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Reading list

Machine Learning Basics for a Newbie
6 Steps of Machine learning Lifecycle Introduction to Predictive Modeling
Introduction to Exploratory Data Analysis & Data Insights Descriptive Statistics Inferential Statistics How to Understand Population Distributions?
Reading Data Files into Python Different Variable Datatypes
Probability for Data Science Basic Concepts of Probability Axioms of Probability Conditional Probability
Central Tendencies for Continuous Variables Spread of Data KDE plots for Continuous Variable Overview of Distribution for Continuous variables Normal Distribution Skewed Distribution Skeweness and Kurtosis Distribution for Continuous Variable
Central Tendencies for Categorical Variables Understanding Discrete Distributions Performing EDA on Categorical Variables
Dealing with Missing Values Understanding Outliers Identifying Outliers in Data Outlier Detection in Python Outliers Detection Using IQR, Z-score, LOF and DBSCAN
Sample and Population Central Limit Theorem Confidence Interval and Margin of Error
Bivariate Analysis Introduction
Covariance Pearson Correlation Spearman's Correlation & Kendall's Tau Correlation versus Causation Tabular and Graphical methods for Bivariate Analysis Performing Bivariate Analysis on Continuous-Continuous Variables
Tabular and Graphical methods for Continuous-Categorical Variables Introduction to Hypothesis Testing P-value Two sample Z-test T-test T-test vs Z-test Performing Bivariate Analysis on Continuous-Catagorical variables
Chi-Squares Test Bivariate Analysis on Categorical Categorical Variables
Multivariate Analysis A Comprehensive Guide to Data Exploration The Data Science behind IPL
Supervised Learning vs Unsupervised Learning Reinforcement Learning Generative and Descriminative Models Parametric and Non Parametric model
Machine Learning Pipeline Preparing Dataset Build a Benchmark Model: Regression Build a Benchmark Model: Classification
Evaluation Metrics for Machine Learning Everyone should know Confusion Matrix Accuracy Precision and Recall AUC-ROC Log Loss R2 and Adjusted R2
Dealing with Missing Values Replacing Missing Values Imputing Missing Values in Data Working with Categorical Variables Working with Outliers Preprocessing Data for Model Building
Understanding Cost Function Understanding Gradient Descent Math Behind Gradient Descent Assumptions of Linear Regression Implement Linear Regression from Scratch Train Linear Regression in Python Implementing Linear Regression in R Diagnosing Residual Plots in Linear Regression Models Generalized Linear Models Introduction to Logistic Regression Odds Ratio Implementing Logistic Regression from Scratch Introduction to Scikit-learn in Python Train Logistic Regression in python Multiclass using Logistic Regression How to use Multinomial and Ordinal Logistic Regression in R ? Challenges with Linear Regression Introduction to Regularisation Implementing Regularisation Ridge Regression Lasso Regression
Introduction to K Nearest Neighbours Determining the Right Value of K in KNN Implement KNN from Scratch Implement KNN in Python
Bias Variance Tradeoff Introduction to Overfitting and Underfitting Visualizing Overfitting and Underfitting Selecting the Right Model What is Validation? Hold-Out Validation Understanding K Fold Cross Validation
Introduction to Feature Selection Feature Selection Algorithms Missing Value Ratio Low Variance Filter High Correlation Filter Backward Feature Elimination Forward Feature Selection Implement Feature Selection in Python Implement Feature Selection in R
