Difference Between Data Science VS Data Analytics

Data science and data analytics are two related but distinct fields that deal with data collection, analysis, interpretation and application. There is often confusion between these terms as they have emerged rapidly over the last decade and have overlapping concepts. This article will compare data science vs data analytics – their definitions, key differences, processes, required skills and use cases.

Definitions

The field of data science is mainly focused on extracting insights from data sets to solve complex problems. It covers the entire data lifecycle including data access, cleaning, preparation, analysis, modeling, interpretation and visualization. Data scientists utilize sophisticated analytical and machine learning techniques to find patterns, derive meaningful information and predict outcomes from large volumes of structured and unstructured data.

Data Analytics examines raw data to make conclusions and detect patterns. It focuses on techniques like data mining, statistical modeling and quantitative analysis to derive insights and drive fact-based decision making. Data analysts interpret historical data to identify trends, build predictive data models, optimize processes and identify relationships between different data parameters.

Key Differences Between Data Science and Data Analytics

Data science and data analytics have some similarities in the basic data handling process but the differences between them is given below:

Goal: The main goal of data analytics is to provide insights into business performance by analyzing existing data sets to look at historical trends and patterns. Data science has a broader goal to detect hidden patterns and derive actionable insights from large data sets to solve complex problems, guide decision-making and predict future outcomes.
Approach: Data analytics takes a backward-looking approach, focusing on organizing, studying and drawing conclusions from historical data. Data science uses both past and current data to not just report insights but also predict future outcomes through predictive modeling and machine learning algorithms.
Tools: Data analytics relies more on traditional BI tools like SQL, spreadsheet, data visualization and statistical analysis tools. Data science incorporates these but also utilizes more advanced tools for machine learning, predictive modeling, natural language processing etc. like Python, R, TensorFlow, Spark and Hadoop.
Complexity: Data analytics problems tend to be straightforward like sales trend analysis, customer segmentation, revenue forecasts etc. Data science tackles more complex and nuanced problems that require building customized predictive models and algorithms.
Audience: Data analytics is geared more towards business executives and managers who need data insights to evaluate performance and aid in decision making. Data science requires a deeper level of statistical and coding skills to preprocess data, build models and share meaningful results.
Skill Sets: Data analysts need skills in statistics, SQL, data visualization and BI tools. Data scientists need a multidisciplinary skillset – statistics, machine learning, coding, analytics, deep learning, modeling, algorithms, domain expertise etc.

Data Science Process

The data science lifecycle involves a sequence of iterative steps:

Defining the Problem: This involves framing the actual business or research problem to be solved using data science techniques. Clarifying project goals, expected outcomes and success metrics.
Collecting and Storing Data: Identifying relevant structured and unstructured data sources, then collecting, assimilating and storing the data for analysis.
Data Pre-processing: Cleaning the data to handle missing values, duplicate observations, fixing formatting inconsistencies etc. Selecting subsets of data for the analytic task.
Exploratory Data Analysis: Conducting initial investigations on the dataset using summary statistics and visualizations to discover patterns, outliers and relationships in the data.
Feature Engineering: Transforming raw data into features that better represent the underlying problem to be modeled. Engineering new features for better model fitting.
Modeling: Applying various machine learning and statistical models on the prepared data to uncover insights and make predictions. Models include regression, classification, decision tree, random forest, neural nets, clustering etc.
Model Evaluation: Evaluating models using appropriate metrics to assess and compare performance, accuracy and effectiveness at solving the business problem.
Model Optimization: Tuning and optimizing models by tweaking parameters, algorithms and training data to improve performance. Applying ensemble modeling as needed.
Deployment: Deploying the optimized model by integrating it into a production environment like a web/mobile app or enterprise process.
Monitoring and Maintenance: Monitoring the model’s performance to detect decay and implementing re-training as required to ensure continued effectiveness.

Data Analytics Process

The key steps in the data analytics process are:

Define Business Requirements: Clarify the business problem to solve or questions to answer using data analytics. Align stakeholder needs.
Data Collection: Gather relevant structured and unstructured data from company sources like databases, CRM systems, files, social media etc.
Data Processing: Clean, filter, transform and validate data for the analytic task. Handle missing values.
Exploratory Analysis: Conduct preliminary analysis on data using SQL queries, Excel, BI tools and statistical analysis to discover patterns.
Advanced Analytics: Apply more advanced techniques like forecasting, regression, predictive modeling, machine learning algorithms to derive deeper insights from data.
Data Visualization: Visualize data findings using dashboards, reports, charts and graphs to illustrate key trends and insights.
Interpretation: Interpret data analysis results, statistical outputs and visualized trends to derive meaning, summarize conclusions and make recommendations.
Communication: Create presentations, reports and briefings to convey data insights, findings and recommendations to stakeholders for driving business decisions.

