Data Science Skool

1-1 Anaconda

1-2 Data Types with Python

1-3 Basic Operators and Functions

1-4 Key Takeaways

2-1 Introduction to Numpy

2-2 Demo 01-creating and Printing an Ndarray

2-3 Basic Operations

2-4 Mathematical Functions of Numpy

2-5 Assignment 01 Demo

2-6 Assignment 02 Demo

2-7 Key Takeaways

3-1 Introduction to Scipy_2

3-2 Demo 01-creating and Printing an Ndarray_2

3-3 Demo - Calculate Eigenvalues and Eigenvector

3-4 Scipy Sub Package - Statistics, Weave and Io

3-5 Assignment 01 Demo

3-6 Assignment 02 Demo

3-7 Key Takeaways

4-1 Introduction to Pandas

4-2 Understanding Dataframe

4-3 View and Select Data Demo

4-4 Missing Values

4-5 Data Operations

4-6 File Read and Write Support

4-7 Pandas Sql Operation

4-8 Assignment 01 Demo

4-9 Assignment 02 Demo

4-10 Key Takeaways

5-1 Machine Learning Approach

5-2 Steps 1 and 2

5-3 How It Works

5-4 Steps 3 and 4

5-5 Supervised Learning Model Considerations

5-6 Scikit-learn

5-7 Supervised Learning Models - Linear Regression

5-8 Supervised Learning Models - Logistic Regression

5-9 Unsupervised Learning Models

5-10 Pipeline_1

5-11 Model Persistence and Evaluation

5-12 Assignment 01

5-13 Assignment 02

5-14 Key Takeaways

6-1 Introduction to Data Visualization-mp4_1

6-2 (x,y) Plot and Subplots

6-3 Types of Plots

6-4 Assignment 01 Demo

6-5 Assignment 02 Demo

6-6 Key Takeaways

7-1 Web Scraping and Parsing_1_1

7-2 Navigating a Tree

7-3 Modifying Tree

7-4 Parsing and Printing the Document

7-5 Key Takeaways

8-1 Why Big Data Solutions Are Provided for Python

8-2 Python Integration with Hdfs Using Hadoop Streaming

8-3 Using Hadoop Streaming for Calculating Word Count_1_1

8-4 Python Integration with Spark Using Pyspark

8-5 Using Pyspark to Determine Word Count

8-6 Assignment 01 Demo

8-7 Assignment 02 Demo

8-8 Key Takeaways