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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

Find Out the Difference Between Big Data and Data Science!

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Analytics Vidhya Last Updated : 06 Feb, 2024
5 min read

It’s estimated that by 2025, global data creation will reach a mind-boggling 463 exabytes per day. As our world becomes increasingly data-driven, the combination of Big Data and Data Science promises exciting new opportunities and breakthroughs in various fields. Big Data vs Data Science can be confusing owing to their operations on data. However, there are numerous differences, each playing a specific role in data handling. Let’s explore the difference between the two!

Big Data vs Data Science

Data Science Big Data
It is a field or domain It is a technique
Collects, processes, analyzes and utilizes data for several operations Extracts data for interpretation
Generates data-based products for businesses Converts data into a usable form
SAS, Scala, Python, R and others Spark, Hadoop, Apache, Flink, MongoDB and others
Scientific purpose Business purposes, specifically for customer satisfaction
Public and company datasets, social media, surveys, Internet of Things devices, transactions and financial reports, system logs Social networking sites, weather stations, share markets, e-commerce sites, telecom companies

What is Data Science?

It is the field of study of the high volume of data used to build prescriptive and predictive analytical models. Data science also differentiates between raw data and useful data. The method includes the usage of ML algorithms, scientific methods, processes and tools. It also involves capturing, analyzing, digging and utilizing the data from the available datasets. Data science combines data, computer science, mathematics, statistics, business and computing.

What is Big Data?

Synonymous with big data, the data excess in quantity is called big data. The data can be in any form, such as information or statistics and is impossible to store in a single computer. Categorizing it further, it can be structured, semi-structured or unstructured. Another criterion describes it as the 5 V’s: variety, value, volume, veracity and value. The high volume of data is crucial for analysis purposes to find unbiased inferences on any topic. The analysis of big data comprises hidden patterns which need to be decoded for better decision-making by organizations.

Big Data
Source: ResearchGate

Uses of Data Science

  • Data science algorithms provide accurate search results based on searched keywords within a shorter time.
  • To provide more targeted ads on digital billboards and display banners
  • Enhances user experience through recommendations based on previous searches
  • In marketing for the promotion of products according to demand

Uses of Big Data

  • Provides financial services for investment companies, banks and wealth-based firms
  • Utilized in domains like fraud, customer, operational and compliance analytics
  • Helps gain subscribers and retain existing customers by analyzing the data
  • Assists retail companies by analysis of weblogs, store-branded credit card data and similar information

Advantages of Data Science

  • Provides a personalized approach to data
  • Enhances decision-making aspect of the business
  • Enables understanding of future trends and outcomes, thus guiding advanced planning
  • Better risk management and mitigation

Advantages of Big Data

  • It is a cost-effective approach owing to efficient analysis and data management.
  • Provides data for utilization and application of advanced analytics and machine learning
  • Processes complex data commonly unmanageable by simple programming
  • Eases excess data interpretation for strategy building and further usage

Disadvantages of Data Science

  • Users must have experience in data visualization, statistical analysis and machine learning.
  • Time-consuming owing to the requirement of preprocessing and data cleaning
  • Ethical concerns while handling sensitive data

Disadvantages of Big Data

  • Only experts and skilled personnel can handle associated tools
  • No provision for security and privacy while handling sensitive data
  • Requires proper management and infrastructure, which is not a budget-friendly option
  • Difficult integration with already in-use processes and systems

Skill Required to Build a Career in Data Science

  • Educational requirements include masters and Ph.D.
  • Required statistical and programming knowledge are SAS or R
  • Ability to execute complex queries via SQL
  • Coding requirements are Python, Java, Perl, C/C++
  • Demonstrated experience in handling
  • Familiarity with Hadoop, hive or Pig data storage platform
https://www.analyticsvidhya.com/blog/2020/11/14-must-have-skills-to-become-a-data-scientist-with-resources/

Skill Required to Build a Career in Big Data

  • Ability to analyze data’s relevancy and curate solutions
  • Capacity to generate methodology for gathering, interpretation and analysis of data strategy.
  • Skill in dealing with numbers, programs and algorithms
  • Familiarity with business objectives, growth, and profits

Also Read: Java vs Python: Which is a Better Language for Data Science?

Career in Big Data vs Data Science

The career in big data vs data science is significantly different in terms of job title and industry. The candidates choosing data science can expect to work at the job titles like data developers, data creatives, data researchers, data scientists, and business intelligence analysts. The big data candidates should probably be hired as data analysts, big data engineers, data strategists, big data visualizers, big data architects and big data engineers.

Moreover, the demand for data science candidates is wide, comprising healthcare, finance, marketing and others. Big data professionals are limited to finance, e-commerce and telecommunications.

Choosing the right career depends on your interests and skill set. Becoming a data scientist is a wonderful career for those who are good team leaders, possess remarkable communication skills, and are adept at building ML models. The big data engineering profession demands individuals who are programmers or experts in data and software.

Exclusive Data Science Roadmap 2023 [Step-wise Guide]

Salary in Big Data vs Data Science

There isn’t a considerable differencebetween big data and data sciencejob earnings. Data scientists earn approximately $117,000 annually, while big data specialists make around $104,000 yearly. Therefore, on comparing this, we can conclude that data scientists stand a stronger chance of landing high-paying job opportunities. However, when we consider senior data scientist and big data analyst salaries, we will note that the two professionals earn almost the same.

Bottomline

The continuous generation of new data from ever-growing sources needs efficient methods to deal with it. Requiring gathering, storage and analysis followed by quality inferences for utilization in business growth, data science, and big data have revolutionized the data industry. Where data science focuses on developing tools and techniques, big data strives to deal with more efficiently. Big data vs data science differ in operations yet find similarities in the information being worked upon.

If you’re interested in mastering the art of Data Science, then our Blackbelt program is an excellent place to start. With expert instructors, hands-on projects, and a supportive community of learners, this program can help you take your Data Science skills to the next level. So why wait? Take the first step towards your Data Science journey and join the Blackbelt program today!

Frequently Asked Questions

Q1. Is big data and data science same?

A. No, big data and data science are not the same. Big data refers to the large volume of data, while data science is extracting insights from data.

Q2. How is big data different from data science?

A. Big data refers to storing, managing, and processing large volumes of data, while data science focuses on analyzing and interpreting data to gain insights and make informed decisions.

Q3. Which pays more big data or data science?

A. Both big data and data science are high-paying fields, with salaries varying depending on the role and the company.

Q4. Does big data require coding?

A. Big data requires coding skills to manage and analyze large datasets effectively.

Q5. Which is difficult data science or big data?

A. Both fields require strong technical skills and can be challenging, but the difficulty level depends on the individual’s background, experience, and the specific tasks they are working on.

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Analytics Vidhya

Analytics Vidhya Content team

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