Introduction to Decision Tree Purity in Decision Tree Terminologies Related to Decision Tree How to Select Best Split Point in Decision Tree? Chi-Squares Information Gain Reduction in Variance Optimizing Performance of Decision Tree Train Decision Tree using Scikit Learn Pruning of Decision Trees
Introduction to Feature Engineering Feature Transformation Feature Scaling Feature Engineering Frequency Encoding Automated Feature Engineering: Feature Tools
Introduction to Naive Bayes Conditional Probability and Bayes Theorem Introduction to Bayesian Adjustment Rating: The Incredible Concept Behind Online Ratings! Working of Naive Bayes Math behind Naive Bayes Types of Naive Bayes Implementation of Naive Bayes
Understanding how to solve Multiclass and Multilabled Classification Problem Evaluation Metrics: Multi Class Classification
Introduction to Ensemble Techniques Basic Ensemble Techniques Implementing Basic Ensemble Techniques Finding Optimal Weights of Ensemble Learner using Neural Network Why Ensemble Models Work well?
Introduction to Stacking Implementing Stacking Variants of Stacking Implementing Variants of Stacking Introduction to Blending Bootstrap Sampling Introduction to Random Sampling Hyper-parameters of Random Forest Implementing Random Forest Out-of-Bag (OOB) Score in the Random Forest IPL Team Win Prediction Project Using Machine Learning Introduction to Boosting Gradient Boosting Algorithm Math behind GBM Implementing GBM in python Regularized Greedy Forests Extreme Gradient Boosting Implementing XGBM in python Tuning Hyperparameters of XGBoost in Python Implement XGBM in R/H2O Adaptive Boosting Implementing Adaptive Boosing LightGBM Implementing LightGBM in Python Catboost Implementing Catboost in Python
Different Hyperparameter Tuning methods Implementing Different Hyperparameter Tuning methods GridsearchCV RandomizedsearchCV Bayesian Optimization for Hyperparameter Tuning Hyperopt
Understanding SVM Algorithm SVM Kernels In-depth Intuition and Practical Implementation SVM Kernel Tricks Kernels and Hyperparameters in SVM Implementing SVM from Scratch in Python and R
Introduction to Principal Component Analysis Steps to Perform Principal Compound Analysis Computation of Covariance Matrix Finding Eigenvectors and Eigenvalues Implementing PCA in python Visualizing PCA A Brief Introduction to Linear Discriminant Analysis Introduction to Factor Analysis
Introduction to Clustering Applications of Clustering Evaluation Metrics for Clustering Understanding K-Means Implementation of K-Means in Python Implementation of K-Means in R Choosing Right Value for K Profiling Market Segments using K-Means Clustering Hierarchical Clustering Implementation of Hierarchial Clustering DBSCAN Defining Similarity between clusters Build Better and Accurate Clusters with Gaussian Mixture Models
Understand Basics of Recommendation Engine with Case Study
8 Ways to Improve Accuracy of Machine Learning Models
Introduction to Dask Working with CuML
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to AutoML Implementation of MLBox Introduction to PyCaret TPOT Auto-Sklearn EvalML
Pickle and Joblib Introduction to Model Deployment
Deploying Machine Learning Model using Streamlit Deploying ML Models in Docker Deploy Using Streamlit Deploy on Heroku Deploy Using Netlify Introduction to Amazon Sagemaker Setting up Amazon SageMaker Using SageMaker Endpoint to Generate Inference Deploy on Microsoft Azure Cloud Introduction to Flask for Model Deploying ML model using Flask
Model Deployment in Android Model Deployment in Iphone

