Data Science Topics you need to know



Without a doubt, data science topics and areas are among the most common business topics today.

Marketers, C-level executives, financiers, and others, in addition to data analysts and business intelligence experts, want to improve their data skills and knowledge.

Data science and data processing, machine learning, artificial intelligence, neural networks, and other fields all fall under the umbrella of data world.

On this page, we’ve compiled a list of basic and advanced data science topics to help you figure out where you should focus your efforts.

Furthermore, they are trending topics that you can use as a guide to help you prepare for data science work interview questions.

MUST READ: Why Data Science is Important?

1. Data Mining

This is just one example of a broad data science topic.

Data mining is an iterative procedure for identifying trends in large data sets. Machine learning, statistics, database systems, and other approaches and techniques are included.

The two main goals of data mining are to identify patterns in a dataset and to create trends and relationships in order to solve problems.

Problem specification, data discovery, data planning, modelling, assessment, and implementation are the general stages of the data mining process.
Classification, forecasts, association laws, data reduction, data discovery, supervised and unsupervised learning, dataset organization, sampling from datasets, constructing a model, and so on are all words used in data mining.

data mining process

2. Data visualization

The presentation of data in a graphical format is known as data visualisation.

It allows all levels of decision-makers to see data and analytics displayed visually, allowing them to spot valuable patterns or trends.

Another broad topic is data visualisation, which includes the interpretation and application of basic graph forms (such as line graphs, bar graphs, scatter plots, histograms, box and whisker plots, and heatmaps).

These graphs are indispensable. You must also learn about multidimensional variables, like adding variables and using colours, scale, shapes, and animations.

Manipulation is also a factor here. Data should be able to be rascaled, zoomed, filtered, and aggregated. Using advanced visualisations like map charts and tree maps is also a desirable ability.

Data visualization

3. Dimension reduction methods and techniques

The dimension reduction method entails transforming a large data set into a smaller dataset that offers equivalent information in a shorter amount of time.

In other words, dimensionality reduction is a set of machine learning and statistics techniques and methods for reducing the number of random variables.
Dimension reduction can be accomplished using a variety of methods and techniques.

Missing Values, Low Variance, Decision Trees, Random Forest, High Correlation, Factor Analysis, Principal Component Analysis, and Backward Feature Elimination are among the most common.

4. Classification

A central data mining technique for assigning categories to a collection of data is classification.

The aim is to aid in the collection of reliable data analysis and forecasts.

One of the most important techniques for effectively analysing a large number of datasets is classification.

One of the hottest data science subjects is classification. A data scientist should be able to solve various business problems using classification algorithms.

This involves understanding how to identify a classification problem, visualise data using univariate and bivariate visualisation, extract and prepare data, construct classification models, and evaluate models, among other things. Some of the main concepts here are linear and non-linear classifiers.

5. Simple and multiple linear regression

For analysing relationships between an independent variable X and a dependent variable Y, linear regression models are one of the most basic statistical models.

It’s a form of mathematical modelling that allows you to make predictions and prognoses about the value of Y based on various X values.

Simple linear regression models and multiple linear regression models are the two major forms of linear regression.

Words like correlation coefficient, regression line, residual plot, linear regression equation, and so on are important. See some basic linear regression examples to get started.

6. K-nearest neighbor

The N-nearest-neighbor algorithm is a data classification algorithm that determines how likely a data point is to belong to one of several groups. It depends on the distance between the data point and the group.
k-NN is one of the best data science topics ever since it is one of the most important non-parametric methods used for regression and classification.
A data scientist should be able to determine neighbours, use classification rules, and choose k, to name a few skills. One of the most important text mining and anomaly detection algorithms is K-nearest neighbour.

7. Naive Bayes

The term “Naive Bayes” refers to a group of classification algorithms based on the Bayes Theorem.
Naive Bayes is a machine learning technique that has a number of important uses, including spam detection and document classification.
There are various Naive Bayes variants. Multinomial Naive Bayes, Bernoulli Naive Bayes, and Binarized Multinomial Naive Bayes are the most common.

8. Classification and regression trees (CART)

Decision trees algorithms play an important role in predictive modelling and machine learning algorithms.

The decision tree is a predictive modelling technique used in data mining, statistics, and machine learning that constructs classification or regression models in the form of a tree (hence the names regression and classification trees and decision trees).

They can be used for both categorical and continuous data.

CART decision tree methodology, classification trees, regression trees, interactive dihotomiser, C4.5, C5.5, decision stump, conditional decision tree, M5, and other terms and topics you should be familiar with in this area.

9. Logistic regression

Logistic regression, like linear regression, is one of the oldest data science topics and fields, and it explores the relationship between dependable and independent variables.

However, when the dependent variable is dichotomous, we use logistic regression analysis (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 words can be encountered.

10.Neural Networks

Nowadays, neural networks are a huge success in machine learning. Neural networks (also known as artificial neural networks) are hardware and software systems that simulate the functioning of human brain neurons.

The primary aim of developing an artificial neuron system is to develop systems that can be trained to learn data patterns and perform functions such as classification, regression, prediction, and so on.

Deep learning technologies such as neural networks are used to solve complex signal processing and pattern recognition problems. The key words here are perceptron, back-propagation, and Hopfield Network, which all contribute to the definition and structure of Neural Networks.

Advanced-Data Science Topics

The topics listed above are some of the fundamentals of data science. Here’s a list of more advanced topics:

  • Discriminant analysis
  • Association rules
  • Cluster analysis
  • Time series
  • Regression-based forecasting
  • Smoothing methods
  • Time stamps and financial modeling
  • Fraud detection
  • Data engineering – Hadoop, MapReduce, Pregel.
  • GIS and spatial data

What are your favorite subjects in data science? Leave a comment with your thoughts.


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