Top Data Science Topics that a data scientist must know

Top Data Science Topics That A Data Scientist Must Know

Without a doubt, data science topics and sectors are among the most popular business subjects today. Not only do data analysts and business intelligence specialists want to improve their data skills and expertise, but so do marketers, C-level executives, bankers, and other professionals.

The data science world is a broad field that includes sub-fields such as mathematics and statistics for data science and mining, machine learning, artificial intelligence, neural networks, etc.

Here, I've compiled a list of fundamental and advanced data science subjects to help you decide where to focus your efforts.

1. The core of the data mining process

Data mining is an iterative process that includes finding patterns in massive amounts of data. Methods and techniques such as machine learning, statistics, database systems, and so on are included.

In order to address issues, the two significant goals of data mining are to uncover patterns and build trends and relationships in a dataset.

• Problem formulation, data exploration, data preparation, modeling, assessment, and implementation are the general steps of the data mining process.

• Data mining keywords include classification, predictions, association rules, data reduction, data exploration, supervised and unsupervised learning, dataset organization, sampling from datasets, model construction, and so on.

1. Data visualization

The presentation of data in a graphical style is known as data visualization. It allows decision-makers at all levels to view data and analytics visually presented, allowing them to find useful patterns or trends.

Another vast area is data visualization, which includes the study and use of fundamental forms of graphs (such as line graphs, bar graphs, scatter plots, histograms, box and whisker plots, and heatmaps).

You cannot go without these graphs. Furthermore, you must learn about multidimensional variables by adding variables and employing colors, sizes, forms, and animations.

Manipulation is also involved here. You should be able to zoom, filter, and combine data.

Using specialized visualizations such as map charts and tree maps is also valuable.

1. Dimension reduction methods and techniques

Dimension Reduction is the process of transforming a data set with many dimensions into a dataset with fewer dimensions while ensuring that it gives equal information in a short amount of time.

In other words, dimensionality reduction is a set of approaches and methodologies used in machine learning and statistics to reduce the number of random variables.

Dimension reduction may be performed using a variety of methods and procedures.

Missing Values, Low Variance, Decision Trees, Random Forest, High Correlation, Factor Analysis, Principal Component Analysis, and Backward Feature Elimination are the most common. More information on these concepts would be explained in a data science course in Bangalore.

1. Classification

The fundamental data mining technique for categorizing a set of data is classification. The goal is to aid in the collection of reliable data analysis and forecasts. Classification is one of the most important ways to analyze massive amounts of data effectively.

Classification is another popular data science topic. A data scientist should understand how to apply classification algorithms to various business challenges. This involves understanding how to construct a classification issue, investigate data using univariate and bivariate visualization, extract and prepare data, develop classification models, and assess models, among other things. Some of the essential terms here are linear and non-linear classifiers.

1. Simple and multiple linear regression

Linear regression models are among the most fundamental statistical models for investigating connections between an independent variable X and a dependent variable Y.

It is a mathematical model that allows you to anticipate and forecast the value of Y based on different values of X.

Linear regression is classified into two types: basic linear regression models and multiple linear regression models. The key concepts include correlation coefficient, regression line, residual plot, linear regression equation, etc. See some easy linear regression examples to get started.

1. K-nearest neighbor (k-NN)

N-nearest-neighbor is a data categorization technique that determines the likelihood of a data point belonging to one of many groups. It is determined by how close the data point is to that group.

K-NN is one of the most important data science topics ever since it is a primary non-parametric approach used for regression and classification.

A data scientist should be able to determine neighbors, use categorization methods, and choose k, to name a few talents. For example, the KNN is one of the most crucial text mining and anomaly detection methods.

1. Naive Bayes

It is a set of classification algorithms that are based on the Bayes Theorem.

Naive Bayes is widely used in Machine Learning and has specific critical applications, such as spam detection and document categorization. There are several Naive Bayes variants. The most often used are the Multinomial Naive Bayes, Bernoulli Naive Bayes, and Binarized Multinomial Naive Bayes.

1. Classification and regression trees (CART)

Decision tree algorithms play an essential part in predictive modeling machine learning algorithms.

The decision tree is a prominent predictive modeling strategy in data mining, statistics, and machine learning that constructs classification or regression models in the shape of a tree (hence the names regression and classification trees). CART decision tree technique, classification trees, regression trees, interactive dichotomization, C4.5, C5.5, decision stump, conditional decision tree, M5, and other words and subjects you should be familiar with in this discipline.

1. Logistic regression

Logistic regression, like linear regression, is one of the oldest data science subjects and fields, and it analyzes the link between dependable and independent variables.

However, logistic regression analysis is used when the dependent variable is dichotomous (binary).

Sigmoid function, S-shaped curve, multiple logistic regression with categorical explanatory variables, multiple binary logistic regression with a combination of categorical and continuous predictors, and other concepts will be encountered.

1. Neural Networks

Nowadays, neural networks are a massive hit in machine learning. Neural networks (also known as artificial neural networks) are hardware and software systems that imitate the behavior of human brain neurons.

The primary purpose of developing an artificial neural network is to produce systems that can be taught to understand data patterns and perform functions such as classification, regression, prediction, and so on.

Deep learning technologies such as neural networks are utilized to solve complicated signal processing and pattern recognition challenges. Key terminology in this section refers to the idea and construction of Neural Networks, perceptrons, back-propagation, and the Hopfield Network.

Conclusion

These are only some of the topics; there are many other topics like Discriminant analysis, Association rules, Cluster analysis, Time series, Regression-based forecasting, etc. Learn these exciting topics from the IBM-accredited data science training in Bangalore today and gain profound knowledge of the real data world.