Data Science vs Statistics – Top 7 Differences

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Analytics Vidhya Last Updated : 02 Nov, 2023
10 min read

Introduction

With an increase in postings for data scientists on Indeed by 256%, data science has become an industry buzzword. A growing need for data science roles in various fields has led to the masses opting for specialized degrees and training programs in data science. Businesses and governments extensively use data to make essential choices and plan future investments and activities. However, in data science, steps in statistics contribute equally to decision-making.

Wondering which one is more useful – Data Science vs Statistics?

Let’s explore!

Table of contents

Top 7 Differences Between Data Science and Statistics

AspectData ScienceStatistics
DefinitionData science is an interdisciplinary field that combines statistics, computer science, and technology to extract valuable insights from large volumes of data. It involves converting real-life problems into research projects and using statistical analysis, machine learning algorithms, and computational tools to make data-driven decisions.Statistics is a mathematical science that involves analyzing existing data to solve specific problems. It focuses on applying statistical tools and techniques to data, interpreting the results, and presenting them in a way that aids decision-making. Statistics is often used in data science as a modified application to analyze smaller sampled data and determine cause-and-effect relationships.
ConceptData science aims to identify underlying trends and patterns in data to drive decision-making. It can work with datasets of any size, including big data, and handles both quantitative and qualitative data. Key steps include data mining, data pre-processing, exploratory data analysis, and model building and optimization. Techniques such as regression and classification are commonly used.Statistics focuses on determining cause-and-effect relationships in analyzed data through a purely mathematical approach. It typically analyzes smaller sampled data and is primarily applied to quantitative data. Key terms include mean, median, mode, standard deviation, and variance. Techniques like probability distribution, acceptance sampling, and statistical quality control are commonly used.
Application AreasData science finds application in specialized areas such as computer vision, natural language processing, disaster management, recommender systems, search engines, and more. It is employed in industries like healthcare, finance, e-commerce, marketing, and engineering.Statistics is applied in various industries where random variations are observed in sampled data. It finds applications in areas such as medicine, information technology, economics, engineering, finance, marketing, accounting, and business. It is commonly used for tasks like market research, financial analysis, economic forecasting, and quality control.
ApproachData science focuses on identifying the best modeling technique by evaluating the predicted accuracy of different machine learning models. Data scientists compare multiple models before selecting the most accurate one.Statistics typically begins with a simple model, such as linear regression, to analyze data and check its consistency against the model hypothesis. The approach involves building on the best-fitting basic model rather than comparing multiple models as in data science.
Career OptionsData science offers a range of clearly defined roles, including data scientist, data analyst, data architect, data engineer, and database manager. There has been a rising demand for data science professionals in recent years, and salaries can range from $60,000 to $110,000 per year, depending on experience and seniority.Statistics offers various roles, such as market researcher, financial analyst, business analyst, economist, and database administrator. These roles have always been in demand globally, even before the popularity of data science. Salaries typically range from $75,000 to $100,000 per year, varying based on responsibilities and experience within an organization.
Skill SetsData science requires a degree in data science or a related field, along with a solid understanding of algorithms. Knowledge of statistics, mathematics, and analytical skills is crucial. Proficiency in programming languages like Python, R, C/C++, and Java is necessary, as well as soft skills like teamwork, communication, and problem-solving abilities.Statistics requires a degree in statistics or mathematics. Excellent mathematical skills, including knowledge of calculus, linear algebra, and probability, are essential. Fluency in tools like Excel, SAS, SPSS, and Minitab is necessary for statistical analysis, and basic knowledge of programming languages like Python can be beneficial. Communication and planning skills are also important for a statistician.
Real-world ApplicationsData science finds applications in various domains, including healthcare, computer vision, retail and e-commerce, banking and finance for fraud detection, aviation for flight planning, manufacturing for predictive maintenance, transportation and logistics for fleet management, and chatbots.Statistics has real-life applications in areas such as the stock market, weather forecasting, sports and sporting events, research, public administration, business, consumer goods, insurance, and disaster prevention, among others.

Data Science vs Statistics – Definition

What is Data Science?

Data science is an analysis of data to gain essential business insights. It comprises several disciplines, such as statistics, artificial intelligence, mathematics, and computer science. These help analyze enormous volumes of data. Data scientists utilize their knowledge to find solutions to the issue, why it occurred, what can be expected, and what else can be achieved.

Many industries today use Data Science to forecast consumer patterns and trends and to discover novel prospects. It helps businesses make well-informed decisions regarding product development and sales. It functions as a discipline for process improvement and fraud detection. Governments also use data science to increase the efficiency of their public services.

What is Statistics?

The applied science of statistics involves gathering and examining data to discover patterns and trends, eliminate biases, and help with decision-making. It is a feature of business intelligence that comprises gathering and analyzing commercial data and presenting trends.

Businesses may use statistical evaluation to benefit in many ways, such as identifying the best-performing product lines, identifying underperforming salespeople, and understanding how revenue growth varies across different regions.

Predictive modeling can benefit from the use of statistical analysis methods. Statistical analysis tools enable businesses to look deeper to view more critical details, compared to showing simple trend projections that various external events can influence.

Data Science vs Statistics – Concept

Statistics is a discipline of mathematics concerned with gathering, evaluating, interpreting, displaying, and structuring data. It focuses on creating statistical models and approaches for deriving significant findings from data. Statistics relies on retrieving trends from data, testing hypotheses, and probabilistic analysis.

Statistics can be used in several ways. The statisticians gather data and run analyses on them. Their main objective is to analyze data and offer solutions and insights to help with decision-making. Statisticians evaluate data and conclude using mathematical formulas and statistical models. Statistician employs math for quantitative analysis, even though they can work on various topics with multiple datasets.

On the other hand, the broad subject of data science integrates statistical analysis, computer programming, machine learning, and industry expertise to draw insights, trends, and information from challenging and enormous datasets. Data science uses various methods, instruments, and algorithms to process, examine, and display data to address real-world challenges and reach data-driven conclusions.

Data scientists specialize in developing technologies that carry out these analyses while offering valuable outcomes. Prominent data scientists focus on enormous amounts of data. They have to figure out how to get useful data from data warehouses. While statisticians concentrate more on the equations and mathematical frameworks they utilize for their research, they also actively develop and use data systems.