Required Skills

Data Science Skills

Mathematics – Calculus, Linear Algebra, Statistics
Programming – Python, R, SQL, Hadoop, Spark
Machine Learning – Algorithms like Regression, Classification, Clustering
Data Visualization – Using tools like Tableau, PowerBI, Matplotlib
Artificial Intelligence – Natural Language Processing, Neural Networks, Robotics
Cloud Computing – AWS, Google Cloud, Azure
Databases – SQL, PostgreSQL, MongoDB, Cassandra
Domain Expertise – Relevant industry/business functional knowledge

Data Analytics Skills

Spreadsheets – Excel, Google Sheets
SQL – Writing complex queries to extract and analyze data
Analytics and Visualization – Using tools like Tableau, Qlik, Microsoft BI
Statistics – Statistical modeling, hypothesis testing, regression
Communication – Report writing and presentations
Attention to Detail – Data validation, accuracy checks
Business Acumen – Understanding workflows and objectives

Use Cases

Data Science Use Cases

Predictive modeling – Predict customer churn, fraud detection, demand forecasting
Price optimization – Optimal pricing strategies using econometric analysis
Algorithmic trading – Discover signals and automate trades using quantitative models
Personalized recommendations – Recommend content, products using collaborative filtering
Sentiment analysis – Automatically analyze customer sentiment from feedback
Image recognition – Facial recognition, medical diagnosis from imaging data
Autonomous vehicles – Self-driving algorithms relying on computer vision and ML
Anomaly detection – Detect fraud, network intrusion using classification techniques

Data Analytics Use Cases

Sales performance – Analyze sales metrics, customer types, product appeal
Marketing analytics – Analyze campaigns, channel performance, competitive data
Web analytics – Analyze user journeys, engagement metrics, funnel optimization
Financial analysis – Analyze profitability, risk factors, portfolio performance
Operational analytics – Analyze supply chain, logistics, manufacturing performance
Healthcare analytics – Clinical metrics, patient health indicators, diagnoses
People analytics – HR metrics like performance, attrition, hiring costs

Data Science VS Data Analytics: Head to Head Comparison

Basis of Comparison	Data Science	Data Analytics
Goal	Detect hidden patterns and insights to solve complex problems, guide decisions and predict future outcomes	Provide insights into business performance by analyzing historical data to identify trends and patterns
Approach	Uses current and historical data to predict future outcomes through predictive modeling and machine learning	Backward-looking approach focused on studying past data to draw conclusions
Tools Used	Programming languages like Python, R, machine learning libraries like TensorFlow, big data platforms like Hadoop and Spark	Spreadsheets, BI tools, SQL, statistical analysis software
Problem Complexity	Tackles complex, nuanced problems requiring customized predictive modeling and algorithms	Straightforward problems like sales forecasting, customer segmentation, trend analysis
Audience	Requires deeper statistical, machine learning and coding skills to preprocess data, train models and interpret results	Tailored more towards executives and managers who need data insights to aid decision making
Skill Sets Required	Statistics, machine learning, programming, modeling, algorithms, deep learning, domain expertise	Statistics, SQL, data visualization, business intelligence tools, communication skills
Process Steps	Defining problem, data collection, cleaning and preprocessing, exploratory analysis, feature engineering, modeling, evaluation, optimization, deployment, monitoring	Define requirements, data collection, processing, exploratory analysis, advanced analytics, visualization, interpretation, communication
Use Cases	Predictive modeling, algorithmic trading, personalized recommendations, anomaly detection, autonomous vehicles	Sales analytics, marketing analytics, financial analysis, operational analytics, healthcare analytics, people analytics

Data Science VS Data Analytics

Conclusion

Data science and data analytics have some overlap, data science tackles more advanced use cases using sophisticated machine learning and predictive modeling techniques. Data analytics focuses more on mining historical data to derive business insights for strategic decisions. Data science requires skills in machine learning, AI, programming and statistics whereas data analytics relies more on analytics skills and business knowledge. Both fields are critical for harnessing the power of data to help drive growth for modern data-driven organizations.

difference between Data Science VS Data Analytics

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Difference Between Data Science VS Data Analytics

Definitions

Key Differences Between Data Science and Data Analytics

Data Science Process

Data Analytics Process