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Data Analytics With Python | Data Analysis With Python | Data Analytics For Beginners | Simplilearn

Data analytics plays a vital role in every company for making crucial decisions and improving the business. In this presentation, you'll learn Data Analytics using Python. You will see the different applications of Data Analytics and the various types of Data Analytics. You will understand why Python for Data Analytics and deep dive into learning Data Analytics using NumPy, Pandas, and Matplotlib. <br><br>1. What is Data Analytics? <br>2. Applications of Data Analytics <br>3. Types of Data Analytics<br>4. Data Analytics Process Steps <br>5. Why Python for Data Analytics<br>6. Use Case Demo<br><br>Why become Data Analyst?<br>By 2020, the World Economic Forum forecasts that data analysts will be in demand due to increasing data collection and usage. Organizations view data analysis as one of the most crucial future specialties due to the value that can be derived from data. Data is more abundant and accessible than ever in todayu2019s business environment. In fact, 2.5 quintillion bytes of data are created each day. With an ever-increasing skill gap in data analytics, the value of data analysts is continuing to grow, creating a new job and career advancement opportunities. <br><br>The facts are that professionals who enter the Data Science field will have their pick of jobs and enjoy lucrative salaries. According to an IBM report, data and analytics jobs are predicted to increase by 15 percent to 2.72 million jobs by 2020, with the most significant demand for data analysts in finance, insurance, and information technology. Data analysts earn an average pay of $67,377 in 2019 according to Glassdoor.<br><br>Who should take up this course?<br>Aspiring professionals of any educational background with an analytical frame of mind are best suited to pursue the Data Analyst Masteru2019s Program, including:<br>1. IT professionals<br>2. Banking and finance professionals<br>3. Marketing managers<br>4. Sales professionals<br>5. Supply chain network managers<br>6. Beginners in the data analytics domain<br>7. Students in UG/ PG programs<br><br>ud83dudc49Learn more at: https://bit.ly/2SECA5r

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Data Analytics With Python | Data Analysis With Python | Data Analytics For Beginners | Simplilearn

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Presentation Transcript

  1. Data Analytics using Python

  2. What’s in it for you? • What is Data Analytics? • Applications of Data Analytics • Types of Data Analytics • Data Analytics Process Steps • Why Python for Data Analytics • Use Case Demo

  3. What is Data Analytics?

  4. Click here to watch the video Data Analytics using Python

  5. What is Data Analytics? Data Analytics is the process of exploringandanalyzinglarge datasets to make predictions and help data-driven decision making Analyze data Make decisions

  6. Applications of Data Analytics

  7. Applications of Data Analytics Fraud Analysis

  8. Applications of Data Analytics Healthcare

  9. Applications of Data Analytics Applications of Data Analytics Deep Learning frameworks Inventory Management

  10. Applications of Data Analytics Delivery Logistics

  11. Applications of Data Analytics Targeted Marketing

  12. Applications of Data Analytics City Planning

  13. Types of Data Analytics

  14. Types of Data Analytics How can we make it happen? What will happen? Foresight What has happened? Value Descriptive Analytics Predictive Analytics Prescriptive Analytics Insight Hindsight Difficulty

  15. Types of Data Analytics Descriptive Analytics Predictive Analytics Prescriptive Analytics Finding ways to improve the sales and profit Studying the total units of furniture sold and the profit that was made in the past Predicting the total units that would sell and the profit we can expect in the future

  16. Data Analytics Process Steps

  17. Data Analytics Process Steps 5 Step Process 1 5 Data Collection 4 2 3 Result Interpretation Data Preparation Data Modeling Data Exploration

  18. Why Python for Data Analytics?

  19. Why Python Data Analytics? Easy to learn with simple syntax Huge collection of libraries Scalable and flexible Graphics and visualization Community support

  20. Python Libraries

  21. Python libraries for Data Analytics

  22. NumPy for Data Analytics • Supports n-dimensional arrays • Provides numerical computing tools • Useful for Linear algebra and Fourier transform

  23. Pandas for Data Analytics • Useful for handling missing data • Perform mathematical operations • Provides functions to manipulate data

  24. Matplotlib for Data Visualization • Plotting library in Python • Several toolkits extend Matplotlib functionality • Creates interactive visualizations

  25. Demo

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