Data Science is the field that helps in extracting meaningful insights from data using programming skills, domain knowledge, and mathematical and statistical knowledge. It helps to analyze the raw data and find the hidden patterns.

Therefore, a person should be clear with statistics concepts, machine learning, and a programming language such as Python or R to be successful in this field. In this article, I will share the basic Data Science concepts that one should know before transitioning into the field.

Whether you are a beginner in the field or want to explore more about it or you want to transition into this multifaceted field, this article will help you understand Data Science more by exploring the basic Data Science concepts. 

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Statistics Concepts Needed for Data Science

Statistics make a central part of data science. Statistics is a broad field that offers many applications. Data scientists must know the statistics very well. This can be inferred from the fact that statistics help to interpret and organize data. The descriptive statistics and knowledge of probability are must-know data science concepts.

Below are the basic Statistics concepts that a Data Scientist should know:

1. Descriptive Statistics

Descriptive statistics help to analyze the raw data to find the primary and necessary features from it. Descriptive statistics offers a way to visualize the data to present it in a readable and meaningful way. It is different from inferential statistics as it helps to visualize the data in a meaningful way in the form of plots. Inferential statistics, on the other hand, help in finding insights from data analysis.

2. Probability

Probability is the mathematical branch that determines the likelihood of occurrence of any event in a random experiment. As an example, a toss of a coin predicts the probability of getting a red ball from a bag of colored balls. Probability is a number whose value lies between 0 and 1. The higher the value, the event is more likely to happen.

There are different types of probability, depending upon the type of event. Independent events are the two or more occurrences of an event that are independent of each other. Conditional probability is the probability of occurrence of any event having a relationship with any other event.

3. Dimensionality Reduction

Dimensionality reduction means reducing the dimensions of a data set so that it resolves many problems that do not exist in the lower dimension data. This is because there are many factors in the high dimensional data set and scientists need to create more samples for every combination of features.

This further increases the complexity of data analysis. Therefore, the dimensionality reduction concept resolves all these problems and offers many potential benefits such as lesser redundancy, fast computing, and fewer data to store.

4. Central Tendency

The central tendency of a data set is a single value that describes the complete data by the identification of a central value. There are different ways to measure the central tendency:

• Mean: It is the average value of the data set column.
• Median: It is the central value in the ordered data set.
• Mode: The value repeating most in the data set column.
• Skewness: It measures the symmetry of data distribution and determines if there is a long tail on either or both sides of the normal distribution.
•  Kurtosis: It defines whether the data has a normal distribution or has tails.

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5. Hypothesis Testing

Hypothesis testing is to test the result of a survey. There are two types of hypothesis as part of hypothesis testing viz. Null hypothesis and Alternate Hypothesis. The null hypothesis is the general statement that has no relation to the surveyed phenomenon. The Alternate hypothesis is the contradictory statement of the Null hypothesis.

6. Tests of significance

Test of significance is a set of tests that helps to test the validity of the cited Hypothesis. Below are some of the tests that help in the acceptance or rejection of the Null Hypothesis.

• P-value test: It is the probability value that helps to prove that the null hypothesis is correct or not. If p-value > a, then the Null Hypothesis is correct. If p-value < a, then the Null Hypothesis is False, and we reject it. Here ‘a’ is some significant value which is almost equal to 0.5.
• Z-Test: Z-test is another way of testing the Null Hypothesis statement. It is used when the mean of two populations is different, and either their variances are known, or the size of the sample is large.
• T-test: A t-test is a statistical test that is performed when either the variance of the population is not known or when the size of the sample is small.

7. Sampling theory

Sampling is the part of statistics that involves the data collection, data analysis, and data interpretation of the data which is collected from a random set of population. Under-sampling and oversampling techniques are followed in case we find the data is not good enough to get the interpretations. Under-sampling involves the removal of redundant data, and oversampling is the technique of imitating the naturally existing data sample.

