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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 Data Analytics: Which One Will Give You the Edge in 2023?

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Analytics Vidhya Last Updated : 31 May, 2023
8 min read

Data Science and Data Analytics are two interrelated fields that have become increasingly important in today’s data-driven world. This article will explore the differences and similarities between these two fields and provide real-world examples of their applications. Find out which career is better for you: Data Science vs Data Analytics!

Table of contents

Data Science vs Data Analytics

Data Science is centered around discovering meaningful connections within extensive datasets, while Data Analytics focuses on extracting detailed insights from the data. In conclsuion, Data Analytics can be seen as a specialized branch of Data Science that emphasizes obtaining specific answers to the questions that arise from the field of Data Science.

Data ScienceData Analytics
It requires detailed knowledge of programming languages.It requires basic knowledge of programming languages.
Data science utilizes machine learning algorithms.Data analytics does not utilize machine learning algorithms.
It deals with unstructured data sets.It mostly deals with structured data sets.
The scope of data science is at a macro level.The scope of data analytics is at a micro level.
The goal of data science is to deal with new innovations and explorations.The goal of data analytics is to deal with the existing resources to create further data.

What is Data Science?

Data Science is a multidisciplinary field that uses statistical, mathematical, and computer science techniques to extract insights and knowledge from large and complex data sets. It aims to answer questions and solve problems that may arise in the future by examining vast amounts of structured and unstructured data. Data science is applied in various fields, such as healthcare, finance, marketing, and operations management, and is constantly evolving with the vast amount of data generated daily.

What is Data Science
Source: Slide Team

What is Data Analytics?

Data Analytics involves statistically analyzing existing datasets to solve today’s challenges. It aims to present outcomes that lead to immediate improvements. Data analytics integrates various types of data to identify linkages and streamline findings. In contrast, Data Science deals with unorganized data and focuses on new developments and projections. Data analytics uses organized, previously collected data to find patterns and conclusions. Although Data Science is a more general phrase, it incorporates Data Analytics to discover new ideas and address analytically challenging business problems using advanced techniques like machine learning and predictive algorithms.

what is Data Analytics
Source: AACC

Key Components of Data Science

Here are the key components of Data Science:

Statistics

Statistics are vital in the daily activities of a Data Scientist, and it can be considered one of the necessary talents for the Data Scientist. More simply, statistics studies through a collection of data and seeks out significant trends. It has two sections: descriptive statistics, which attempts to explain trends in existing information, and inferential statistics, which strives to formulate projections about future information.

Machine Learning

Machine Learning is the most critical aspect of data science because it enables self-learning algorithms to forecast natural behavioral patterns in specific settings with the greatest accuracy. The process of employing algorithms to gather data, gain insight into it, and then predict future patterns for a subject is known as machine learning. 

Data Visualization

Data visualization entails comprehending the raw records and determining what needs to be shown depending on the objectives and goals of those using it and the processes, not only delivering the processed data effectively. It enhances findings, speeds up decision-making, increases the capacity to hold consumers’ attention, evolves away with the requirements for data scientists, and makes it easier to distribute the generated information. 

Also Read: Here are the top Data Science Innovations of 2023

Key Components of Data Analytics

The major components of data analytics are as follows:

Data Mining

Data mining is an essential component of data analytics as a whole. It is among the fundamental fields in data science because it uses the latest analytics methods to unveil the important aspects of data sets. Effective analytics projects within businesses depend on data mining. The data produces real-time analytics systems that analyze streaming data as it is created or collected, as well as business intelligence (BI) and advanced analytics programs that evaluate and present previous data.

Data Modeling

In order to maximize insights and ensure steady data usage, data modeling is the technique of mapping and visualizing the entire technique underlying the gathering, enhancing, and storing of business information. Organizations use data modeling to explain the different types of data they use, the connections between them, and ways to organize it. Data modeling is a method for optimizing data to enhance how data flows throughout organizations for varying business needs.

Data Cleaning

The most significant resource for machine learning and analytics is data. Whether in business or the computer software industry, data is required. Data cleaning, also referred to as data cleansing or data wrangling, is a critical first stage in the data analytics workflow. Data cleaning is more than eliminating incorrect data; it is usually a component.

Examples of Data Science vs Data Analytics

To address the question of data science vs data analytics, let us look at some real-life examples where these can be used and understand the difference between them:

Data Science in Industries

1. Healthcare

Healthcare practitioners use data science, particularly in medical imaging, to help patients receive better diagnoses and treatments. Several cutting-edge healthcare analytics technologies produce clinical data for bettering patient care. These resources also help develop patient-specific drugs, lowering operating expenses for hospitals and medical centers.

2. Weather Forecasting 

Data science has a fascinating significance for weather forecasting. Climate change affects various industries such as aviation, agriculture, building, consumer products, athletic events, etc. Most of a data scientist’s tasks involve making short-term forecasts, determining whether a forecast is accurate, and validating models using satellite imagery. Machine Learning helps in this situation for matching patterns. Sensor data can generate local forecasts regarding current weather models using reliable equipment.

