Differences between Data Science and Data Analytics

Differences between Data Science and Data Analytics

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  Differences Between Data Scienceand Data Analytics PRESENTATION
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  INTRODUCTION Introduction In today’s data-drivenworld, Data Science and Data Analytics are two of the most in- demand fields. Although they are often used interchangeably, these disciplines have distinct roles, responsibilities, tools, and goals. Understanding the difference is essential for students aiming to build a career in the tech industry.
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  What is Data Science? DataScience is a multidisciplinary field that uses scientific methods, algorithms, and machine learning to extract insights from structured and unstructured data. Key Components Typical Job Roles Machine learning and AI Predictive modeling Big Data technologies Programming (Python, R, SQL) Data engineering Data Scientist Machine Learning Engineer AI Specialist Data Engineer
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  What is Data Analytics? DataAnalytics focuses on examining existing data sets to find trends, draw conclusions, and support decision-making processes. Key Components Typical Job Roles Descriptive and diagnostic analytics Statistical analysis Data visualization BI tools (Tableau, Power BI) SQL and Excel proficiency Data Analyst Business Analyst BI Analyst Reporting Analyst
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  DATA Key Differences BetweenData Science and Data Analytics Feature Data Science Data Analytics Goal Skill Set Tools Outcome Data Types Career Path Predict future trends and automate processes Programming, machine learning, modeling Research-focused and technical roles Analyze past data to make decisions Data querying, reporting, visualization Business and decision- making roles Structured, unstructured, semi-structured Primarily structured data Predictive and prescriptive insights Descriptive and diagnostic insights Python, R, TensorFlow, Hadoop Excel, SQL, Power BI, Tableau
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  Use Case Comparison Data ScienceExample: A tech company uses machine learning models to predict customer churn and automate retention strategies. Data Analytics Example: A retail chain uses data dashboards to track monthly sales trends and optimize inventory.
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  FEATURES If you lovemathematics, coding, and solving complex problems, Data Science might be the right path. If you're more interested in business intelligence, storytelling with data, and making informed decisions, Data Analytics could be your best fit. Which One Should You Choose? DATA SCIENCE Data Science is a multidisciplinary field that combines statistics, computer science, and domain knowledge to extract meaningful insights and knowledge from structured and unstructured data. DATA ANALYTICS Data Analytics is the process of examining, cleaning, transforming, and interpreting data to discover useful information, draw conclusions, and support decision-making.
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  SKILLS AND TOOLS YOU’LLNEED For Data Science Python, R, SQL Jupyter Notebook TensorFlow, Keras Apache Spark, Hadoop Advanced Statistics & Machine Learning For Data Analytics Excel, SQL Power BI, Tableau Google Data Studio Statistical Analysis Critical Thinking
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  Final Thoughts Both DataScience and Data Analytics play crucial roles in leveraging data for growth and innovation. While their approaches and objectives differ, both require a solid understanding of data handling and problem-solving. 📝 Tip for Students: Start with foundational courses in statistics and SQL, then explore more advanced topics based on your interest.
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