Basic statistics concepts to Learn to start your Data Science Career

Statistics Concepts is used widely by Data scientists around the world to perform their daily tasks because of its uniqueness in problem-solving.

Statistics is a genre of mathematics that uses quantified models and representation for a given set of Data.

Top statistics concepts for Data Science

1. Descriptive statistics
2. Inferential statistics
3. Hypothesis testing
4. Probability distributions
5. Regression analysis
6. Bayesian statistics
7. Data visualization
8. Cluster analysis
9. Machine learning
10. Time series analysis
11. Sampling methods
12. Outlier detection
13. Data preprocessing
14. Dimensionality reduction
15. Cross-validation
16. Classification
17. Clustering
18. Ensemble methods
19. Deep learning
20. Ethics and bias in data science

Descriptive statistics: These are statistical methods used to summarize and describe the basic features of a dataset, such as mean, median, mode, variance, standard deviation, etc.

Inferential statistics: These are statistical methods used to conclude a larger population based on a sample from that population.

Hypothesis testing: This is a statistical method used to test whether a hypothesis is true or false based on the observed data.

Probability distributions: These are mathematical functions that describe the likelihood of different outcomes in a random experiment.

Regression analysis: This is a statistical method used to model the relationship between a dependent variable and one or more independent variables.

Bayesian statistics: This is a statistical framework that uses Bayes’ theorem to update beliefs or probabilities based on new evidence or data.

Data visualization: This is the graphical representation of data that can help to identify patterns, trends, and outliers.

Machine learning: This is a field of artificial intelligence that uses statistical methods and algorithms to enable machines to learn from data and make predictions or decisions.

Cluster analysis: This is a statistical method used to group similar observations or data points based on their characteristics or attributes.

Time series analysis: This is a statistical method used to analyze data that varies over time, such as stock prices, weather data, or website traffic.

Sampling methods: These are techniques used to select a representative subset of a population for data analysis, which can help reduce the cost and time of data collection.

Outlier detection: This is the process of identifying and dealing with data points that are significantly different from the rest of the dataset, which can skew statistical analysis results.

Data preprocessing: This refers to the various techniques used to prepare data for analysis, including cleaning, normalization, feature selection, and transformation.

Dimensionality reduction: This is the process of reducing the number of variables or features in a dataset, which can help simplify analysis and improve model performance.

Cross-validation: This is a technique used to evaluate the performance of a machine learning model by splitting the data into training and testing sets, and repeatedly testing the model on different subsets of the data.

Classification: This is a type of machine learning problem where the goal is to assign a categorical label or class to a new observation based on its features.

Clustering: This is a type of unsupervised machine learning problem where the goal is to group similar observations based on their characteristics, without a predefined set of labels.

Ensemble methods: These are machine learning algorithms that combine the predictions of multiple models to improve performance, such as bagging, boosting, and stacking.

Deep learning: This is a type of machine learning that uses neural networks to model complex relationships in data, and has been particularly successful in tasks such as image recognition and natural language processing.

Ethics and bias in data science: These are important considerations in data science, as data and algorithms can perpetuate or amplify societal biases, and have real-world implications for individuals and communities.

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