Data Science vs Statistics – Application

Applications of Statistics

  1. Statistics is essential in setting up studies, conducting surveys, and analyzing data in research projects across various disciplines as diverse as social sciences, economics, psychology, and medicine.
  2. Using statistical methods, such as control graphs, testing for hypotheses, and analysis of variance (ANOVA), diverse businesses may ensure consistency, find errors, and boost overall productivity.
  3. Statistics come in handy in economics and finance to study financial markets, conduct risk analyses, determine the value of assets, and forecast economic indicators. It benefits prediction, risk management, portfolio optimization, and wise investment choices.
  4. The planning and evaluation of clinical trials to ascertain the efficacy and safety of novel treatments or medications depend heavily on statistics. Additionally, it is used in healthcare to assess patient data, conduct epidemiology research, find patterns in disease, and assess the efficacy of therapies.

Application of Data Science

  1. Machine learning algorithms are used in data science to create precise categorization and prediction models. It is utilized in various fields, including demand forecasting, recommendation systems, credit scoring, and fraud detection.
  2. NLP processes and analyzes human language data using data science. It is the driving force behind programs that perform sentiment analysis, text categorization, chatbots, translated languages, and data retrieval.
  3. Data science examines market trends, consumer behavior, and social media data. It enables companies to undertake sentiment analysis, target marketing campaigns, and improve ad campaigns by providing insights into consumer tastes.
  4. With tools like distributed computing, data mining, and scalable algorithms, data science is vital for managing and analyzing large-scale and complicated datasets to identify patterns and insights.
  5. Data Science strategies are used in computer vision applications such as object detection, segmentation of images, face recognition, and video analysis. It makes it possible for programs like surveillance systems, driverless vehicles, and imaging in medicine.

Data Science vs Statistics –  Analyzing and Interpreting Data

The majority of the time, statistics works with well-organized, structured datasets. Researchers prioritize the appropriate formulation of experiments or sampling approaches to ensure accurate data gathering. Furthermore, they also clean, arrange, and transform the data to fit into specific statistical models.

The main goal of statistics is to interpret data in light of statistical models and presumptions. To gauge the degree of evidence, it offers p-values, ranges of confidence, and indicators of uncertainty. Making inferences regarding the population using sample data is a common aspect of statistical interpretation.

Statistics vs. Data Science in data interpretation
Source: Big2smart

Data Science, moreover, deals with large, diversified datasets, including structured and unstructured data. Data scientists routinely conduct data cleanup, preliminary processing, and feature engineering activities to prepare data for the study. It also combines and collects data from various sources, such as written content, visual content, and sensor data, to comprehensively understand the facts.

Besides statistical inference, data science seeks to glean insights that can be applied to action. Data scientists incorporate subject expertise and business goals when interpreting data in a larger context. They concentrate on obtaining significant patterns, detecting trends, formulating predictions, and creating data-driven solutions to challenges encountered in the real world.

Data Science vs Statistics – Statistical Modeling and Hypothesis Testing

In statistics, the emphasis is on creating and using formal statistical models based on accepted concepts. Parametric models, which have established presumptions about the statistical characteristics of the underlying data, are often employed by statisticians. They use statistical techniques to tailor such models to the data, including time series, ANOVA, logistic regression, and linear regression.

Statistical modeling is approached more broadly in data science. Modeling strategies used by data scientists include statistical methodologies, machine learning algorithms, and deep learning models.

Data Science is less concerned with conforming to predefined assumptions and choosing and optimizing models that result in optimal prediction performance. Data scientists often deal with challenging, raw, or high-dimensional data, needing modeling techniques that are more adaptable and reliable.

Based on the research topic, statisticians create null and alternative hypotheses and run statistical tests to assess the evidence supporting the alternative views. To ascertain the statistical importance of the results, they compute test statistics, p-values, and confidence ranges. The application of robust statistical techniques is highlighted by statisticians when drawing inferences about the population from sample data.

Though it can assess model performance, hypothesis testing isn’t necessarily the main goal in Data Science workflows. Data scientists create models that effectively classify observations or forecast outcomes using statistical methods and machine learning algorithms. Metrics measure the model’s effectiveness, including accuracy, precision, recall, and F1 score.

