5 Data Science Concepts Every Data Scientist Should Know

Now is the ideal time to build your expertise in data science concepts. Thanks to the rise of artificial intelligence and big data, data science has become a highly sought-after knowledge area. The Bureau of Labor Statistics  estimates that data science jobs will grow by over 30% between 2019 and 2029.

This blog post will introduce you to key data science concepts and why they’re essential for extracting insights from data. You’ll also learn how you can improve your readiness to solve real-world challenges with data.

1. Programming

Programming is a fundamental data science concept. It refers to the process of creating a computer program to accomplish a specific task. Data science professionals should understand programming concepts such as programming languages, data structures, and algorithms.

Programming Languages

Programming languages tell computers how to carry out tasks or achieve desired outputs. Data scientists use them to create and implement algorithms, build statistical models, and perform statistical analysis. SAS, R, and Python are common programming languages in data science  .

Data Structures

Data structures are techniques for storing, organizing, and manipulating data. As a data scientist, you’ll work with arrays, linked lists, stacks, queues, graphs, and more. You should know the various types of data structures and how to choose the most appropriate one to solve your problem.

Algorithms

Algorithms and data structures are closely related data science concepts. An algorithm is a set of rules performed on a data structure for achieving the desired output.

For example, data scientists use machine learning algorithms to make predictions and classifications. Machine learning algorithms include linear regression, logistic regression, and dimensionality reduction.

2. Statistics

Programming and statistics form the backbone of data science. If you want to employ data science in your career, you should know how to organize, interpret, and derive insights from data using statistics.

Numerous data science concepts are related to statistics, such as:

Probability Distributions

Probability distributions are mathematical functions that determine the probability of an outcome occurring. There are various types of probability distributions, and each helps data scientists answer different questions.

Hypothesis Testing

Hypothesis testing is a statistical method for evaluating a theory with data. Data scientists must know statistical tests such as predictive modeling, data dimension reduction, classification, and clustering.

Statistical Significance

In hypothesis testing, statistical significance is a figure that measures the reliability of the results. In other words, it shows the degree to which the results are due to chance.

Regression

Regression is a statistical analysis technique that examines the relationship between two or more variables. Data science professionals use regression to develop predictions, forecasts, and models.

3. Database Management

Practicing data science involves regular interaction with databases. That’s why database management is another important data science concept.

As a data scientist, you should understand database theory and design. You must also know the tools and technologies needed to create and manage large-scale databases.

Data scientists are familiar with:

• All the components of a database system, including the hardware, software, and system utilities.

• Their role and responsibilities in database management, including enterprise policies and procedures.

• Methods for managing stored data using query languages such as SQL.

4. Artificial Intelligence (AI)

AI is a vital data science concept in today’s business environment. More than half of U.S. companies surveyed for a recent PwC report that they accelerated their plans for AI adoption.

AI refers to the ability of computers or machines to perform tasks typically associated with humans. It has applications in every industry. From marketing to supply chain management, AI helps business functions automate processes, detect fraud, predict consumer preferences, and beyond.

Data scientists should understand the theory and application of AI and its related data science concepts.

Data Mining

Data mining is the foundation of AI. It identifies patterns in large structured datasets, which helps data scientists build predictive models for AI systems.

Machine Learning

Machine learning is a type of AI that enables machines to solve problems without explicit programming. It harnesses algorithms and statistical models to find patterns in data and then draws inferences to make predictions and classifications.

Deep Learning

Deep learning is a technique for performing machine learning. With deep learning, computers “learn” from data using multiple layers of algorithms arranged in what’s called a neural network. Neural networks are modeled after the human brain and can process complex non-linear relationships.

5. Big Data

Today, companies have extensive access to large, complex volumes of data known as big data. And the amount of big data is growing. The technology research company TechNavio predicts the big data market will expand  by over $247B from 2021 to 2025.

Big data is a crucial data science concept. By tapping into big data, enterprises can  achieve cost savings, make more efficient and informed decisions, and discover new growth opportunities.

Data scientists should understand the types of big data (structured, unstructured, and semi-structured) and the techniques and tools for visualizing, managing, and analyzing big data. Also important is knowledge of the four types of data analytics. Each solves a different problem using a unique methodology: