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Python for Data Science | Python Data Science Tutorial | Data Science Certification | Edureka
The document provides an overview of data science, highlighting its need for handling large datasets, making faster decisions, and gaining business insights. It explains the role of Python in data science, including its libraries such as NumPy and pandas for data manipulation, as well as an introduction to machine learning concepts like supervised, unsupervised, and reinforcement learning. A demonstration of logistic regression as a machine learning application is also included.
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Introduction to the necessity of Data Science then and now, focusing on handling large datasets, better decision-making, and gaining business insights.
Definition of Data Science, methods, and processes for extracting insights, along with its role in the data life cycle.
Overview of Python's simplicity, versatility, high-level nature, and libraries for data manipulation, analysis, and machine learning.
Introduction to data manipulation, highlighting efficient data extraction and transformation using libraries like NumPy and Pandas.
Introduction to Machine Learning as a subset of AI, focusing on software's ability to learn from data for accurate predictions.
Classification of machine learning into Supervised, Unsupervised, and Reinforcement Learning methodologies.
Details on Supervised Learning, logistic regression algorithms, and a practical demo related to predicting consumer interest.
Classification of machine learning into Supervised, Unsupervised, and Reinforcement Learning methodologies.
Focus on Unsupervised Learning and clustering techniques such as Hierarchical and K-Means Clustering.
Introduction to Reinforcement Learning, explaining concepts of agent learning through past experiences and actions.
Quick recap of the presentation covering all the main topics discussed in the session.
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Python for Data Science | Python Data Science Tutorial | Data Science Certification | Edureka
- 1. Copyright © 2018,edureka and/or its affiliates. All rights reserved.
- 2. Copyright © 2018,edureka and/or its affiliates. All rights reserved. Agenda Need of Data Science01 What Is Data Science?02 How Python Is Used For Data Science?03 Data Manipulation In Python04 Implement Machine Learning Using Python05 Demo06
- 3. Copyright © 2018,edureka and/or its affiliates. All rights reserved. Need Of Data Science
- 4. Copyright © 2018,edureka and/or its affiliates. All rights reserved. Need Of Data Science THEN NOW THEN ❖ To handle and analyze extremely large datasets/ data flow ❖ Faster & better Decision making ❖ No Predictions ❖ Reduce Production Cost ❖ Gain business Insights ❖ Build intelligence & ability in machines
- 5. Copyright © 2018,edureka and/or its affiliates. All rights reserved. What Is Data Science?
- 6. Copyright © 2018,edureka and/or its affiliates. All rights reserved. What Is Data Science? Data Science, known as data driven science makes use of scientific methods, processes, algorithms and systems to extract knowledge or insights with the goal to discover hidden patterns from the raw data.
- 7. Copyright © 2018,edureka and/or its affiliates. All rights reserved. Data Life Cycle Data Scientist provides a ONE STOP SOLUTION for all these operations
- 8. Copyright © 2018,edureka and/or its affiliates. All rights reserved. Programming Languages For Data Science Python
- 9. Copyright © 2018,edureka and/or its affiliates. All rights reserved. Programming Languages For Data Science Python
- 10. Copyright © 2018,edureka and/or its affiliates. All rights reserved. Python For Data Science
- 11. Copyright © 2018,edureka and/or its affiliates. All rights reserved. Python For Data Science 01 Python It is simple and easy to learn
- 12. Copyright © 2018,edureka and/or its affiliates. All rights reserved. Python For Data Science 02 01 Python Fit for many platforms It is simple and easy to learn
- 13. Copyright © 2018,edureka and/or its affiliates. All rights reserved. Python For Data Science 02 03 01 It is high level and interpreted language Python 03 Fit for many platforms It is simple and easy to learn
- 14. Copyright © 2018,edureka and/or its affiliates. All rights reserved. Python For Data Science 02 03 04 01 It is high level and interpreted language Fit for many platforms It is simple and easy to learn Python 03 04 Perform data manipulation, analysis and visualization
- 15. Copyright © 2018,edureka and/or its affiliates. All rights reserved. Python For Data Science 02 04 05 01 Powerful libraries for Machine learning applications & other scientific computations Perform data manipulation, analysis and visualization It is high level and interpreted language Fit for many platforms It is simple and easy to learn Python 0303 04
- 16. Copyright © 2018,edureka and/or its affiliates. All rights reserved. Data Manipulation
- 17. Copyright © 2018,edureka and/or its affiliates. All rights reserved. Data Manipulation Using data manipulation, you can extract, filter and transform your data quickly and efficiently. NumPy Pandas LIBRARIES USED:
- 18. Copyright © 2018,edureka and/or its affiliates. All rights reserved. NumPy & Pandas NumPy is a Python package which stands for ‘Numerical Python conda install numpy import numpy NumPy Pandas is built on top of NumPy. It is used for data manipulation and analysis. Pandas conda install pandas import pandas
- 19. Copyright © 2018,edureka and/or its affiliates. All rights reserved. Demo: Basic Operations
- 20. Copyright © 2018,edureka and/or its affiliates. All rights reserved. Overview of Machine Learning
- 21. Copyright © 2018,edureka and/or its affiliates. All rights reserved. Machine Learning Machine learning is a type of Artificial Intelligence that allows software applications to learn from the data and become more accurate in predicting outcomes without human intervention.
- 22. Copyright © 2018,edureka and/or its affiliates. All rights reserved. Types Of Machine Learning 1 2 3 Supervised Learning Unsupervised Learning Reinforcement Learning
- 23. Copyright © 2018,edureka and/or its affiliates. All rights reserved. Types Of Machine Learning 1 2 3 Supervised Learning Unsupervised Learning Reinforcement Learning
- 24. Copyright © 2018,edureka and/or its affiliates. All rights reserved. Supervised Learning Supervised Learning is where you have input variable (X) and output variable (Y) and you use an algorithm to learn the mapping function from the input to the output. Y = f(X) Linear Regression Logistic Regression Decision Tree Random forest Naïve Bayes Classifier ALGORITHMS:
- 25. Copyright © 2018,edureka and/or its affiliates. All rights reserved. Demo: Logistic Regression
- 26. Copyright © 2018,edureka and/or its affiliates. All rights reserved. Unknown value of variable or the variable to be predicted Logistic Regression ❑ Logistic regression produces results in a binary format ❑ Used to predict outcome of a categorical dependent variable ❑ Outputs – Yes/ no, true/ false, high/ low, pass/ fail Y = a + bX Dependent Variable It is a point at which the line cuts the y- axis Y intercept known variable or the variable related to dependent variable Independent Variable It is the tangent angle made by the line Slope Relation Between Dependent & Independent variable:
- 27. Copyright © 2018,edureka and/or its affiliates. All rights reserved. Demo: Logistic Regression A car company has released a new SUV in the market. Using the previous data about the sales of their SUV’s, they want to predict the category of people who might be interested in buying this. PROBLEM STATEMENT
- 28. Copyright © 2018,edureka and/or its affiliates. All rights reserved. Types Of Machine Learning 1 2 3 Supervised Learning Unsupervised Learning Reinforcement Learning
- 29. Copyright © 2018,edureka and/or its affiliates. All rights reserved. Unsupervised Learning Unsupervised Learning is the training of a model using information that is neither classified or labelled. Unsupervised learning is also called as clustering analysis. Hierarchical ClusteringK- Means Clustering ALGORITHMS:
- 30. Copyright © 2018,edureka and/or its affiliates. All rights reserved. Types Of Machine Learning 1 2 3 Supervised Learning Unsupervised Learning Reinforcement Learning
- 31. Copyright © 2018,edureka and/or its affiliates. All rights reserved. Reinforcement Learning It is an area of machine learning where a RL agent learns from the consequences of its actions, rather than from being taught explicitly. It selects its actions on basis of its past experiences (exploitation) and also by new choices (exploration). Q- learning ALGORITHMS: SARSA DQN
- 32. Session In AMinute Need Of Data Science Python For Data ScienceWhat is Data Science? Data Manipulation DemoImplement ML Unsupervised Learning Reinforcement Learning Supervised Learning
- 33. Copyright © 2017,edureka and/or its affiliates. All rights reserved.