It is no doubt that data science topics and areas are some of the hottest business points today.

Not only data analysts and business intelligence specialists aim to advance their data skills and knowledge but also marketers, C-level managers, financiers, and etc.

Data world is a wide field that covers mathematical and statistics topics for data science and data mining, machine learning, artificial intelligence, neural networks and etc.

On this page, we collected some basic and advanced topics in data science to give you ideas where to master your skills.

Moreover, they are hot subjects you can use as directions to prepare yourself for data science job interview questions.

**1. The core of data mining process**

This is an example of a wide data science topic.

What is it?

Data mining is an iterative process that involves discovering patterns in large data sets. It includes methods and techniques such as machine learning, statistics, database systems and etc.

The two main data mining objectives are to find out patterns and establish trends and relationship in a dataset in order to solve problems.

The general stages of the data mining process are: problem definition, data exploration, data preparation, modeling, evaluation, and deployment.

Core terms related to data mining are classification, predictions, association rules, data reduction, data exploration, supervised and unsupervised learning, datasets organization, sampling from datasets, building a model and etc.

**2. Data visualization**

What is it?

Data visualization is the presentation of data in a graphical format.

It enables decision-makers of all levels to see data and analytics presented visually, so they can identify valuable patterns or trends.

Data visualization is another broad subject that covers the understanding and use of basic types of graphs (such as line graphs, bar graphs, scatter plots, histograms, box and whisker plots, heatmaps.

You cannot go without these graphs. In addition, here you need to learn about multidimensional variables with adding variables and using colors, size, shapes, animations.

Manipulation also plays a role here. You should be able to rascal, zoom, filter, aggregate data.

Using some specialized visualizations such as map charts and tree maps is a hot skill too.

**3. Dimension reduction methods and techniques**

What is it?

Dimension Reduction process involves converting a data set with vast dimensions into a dataset with lesser dimensions ensuring that it provides similar information in short.

In other words, dimensionality reduction consists of series of techniques and methods in machine learning and statistics to decrease the number of random variables.

There are so many methods and techniques to perform dimension reduction.

The most popular of them are Missing Values, Low Variance, Decision Trees, Random Forest, High Correlation, Factor Analysis, Principal Component Analysis, Backward Feature Elimination.

**4. Classification**

What is it?

Classification is a core data mining technique for assigning categories to a set of data.

The purpose is to support gathering accurate analysis and predictions from the data.

Classification is one of the key methods for making the analysis of a large amount of datasets effective.

Classification is one of the hottest data science topics too. A data scientist should know how to use classification algorithms to solve different business problems.

This includes knowing how to define a classification problem, explore data with univariate and bivariate visualization, extract and prepare data, build classification models, evaluate models, and etc. Linear and non-linear classifiers are some of the key terms here.

**5. Simple and multiple linear regression**

What is it?

Linear regression models are among the basic statistical models for studying relationships between an independent variable X and Y dependent variable.

It is a mathematical modeling which allows you to make predictions and prognosis for the value of Y depending on the different values of X.

There are two main types of linear regression: simple linear regression models and multiple linear regression models.

Key points here are terms such as correlation coefficient, regression line, residual plot, linear regression equation and etc. For the beginning, see some simple linear regression examples.

**6. K-nearest neighbor (k-NN) **

What is it?

N-nearest-neighbor is a data classification algorithm that evaluates the likelihood a data point to be a member of one group. It depends on how near the data point is to that group.

As one of the key non-parametric method used for regression and classification, k-NN can be classified as one of the best data science topics ever.

Determining neighbors, using classification rules, choosing k are a few of the skills a data scientist should have. K-nearest neighbor is also one of the key text mining and anomaly detection algorithms.

**7. Naive Bayes**

What is it?

Naive Bayes is a collection of classification algorithms which are based on the so-called Bayes Theorem.

Widely used in Machine Learning, Naive Bayes has some crucial applications such as spam detection and document classification.

There are different Naive Bayes variations. The most popular of them are the Multinomial Naive Bayes, Bernoulli Naive Bayes, and Binarized Multinomial Naive Bayes.

**8. Classification and regression trees (CART)**

What is it?

When it comes to algorithms for predictive modeling machine learning, decision trees algorithms have a vital role.

The decision tree is one of the most popular predictive modeling approaches used in data mining, statistics and machine learning that builds classification or regression models in the shape of a tree (that’s why they are also known as regression and classification trees).

They work for both categorical data and continuous data.

Some terms and topics you should master in this field involve CART decision tree methodology, classification trees, regression trees, interactive dihotomiser, C4.5, C5.5, decision stump, conditional decision tree, M5, and etc.

**9. Logistic regression**

What is it?

Logistic regression is one of the oldest data science topics and areas and as the linear regression, it studies the relationship between dependable and independent variable.

However, we use logistic regression analysis where the dependent variable is dichotomous (binary).

You will face terms such as 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 etc.

**10. Neural Networks**

What is it?

Neural Networks act as a total hit in the machine learning nowadays. Neural networks (also known as artificial neural networks) are systems of hardware and/or software that mimic the human brain neurons operation.

The primary goal of creating a system of artificial neurons is to get systems that can be trained to learn some data patterns and execute functions like classification, regression, prediction and etc.

Neural Networks are a kind of deep learning technologies used for solving complex signal processing and pattern recognition problems. Key terms here relates to concept and structure of Neural Networks, perceptron, Back-propagation, Hopfield Network.

The above were some of the basic data science topics. Here is a list of more interesting and advanced topics:

11. Discriminant analysis

12. Association rules

13. Cluster analysis

14. Time series

15. Regression-based forecasting

16. Smoothing methods

17. Time stamps and financial modeling

18. Fraud detection

19. Data engineering – Hadoop, MapReduce, Pregel.

20. GIS and spatial data

What are your favorite data science topics? Share your thoughts in the comment field above.

Silvia Vylcheva has more than 10 years of experience in the digital marketing world – which gave her a wide business acumen and the ability to identify and understand different customer needs.

Silvia has a passion and knowledge in different business and marketing areas such as inbound methodology, data intelligence, competition research and more.