STATISTICS is The Most Essential Part of Data Science, Machine Learning, MLOps and data engineering discipilines.

In this Age of AI and Generative AI, data is being generated and used at an unprecedented rate to make the world more exciting to live.

Someone needs to process that data, extract the insights from that data and make predictions for better outcomes using Machine Learning and Deep Learning. To process, extract insights and predict outcomes, we need statistics.

How?

Let’s find out:

First of ALL.

What is Statistics?

Statistics is the process of using data to understand the world around us. It involves collecting data, summarizing it and using it to make predictions about the population from where data is extracted.

For Example: An organization might use statistics to understand the demographics of its employees, the effectiveness of their work or factors that lead to enhanced work performance.

Role of Statistics in Data Science

The core of data science is Machine Learning and deep learning which uses algorithms which in turn are based on Statistics. Data science problems cannot be solved if you do not have a grasp of statistical concepts.

Without any doubt, Statistics can be hard for some people and some people are innately professional due to their previous experience. Hard concepts include complex mathematical notations, greek notation and complicated equations that make it hard to develop an interest in the subject.

But this complexity can be addressed with simple, clear and concise explanations of those concepts by leveraging some profound books and courses that are mentioned in the resources portion of this article.

From Exploratory data analysis to choosing a machine learning algorithm to designing hypothesis testing experiments, statistics is a must-have for anyone diving into data analysis, data science, data engineering and working with LLMs.

Why Should You Ace Statistics?

Data is the new currency of the world that is mandatory for the smallest of companies to the largest organizations to manage tasks, use insights and predict the incoming outcomes for better business and work.

Statistics help answer these questions:

• What features are important in raw data?

• What features can make a better model?

• How should we measure the performance of that model?

• What are already known outcomes and what we can achieve more?

• How can we fine-tune the model to make it more efficient?

Statistics in Data Science Project Lifecycle

Statistics is involved in every step of the data science project lifecycle. Here is how:

Defining the Problem

The most basic yet important part of the data science project lifecycle is defining the problem. Because the most important part of predictive modelling is understanding the problem and carefully defining it.

Precisely defining the problem helps in deciding what kind of problem we would be dealing with and what techniques we can use during the next steps of the cycle.

However, problem defining is not straightforward because most of the time, the problem is not laid out until we explore the data. So for beginners, it may require them to be somewhat proficient in the EDA(Exploratory Data Analysis).

Exploring the Data

Data Exploration involves data collection and gaining a deep understanding of the distribution of data variables and their relationship with other variables.

Here, if you are proficient in a specific domain that comes in handy because you can already have an idea what kind of data variables you will be dealing with. For Example, if someone is from a finance background, he/she might not need to google the variables in the data like credit, FICO Score etc.

Statistic concepts that are used here, are descriptive statistics.

Data Cleaning and Preprocessing

Often the data we are given or collected is not very useful for conducting experiments on it. For example: there could be missing values, data errors (from bad observations of devices) and unformatted data(Observation of different scales).

Data Cleaning requires outlier detection and missing value imputation from statistics.

Further, Data preprocessing is used to make data available in a confined structure that would be useful for model selection.

Data processing can be done efficiently if you have a good grasp of data sampling, feature selections, scaling and encoding.

Model Selection and Evaluation

First, to predict an outcome, a model needs to be selected and that model’s evaluation for its learning methods. In the field of statistics, Experimental design is a subfield that deals with the selection and evaluation process of models which requires a profound understanding of Statistical hypothesis tests and estimation statistics.

Building and Fine-Tuning Model

Once the model is selected, data is cleaned data is pipelined to that machine learning algorithm to test different hypotheses. Keep in mind that every machine learning model has hyperparameters that enable the data scientist to completely Fine-tune for a better prediction of outcomes.

Complete the Statistics course outline for All levels (Beginners to Advanced)

Beginner Level

Module 1: Introduction to Statistics

• What is statistics?

• Types of data

• Descriptive statistics

• Inferential statistics

Module 2: Probability

• Basic probability concepts

• Probability distributions

• Bayes’ theorem

Module 3: Hypothesis Testing

• Null and alternative hypotheses

• Type I and Type II errors

• Common statistical tests

Intermediate Level

Module 4: Linear Regression

• Simple linear regression

• Multiple linear regression

• Model evaluation

Module 5: Logistic Regression

• Logistic regression basics

• Model interpretation

• Applications in data science

Module 6: Decision Trees

• Decision tree algorithms

• Model selection and evaluation

• Applications in data science

Advanced Level

Module 7: Clustering

• Clustering algorithms

• Model selection and evaluation

• Applications in data science

Module 8: Time Series Analysis

• Time series forecasting models

• Model evaluation

• Applications in data science

Module 9: Natural Language Processing

• NLP basics

• Statistical methods for NLP

• Applications in data science

Practical Learning Tips for Statistics

The top-down technique and the bottom-up approach are the two basic ways to learn statistics for data science.

Top-Down Method

The top-down strategy includes starting with a broad understanding of statistics before delving further into the particular concepts and techniques required for data science. For those who are already proficient in other branches of mathematics, such as calculus and linear algebra, this method works well. You can start by enrolling in a general statistics course or reading a general statistics book to learn statistics for data science utilizing the top-down method. You can start learning more advanced statistical techniques for data science, such as machine learning and natural language processing, once you have a fundamental foundation in statistics.

Bottom-Up Method

The bottom-up strategy includes beginning with the precise statistical techniques required for data science and working your way up to a broader understanding of statistics. For those who are unfamiliar with other branches of mathematics, this method works well.

You can begin by enrolling in a data science course or reading a data science textbook to learn statistics for data science utilizing the bottom-up method. With the help of these resources, you may typically learn the practical statistical techniques required for data science. You can start learning more about the fundamentals of statistical approaches for data science once you have mastered the fundamentals.

Learning Resouces

Books

**Statistics for Data Science *by *James D. Miller

• It provides a comprehensive introduction to statistics for data science. This book is easy to follow and is well-written and covers a wide range of topics for statistical understanding.

**The Elements of Statistical Learning** by Trevor Hastie, Jerome Friedman, and Robert Tibshirani

• It is a little bit more advanced book on statistics that covers a wide range of machine-learning algorithms. The book is more biased towards a theoretical understanding of concepts but it is also easy to follow.

**Naked Statistics *by *Charles Wheelan

• This book is for people who dread mathematics and can only learn through practical examples of real-life scenarios. You can follow it with The Elements of Statistical Learning, which would make a perfect combination.

**Statistical Learning with Python** by Gareth James, Daniela Witten, Trevor Hastie, and Jonathan Taylor

• This Book covers advanced statistical deep-learning topics along with NLP concepts. That makes you stand out in your data science landscape.

Courses

If you are a Course learning maze, You are covered with these courses mentioned below:

• **Introduction to Statistics for Data Science** by Stanford University on Coursera

• **Statistics with Python** by the University of Michigan on Coursera

• **Mathematics for Machine Learning and Data Science** by *DeepLearning.AI on Coursera*

Final Thoughts

The ability to use statistics is crucial for data scientists. You will be able to make sense of the enormous volumes of data that you will come across at work by mastering statistics. You may find a variety of tools to assist you in learning statistics for data science, so pick the strategy and learning resources that suit you best and get started learning right away.

I will be creating more on these topics in future. So follow for more valuable insights and projects on data science, deep learning and artificial intelligence, especially NLP.