8. Bayesian Statistics

It is the statistical method that is based on the Bayes Theorem. Bayes theorem defines the probability of occurrence of an event depending upon the prior condition related to an event. Therefore, Bayesian Statistics determine the probability based on previous results. Bayes Theorem also defines the conditional probability, which is the probability of occurrence of an event considering certain conditions to be true.

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Machine Learning and Data Modeling

Machine learning is training the machine based on a specific data set with the help of a model. This trained model then makes future predictions. There are two types of machine learning modeling, i.e., supervised and unsupervised. The supervised learning works on structured data where we predict the target variable. The unsupervised machine learning works on unstructured data that has no target field.

Supervised machine learning has two techniques: classification and regression. The classification modeling technique is used when we want the machine to predict the category, while the regression technique determines the number. As an example, predicting the future sale of a car is a regression technique and predicting the occurrence of diabetes in a sample of the population is classification.

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Below are some of the essential terms related to Machine learning that every Machine Learning Engineer and Data Scientist should know:

1. Machine Learning: Machine learning is the subset of artificial intelligence where the machine learns from the previous experience and uses that to make predictions for the future.
2. Machine Learning Model: A Machine Learning model is built to train the machine using some mathematical representation which then makes predictions.
3. Algorithm: The algorithm is the set of rules using which a Machine Learning Model gets created.
4. Regression: Regression is the technique used to determine the relationship between independent and dependent variables. There are various regression techniques used for modeling in machine learning based on the data we have. Linear regression is the basic regression technique.
5. Linear Regression: It is the most basic regression technique used in machine learning. It applies to the data where there is a linear relationship between the predictor and the target variable. Thus, we predict the target variable Y based on the input variable X, both of which are linearly related. The below equation represents the linear regression:

Y=mX + c, where m and c are the coefficients.

There are many other regression techniques, such as Logistic regression, ridge regression, lasso regression, polynomial regression, etc.

1. Classification: Classification is the type of machine learning modeling that predicts the output in the form of a predefined category. Whether a patient will have heart disease or not is an example of a classification technique.
2.  Training set: The training set is part of the data set, which is used to train a machine learning model.
3. Test set: It is part of the data set and has the same structure as the training set and tests the performance of the machine learning model.
4. Feature: It is the predictor variable or an independent variable in the data set.
5. Target: It is the dependent variable in the data set whose value is predicted by the machine learning model.
6. Overfitting: Overfitting is the condition that leads to the overspecialization of the model. It occurs in the case of a complex data set.
7. Regularization: This is the technique used to simplify the model and is a remedy to overfitting.

Basic libraries used in Data Science

Python is the most used language in data science, as it is the most versatile programming language and offers many applications. R is another language used by Data Scientists, but Python is more widely used. Python has a large number of libraries that make the life of a Data Scientist easy. Therefore, every data scientist should know these libraries.

Below are the most used libraries in Data Science:

1. NumPy: It is the basic library used for numerical computations. It is mainly used for data analysis.
2. Pandas: It is the must-know library which is used for data cleaning, data storage, and time series.
3. SciPy: It is another python library which is used to solve differential equations and linear algebra.
4. Matplotlib: It is the data visualization library used to analyze correlation, determine outliers using scatter plot, and to visualize data distribution.
5. TensorFlow: It is used for high-performance computations that reduce error by 50%. It is used for speech, image detection, time series, and video detection.
6. Scikit-Learn: It is used to implement supervised and unsupervised machine learning models.
7. Keras: It runs easily on CPU and GPU, and supports the neural networks.
8. Seaborn: It is another data visualization library used for multi-plot grids, histograms, scatterplots, bar charts, etc.

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Conclusion

Overall, Data Science is a field that is a combination of statistical methods, modeling techniques, and programming knowledge. On the one hand, a data scientist has to analyze the data to get the hidden insights and then apply the various algorithms to create a machine learning model. All this is done using a programming language such as Python or R. 

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