Data Analytics in Industries

1. Advertising 

Today, studying a user’s historical behavior is the primary emphasis of digital advertising. Another transformation of data analytics is digital advertising. The majority of advertising sources currently, including interactive billboards in towns and internet banners, employ data analytics and data algorithms. It represents one factor contributing to digital adverts receiving higher CTRs than conventional advertising methods.

2. Transportation 

Data analytics optimizes making purchasing decisions easier for travelers by using data from social networking sites that indicate the habits and goals of different consumers. Using data gathered from networking sites will also assist businesses in customizing their particular packages and offers, enhancing customized travel suggestions.

Checkout: 10 things you should know about Analytics related Career

Skill Set and Education Requirement for Data Science

  • A master’s or doctoral degree in a field relating to analytics
  • Having expertise in the industry, you might be employed in, such as product development or financing
  • Advanced modeling abilities in mathematics
  • AI, deep learning,machine learning(ML) and data analytics
  • Big data tools for manipulation, for example, MySQL, TensorFlow and Apache Spark
  • Expert level of knowledge of programming languages like Python, R programming, JavaScript, C and C++
  • The skills to handle structured and raw data sets.

Skills Set and Educational Requirement for Data Analytics

  • An undergraduate degree in a field connected to data
  • Solid statistical and probability knowledge
  • Strong leadership and communication abilities (for working with teams)
  • Problem-solving and analytical approach
  • Knowledge of relational database systems and different kinds of data systems
  • Skills in handling data and processing
  • having experience with business intelligence and modeling software, such as Power BI or Tableau
  • Knowledge of SQL,Python, R, and MS Excel is required.
  • Developing strategies and solutions for enhancing processes.

Responsibilities of a Data Scientist

  • Collecting data and locating reliable data sources
  • Preparation of the structured and unstructured data groups
  • Examining vast and complicated data to find overlooked trends and patterns
  • Developing and utilizing multiple machine-learning techniques and predictive models
  • Combining models with ensemble modeling
  • To showcase information and findings, perform great data visualization.
  • Create solutions and provide answers to difficult business problems.
  • Communicate and organize with various teams, such as engineering departments and product development groups.

Responsibilities of a Data Analyst

  • Utilize statistical methods and software to examine data and produce results.
  • Utilizing database management technologies and working with databases
  • Gathering information from a variety of reliable primary and secondary sources
  • Keeping up with and administering data systems
  • Examining, translating, and detecting patterns or hidden trends in large, complicated datasets
  • Data cleaning and filtering procedures
  • Recognizing management issues and needs
  • Developing methods and techniques for enhancing processes.

Also Read: How to create a High performance Analytics team?

Similarities Between Data Analysis and Data Science

Data Science and Data Analytics share several similarities:

  1. Data-driven approach: Both fields rely on the analysis and interpretation of data to derive insights and make informed decisions.
  2. Use of statistical techniques: Both Data Science and Data Analytics employ statistical methods and techniques to analyze data and extract meaningful patterns and relationships.
  3. Data exploration: Both fields involve exploring and examining large datasets to identify trends, patterns, and anomalies.
  4. Programming skills: Data Science and Data Analytics require proficiency in programming languages such as Python or R to manipulate and analyze data effectively.
  5. Data visualization: Both fields utilize data visualization techniques to present findings and insights in a visual and easily understandable format.
  6. Machine learning: Both Data Science and Data Analytics leverage machine learning algorithms and techniques to develop predictive models and gain deeper insights from the data.
  7. Business impact: Both fields aim to provide actionable insights that can drive decision-making and add value to businesses or organizations.

Make the Right Career Choice

Data science vs data analytics has always been a hot topic. The question lies in which one is better and has more career opportunities. Data science and data analytics have equal importance worldwide and would make a great career. Understanding the difference between data science and data analytics will help you make the best choice. Analytics Vidhya offers various courses to help you excel in your desired field of choice and have a great career ahead.

Frequently Asked Questions

Q1. Which is better, data analyst or data scientist?

A. The answer to this question depends on one’s interests, skills, and career goals. Both roles have unique responsibilities, and it’s up to individuals to choose the one that suits them the best.

Q2. Are data analysis and data science the same?

A. No, data analysis and data science are not the same. Data analysis refers to inspecting, cleaning, transforming, and modeling data to discover useful information, conclusions, and supporting decision-making. On the other hand, data science has a broader role that includes data analysis but also involves building predictive models and algorithms, developing machine learning models, and conducting statistical analysis.

Q3. Can a data scientist be a data analyst?

A. Yes, a data scientist can be a data analyst. Data analysis is a crucial part of a data scientist’s role. They need to possess data analysis skills to conduct exploratory data analysis, clean and preprocess data, and interpret the results of their models.

Q4. Is a data scientist above a data analyst?

A. No, a data scientist is not necessarily above data analyst. Both roles are essential, and their level of seniority depends on their specific job responsibilities, industry, and organization.

Q5. Who gets paid more, data scientist or data analyst?

A. Generally, data scientists get paid more than data analysts due to the more specialized skill set required for the role. However, compensation varies based on the individual’s experience, industry, and organization.

Q6. Does data analysis require coding?

A. Data analysis typically requires coding skills to conduct data analysis tasks efficiently. Basic programming skills in languages like Python or R can help data analysts manipulate, transform, and analyze data effectively.

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