientist is the ability to make their work digestible, relevant, and actionable.