Data science is a broad discipline that covers everything from statistics to machine learning. It is a relatively new field, but everyone wants to learn more about it now that data has become essential in business and society. You can use it to fulfill multiple business objectives, and this post will elaborate on the different data science techniques.
What Is Data Science?
Data science is the discipline concerned with trend analytics and interpretation solutions to answer complex questions by processing mixed data sets. So, data science solutions use structured and unstructured data for insight generation.
Data scientists are professionals who combine statistical concepts with computer science. Therefore, the business can assess its growth and forecast future sales trajectories.
Data management firms often utilize data science techniques to enhance the scope of their data analytics solutions by estimating trends into the future. Otherwise, data analysts can provide insights only into the past or present.
Also Read: Develop Business Strategy Using Facts
Essential Data Science Techniques
1 | SQL (Structured Query Language)
Business applications use SQL, pronounced as the ‘sequel,’ as a database language suitable to query, manipulate, and analyze datasets. Besides, it is the most popular language for interacting with databases and has been around since 1986.
SQL is mostly a declarative language that describes what you plan to accomplish instead of how you expect to do it. This working model makes it more efficient for programmers to implement new data science techniques without having all the data upfront.
Often, firms select an appropriate database language when working on large-scale projects with different data science services and data analytics solutions.
Also, SQL is a high-level database management language because it allows you to describe complex actions using simple command syntax and data structure. It is easier than C++ or Java because those low-level languages demand more code writing, complicated errors, and extensive debugging.
2 | Basic Statistics
You may have heard the term statistics in data science optimization techniques. Statistics is a branch of mathematics that deals with data patterns and how to interpret their changes over time. It gives you tools to understand your data better, which will help you make informed decisions.
Many statistics are available for data science solutions, machine learning, and deep learning. Frequently, the following statistical optimization concepts are crucial in data science techniques.
- The mean is the average value obtained by dividing all observed values by the number of records.
- Additionally, the median is the middle value you would observe in the dataset.
- You can consider the mode as the most common number in the database.
- Also, variance shows how values change in a range for respective standard deviations in data.
3 | Regression and Classification
Understanding the difference between classification and regression is essential because it might confuse your team members. While you can perform these two tasks simultaneously, they have distinct solutions based on data science optimization techniques.
In classification, you attempt to predict an object’s class or category based on its attributes. The process consists of numerical values, categorical values, or text values. E.g., age data is numerical while gender is a category. Also, the employee job title or marketing campaign name is a text value.
A simple use case is how you assign a probability value for whether an email message is spammy or legitimate based on words used within it. Such techniques are integral to several data science solutions and analytics services.
In regression, you forecast continuous values given other known numeric variables from your data set. For example, advanced data science techniques can predict annual revenue from monthly revenue information and the cost of goods sold (COGS).
4 | K-nearest Neighbors
K-nearest neighbor (KNN) is a classifier that learns from the training data. Also, the training data helps you build a model, and this model is used to classify new data. The firms look at all the points in the training set and find which one best matches your revised data point.
Therefore, you can discover the nearest neighbor to the unique initial point. The KNN algorithm works well when you have small amounts of labeled data (say ten or fewer). So, it is often present in applications that handle little labeled input.
5 | Decision Trees
Decision trees are classification methods that use tree structures to show how you classify data into several groups. You can use decision trees in supervised and unsupervised machine learning methods for advanced data science techniques.
A decision tree consists of nodes (branches) with multiple paths, and each node path ends at a terminal node containing the outcome for that branch.
Decision trees are often remarkable in predicting future outcomes based on past events or trends. Your team can test all possible combinations of variables to conclude the relationship between their interactions.
For example, imagine predicting if someone will become ill during flu season by studying their age, weight, gender, and lifestyle via data analytics solutions and decision trees.
These are some standard data science techniques you require to understand its methodology. Business leaders can apply them in many ways to solve numerous corporate development problems.
Using advanced data science techniques allow for further micro-enhancements leading to exclusive insight generation. However, you require professional assistance to implement these techniques correctly for data science optimization.
A leader in data science solutions, SG Analytics, assists organizations process complex datasets in unlocking robust business growth. Contact us today if you desire scalable analytical capabilities driven by actionable insights.