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Data Science vs. Data Analytics

Explore the key differences between data science and data analytics. Enroll in a data science course in Dubai to gain expertise in data-driven decision-making.<br>

Sowmeeya
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Data Science vs. Data Analytics

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  1. Data Science vs. Data Analytics Data is paramount in today's business world. Yet, "Data Science" and "Data Analytics" are often confused. This presentation clarifies these distinct fields. We will define roles, skills, and applications of each. You will gain insights into their core differences.

  2. Defining Data Analytics: Understanding the Past Data Collection Descriptive Statistics Gathering data from databases, spreadsheets, and CRM Calculating means and standard deviations to summarize systems. data. Data Cleaning Data Visualization Ensuring accuracy and consistency by handling missing Creating charts and dashboards using Tableau or Power BI. values. Data analytics focuses on examining historical data. The goal is to understand past performance. For instance, sales data can show trends and areas for improvement.

  3. Defining Data Science: Predicting the Future Predictive Modeling Machine Learning Statistical Analysis Data Science predicts future outcomes. Key activities include predictive modeling. Machine learning automates decision-making. Statistical analysis validates models. Experiment design evaluates strategies. Churn models predict customer subscription cancellations.

  4. Core Skills: Data Analytics vs. Data Science Data Analytics Data Science • SQL • Python or R • Excel • Machine Learning • Tableau/Power BI • Statistical Modeling • Basic Statistics • Big Data Technologies • Communication Skills • Advanced Visualization Data Analytics uses SQL and Excel. It needs data visualization and basic statistics skills. Data Science requires Python or R, machine learning, and statistical modeling. Big data technologies are also essential.

  5. Tools of the Trade: A Comparison Excel Foundational for data SQL Essential for querying and manipulation and analysis. managing data. Tableau/Power BI Dominant for creating interactive dashboards. Data Analytics uses Excel, SQL, and Tableau. Data Science uses Python, R, and Hadoop. Cloud platforms like AWS are also used. Python has libraries like Pandas and TensorFlow.

  6. Applications in Business: Real-World Examples Marketing Analyzing campaign performance and customer segmentation. Sales Identifying sales trends and optimizing pricing strategies. Finance Detecting fraud and managing risk. Data Analytics is used in marketing and sales. It optimizes supply chains. Data Science powers recommendation systems and fraud detection. It also offers predictive maintenance.

  7. Career Paths: Opportunities and Outlook $60K-$100K Data Analyst Stable demand across industries. $90K-$150K+ Data Scientist High demand with rapid AI growth. Data Analysts include business and financial analysts. Data Scientists can be machine learning engineers. They can also become AI researchers. Demand for both roles continues to grow.

  8. Summary: Key Takeaways and Future Trends Key Differences Focus, skills, tools, and Increasing Convergence The fields are becoming more applications vary. integrated. Emerging Trends AutoML and data ethics are on the rise. Data analytics and data science are vital for data-driven organizations. AutoML democratizes data science, while data ethics and AI gain focus. Enroll in a data science course in Dubai to stay ahead.

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