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Do you need to become a Ph.D. in mathematics or statistics to become a data scientist? No, you do not. The extent to which you need to know depends on what role you want to take up in the organization. This chapter gives you an idea of all the concepts you need to know if you want to become a data scientist. As a data scientist, you first need to understand the basic concepts of probability theory and statistics. If you want to master different statistics and mathematics algorithms, you need to practice them regularly. You need to follow a top-down approach if you want to learn how to code. You need to learn how to use a data stack, familiarize yourself with real-world projects and use libraries and documentation. When you notice your lack of theoretical background, you can see the bigger picture. It is important to learn how these algorithms work. Start off with the mathematical aspects before you move onto those areas, which are more complex. This chapter has the list of statistical and mathematical concepts you need to learn.

A Naïve Bayes classifier is an algorithm which uses the principles of probability. One of the most important principles it uses is that the value of one feature in the data set is independent from the value of other features in the data set. According to this theorem, you can predict the probability that an event will occur based on the conditions of the occurrence of that event. It is important to learn how these classifiers work, and to do this, you need to study the basics and principles of conditional probability and probability. One of the easiest ways to understand this theorem is to study the concepts of probability. You can then check on Bayes' theorem and how to code this theorem.

Linear regression is a basic form of regression, and it allows you to determine the connection between two variables in the data set. One of the variables is termed as the predictor variable and the other is known as the response variable. These variables are often continuous in nature. A simple linear regression model uses various data point sets and then extrapolates the trend of the values and how they will progress in the future. Linear regression is a common algorithm used in parametric machine learning, and in this form of learning, you can train the machine to develop a mathematical function using existing data sets. You can use this function to make predictions or forecasts about future events. These functions are termed as models in machine learning terminology. The best way to learn more about regression is to develop an understanding of elementary statistics. If you want to learn more about regression and the various concepts, you need to take an advanced course of statistics.

Logistic regression is a process sued in different cases where you have a binary dependent variable. This form of regression focuses on the estimation of probability of the occurrence of an event. Logistic regression is also an algorithm used in parametric machine learning. Like linear regression, this algorithm also results in a mathematical function. The difference between these forms of regression is that logistic regression leads to developing a mathematical function that estimates the values based on their logarithms or other statistical functions. Another difference between linear and logistic regression is that the former uses real numbers and gives those as the output. The latter uses a model which provides the logistic function. A logistics function produced is also termed as a sigmoid function that takes care of the values to give you an output between 0 and 1. Let us understand why a sigmoid function returns a probability value. It is because of the algebraic concepts used in the algorithms that a negative exponent is taken.

A neural network is a form of machine learning algorithm. These forms of networks are based on the structure of the neurons in the brain. The neural network uses a series of activation points or weights to help you make the necessary predictions. The neurons take the input, apply the transformation function and give you an output that helps you make the necessary decisions. When it comes to capturing any nonlinear relationships within the data set, it is best to use a neural network best since it helps in tasks, such as image and audio processing. The fundamental concept of neural network is to transform the input and processing it to generate the output. If you want to understand the math better in a neural network, you need to take up courses to help you learn more about linear algebra and geometry. This is a better way to start preparing. If you want to delve deeper, you need to learn more about graph theory, matrix theory and real analysis.

The k-means clustering algorithm is a type of unsupervised machine learning algorithm. You can use this algorithm to categorize any unlabeled data. This type of data does not have any defined categories or groups, but it works to identify or define any hidden categories in the data set. The number of variables identified by the algorithm are 'k' in number. The algorithm iterates over the various data points in the data set, so every point is assigned to one of many groups. This algorithm depends on the idea that the distance between the data points and the center of the cluster is the same across all data points. You can use any function that shows the distance between the elements in the data set. Suppose you want to work with this algorithm. In that case, you need to understand the basics of algebra, including addition and subtraction to identify the distance between the data points easily. If you want to delve deeper, you need to learn about Euclidean and non-Euclidean geometry.

Decision trees are an easy way to determine the outcome of any decision you take using a flowchart. The flowchart is in the form of a tree structure that uses a branching method. Every node in the decision tree is based on a specific variable and every branch is an image of the outcome. A decision tree is based on information theory, since this is how they are constructed. An important concept of a decision tree is known as entropy. Entropy is a measure of uncertainty in any variable. When you use entropy, you can construct a decision tree easily. When there is higher entropy, there is more uncertainty in the data. this means you need to split the tree in a way to decrease any uncertainty.

Information gain helps you determine how much information you can gain from someone. When it comes to a decision tree, you can calculate the information gain on every column based on previous columns based on the information present in the columns in the data set.

Here is a small tip – you need to learn basic algebra and probability to know what decision trees are and how they work. If you want to learn more about decision trees, you need to learn about logarithms and probability.

Mathematics and statistics are difficult subjects, and they can feel daunting and dry at times. You will, however, feel equipped when you compare your skills against the skills of your peers. It is also important to learn how you can apply these concepts on various data science problems.

The topics highlighted in this book will need to be read well and understood. This is the only way you can develop and build algorithms. You can rely on different machine learning libraries and tools to complete this work for you. It is useful for a data scientist to understand statistics and math, which helps you determine what is happening in the tools or algorithms. This allows you to choose the algorithm that works best for your data set and allow you to make the necessary predictions.

If you want to work as a data scientist, you need to have some mathematical skills. This is the only way you can understand data and the significance of the patterns in the data set. These are important data science skills since you use them to build and develop models and perform predictive analysis and hypothesis testing. Data scientists use math to develop decision models and they can use different mathematical algorithms to predict the future. This book explains some concepts you need to learn and the skills you need to develop if you want to become a data scientist.

If you are a data scientist, you need to understand the basics of statistics. You should understand the significance of data, develop hypotheses and validate them by simulating certain scenarios. This makes it easier for you to predict futuristic events. It is very difficult to develop advanced statistical skills, but if you are keen about pursuing a career in data science, you need to understand some basics of logistic regression, linear regression, time series analysis, Bayes classification, etc.

Understanding programming languages and coding in various languages, especially R and Python, are important for data science. You need to learn how to write code to instruct the machine on how it should process, manipulate, analyze and visualize the data. It is important to understand how to write code in R and Python. There are different libraries and functions in these languages that allow you to manipulate, analyze and visualize the analysis of the data set.

You can also use SQL to run queries on the data set that allows you to extract and modify data in the database. People also use JavaScript library to develop interactive, dynamic and custom visualizations on the Internet. To do this, you need to learn to write code in R or python. It is important to learn to code if you want to become a data scientist, and these are easy languages to learn.