Different Tools Used in Statistics and Data Science

Tools and Techniques Used in Statistics

  1. Using SPSS in social sciences because it offers extensive statistical processes for data analysis and management.
  2. Using SAS by several businesses because it provides broad statistical analysis and data management capabilities.
  3. Stata has extensive data management, economic analysis, and graphical functions.
  4. Spreadsheet programs like Google Sheets and Microsoft Excel are commonly utilized for statistical computations and data analysis.
  5. The typesetting program LaTeX is widely used in academia and research to produce papers and reports of the highest quality that contain mathematical equations, formulas, and statistical notation.
  6. Tableau supports engaging and interactive visualizations, dashboards, and reports.
  7. To perform numerical and statistical computations, programming languages such as Julia, MATLAB, and Python, with libraries like NumPy, pandas, and SciPy, are used for advanced statistical functions.

Tools and Techniques Used in Data Science

  1. Python offers libraries and frameworks for data analysis, deep learning, ML, and data manipulation, such as NumPy, pandas, scikit-learn, TensorFlow, and PyTorch.
  2. R is a comprehensive programming language with tools and packages like dplyr, tidy, ggplot2, caret, and Keras for data analysis, presentation, and manipulation.
  3. Jupyter is a well-known open-source online interactive computational platform. Using Jupyter for experimental data analysis, designing prototypes, and outlining data analysis workflows.
  4. Pandas is a Python package that offers data structures and functions for swift data manipulation, cleaning, and analysis.
  5. TensorFlow is an open-source ML toolkit. It helps create and implement deep learning and ML models for time series evaluation, image recognition, and NLP.
  6. Apache Hadoop is an open-source platform that enables the dispersed storage and analysis of massive datasets across multiple computers.
  7. Plotly is a dynamic data visualization toolkit that works with Python, R, and JavaScript, allowing online interactive charts, dashboards, and visualizations to be created.

Career Paths and Opportunities

Data scientists collaborate in various sectors and professions, such as developing computer systems, company management, and businesses, consultancy work for management and research and technical disciplines, insurance, and others.

Cloud computing is also a growing domain for data scientists, which helps small and medium-sized organizations access the benefits of data science. Individuals can have different career paths, including data scientists, business analysts, data analysts, data engineers, machine learning engineers, data architects, and more.

Career paths and opportunities | Data Science vs Statistics | statistician vs data scientist
Source: Springboard

Almost all facets of industry and government employ statistical professionals, and businesses engage in promotional activities, advisory services, medical services, engineering, political issues, and commercial and college sports.

When individuals master statistics, they can hold different organizational positions, such as statistician, econometrician, research analyst, actuary, statistical consultant, quantitative analyst, and more.

Also Read: How can a Statistician Become a Data Scientist?

Education and Learning Paths

Data science degrees emphasize data analysis, machine learning, statistical concepts, and advanced programming expertise. Students learn how to develop novel types of data models and data operations through these programs. Additionally, students learn to use cutting-edge technologies to track, handle, and visualize massive datasets. The curriculum includes learning Python, SQL, R, and predictive modeling.

With a statistics degree, you can learn how to gather, arrange, analyze, and interpret numbers to solve business problems. The curriculum often incorporates calculus, mathematical concepts, and statistics, such as modeling statistical data. On the contrary, most data science jobs require a bachelor’s degree in computer science, statistics, or a related field. For senior position, candidates with PhDs or Master’s degrees are more suitable.

If you’re considering a career in statistics, you should have a curriculum that includes math and science in high school or college. A solid background in mathematics can help you prepare for this professional path, as statistics is all about numbers.

Conclusion

In conclusion, the modern world operates on data, as do large corporations, which use data for product creation, design, and marketing. Thus, in regards to data science vs statistics, statistics focuses on predictive statistics and statistical frameworks to analyze and understand data mathematically. While on the other hand, data Science employs a broader strategy, integrating statistical methodologies with technologies like machine learning to comprehend massive datasets.

If you are into statistics and want to make a career in Data Science, we have you covered. Our Blackbelt Plus program is designed for professionals who want to build their careers in this field. With 1:1 mentorship, 50+ guided projects, and basic to advanced topics and assignments, we ensure our learners flourish in the field. Explore the program today!

Frequently Asked Questions

Q1. Can a fresher become a data scientist?

A. There are several opportunities for freshers with a graduate or postgraduate degree in computer science, analytics, science, or math.

Q2. What are the branches of data science?

A. The significant disciplines that are the subparts of data science are machine learning, artificial intelligence, data mining, and data analytics.

Q3. Which stream is best for statisticians?

A. Aspiring statisticians would benefit if they studied the science or commerce field, including math, the fundamental of statistics.

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