Data Science Topics and Areas

The broad category of data world includes data mining, machine learning, artificial intelligence, neural networks, and other branches of data science.

On this page, we’ve gathered some basic and advanced data science topics to assist you in deciding where to concentrate your skill-building efforts.

They are also popular subjects that you can use as a resource to prepare for questions posed during data science job interviews.

Basic Data Science Topics

1. The core of the data mining process

  • Data mining is an iterative process that seeks patterns in large data sets. Machine learning, statistics, database systems, and other techniques and tools are used.
  • Two of data mining’s primary objectives are to find patterns and recognize trends and linkages in a dataset in order to solve issues.
  • The general steps of the data mining process are problem conceptualization, data exploration, data preparation, modeling, evaluation, and implementation.
  • Key terms linked with data mining include classification, predictions, association rules, data reduction, data exploration, supervised and unsupervised learning, dataset organization, dataset sampling, model building, and others.

 2. Data Visualisation.

  • Data and analytics can be visually presented to decision-makers at all levels, enabling them to identify intriguing patterns or trends.
  • The broad field of data visualization includes the study and use of basic graph types such as line graphs, bar graphs, scatter plots, histograms, box and whisker plots, and heatmaps.
  • Manipulation also plays a role in this situation. Data should be able to be combined, filtered, and zoomed in.
  • The capacity to use particular visualization, such as tree maps and map charts, is another well-liked skill.

3. Dimension Reduction methods and techniques

  • A data set with multiple dimensions is reduced in size while maintaining the ability to clearly communicate the same information.
  • In other words, dimensionality reduction includes reducing the number of random variables using a variety of machine learning and statistical methodologies and strategies.
  • There are numerous methods and procedures for achieving dimension reduction.
  • The techniques that are applied most frequently are Missing Values, Low Variance, Decision Trees, Random Forest, High Correlation, Factor Analysis, Principal Component Analysis, and Backward Feature Elimination.

4. Classification

  • Classification is a fundamental data mining technique for categorizing a set of data.
  • The objective is to derive exact analyses and predictions from the data.
  • Classification is one of the most crucial methods for efficiently analyzing a large number of datasets.
  • A data scientist should be skilled at using classification techniques to solve various business problems.

5. Simple and Multiple linear Regression

  • The linear regression model is one of the basic statistical models for analyzing relationships between an independent variable (X) and a dependent variable (Y).
  • By using several values of X, this sort of mathematical modeling enables you to predict and foresee the value of Y.
  • The two main types of linear regression are simple linear regression models and multiple linear regression models.
  • In this context, it’s crucial to understand terms like correlation coefficient, regression line, residual plot, linear regression equation, and others. See a few simple linear regression beginning examples.

6. K-nearest neighbor (k-NN)

  • The likelihood that a data point belongs to a specific category is determined using the N-nearest-neighbor data categorization technique. In relation to how near that group the data point is,
  • K-NN is one of the most significant non-parametric methods for classification and regression, making it one of the most important data science topics ever.
  • A data scientist should be able to, among other things, locate neighbors, apply classification criteria, and choose k. K-nearest neighbor is one of the most significant text mining and anomaly detection techniques.

7. Naive Bayes

  • Based on the so-called Bayes Theorem, a class of classification algorithms known as Naive Bayes has been developed.
  • Spam detection and document classification are only two of the several crucial applications of naive Bayes in machine learning.
  • Numerous Naive Bayes implementations exist. The most frequently employed of these are the Multinomial Naive Bayes, Bernoulli Naive Bayes, and Binarized Multinomial Naive Bayes.

8. Classification and Regression trees (CART)

  • Machine learning algorithms for predictive modeling heavily rely on decision tree algorithms.
  • In data mining, statistics, and machine learning, the decision tree—also referred to as a regression tree or a classification tree—is one of the most frequently used predictive modeling tools. In the form of trees, it produces classification or regression models.
  • They can be utilized with both continuous and categorical data.
  • Classification trees, regression trees, interactive differential predictor, C4.5, C5.5, decision stump, conditional decision tree, M5, and other terms and concepts in this area should be familiar to you.

9. Logistic Regression

  • Logistic regression, which, like linear regression, looks at the relationship between a trustworthy and an independent variable, is one of the earliest data science concepts and fields.
  • The dependent variable for our logistic regression study is a dichotomous variable, though (binary).
  • Among the terms, you’ll come across are sigmoid function, S-shaped curve, multiple logistic regression with categorical explanatory variables, multiple binary logistic regression with a combination of categorical and continuous predictors, etc.

10. Neural Network

Nowadays, neural networks are very successful in machine learning. Artificial neural networks, often known as neural networks, are systems of hardware and/or software that mimic how human brain neurons work.

The creation of systems that can be trained to recognize specific data patterns and carry out tasks like classification, regression, prediction, and other similar activities is the basic goal of constructing an artificial neural network.

Neural networks, a kind of deep learning technology, are used to address complex signal processing and pattern recognition problems. The crucial words in this context are neural networks, perceptrons, back-propagation, and Hopfield networks.

Advanced-Data Science topics

  •  Comparative analysis
  • Organization policies
  • Cluster analysis 
  • Time series 
  • Forecasting using regression
  •  Methods of blending
  • Timing indicators and financial modeling
  • Fraud detection 
  • Hadoop, MapReduce, and Pregel in data engineering.
  • GIS and spatial data

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