Data Analysis Using Python

Welcome to APSSDC Data Analysis Using Python Training this repository consists of all the files, resources, and recorded session links which are discussed during the entire training.

APSSDC-ML-Datasets → [Click Here]

Few resources avaliable @ [resources.md] file don't forget to use them

Instructions for attendance

Everyone should compulsory follow the below instruction in order to get the attendance --> Certificate

1. Login format rollnumber-name-college
2. Don't give spaces in roll number or shorcut of your roll number
3. Don't give spaces between rollnumber and name (only - single minus or hyphen character)
4. Make sure roll number should match with the registered roll number
5. Minimum 120 minutes should attend in 150 minutes session with same login format

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Day01 Introduction to Data and Data Analysis Using Python (25-Nov-2020)

Discussed Concepts:

1. Introduction to Data
2. Steps involved in Data Analysis
3. Types of Data in Statistics (Numerical & Categorical)
4. Types of data in real world
5. Introduction to Python
6. Features and Applications of Python
7. Ananconda Software installation for Jupyter Notebook

[Day01_Discussed_Slides]

[Day01_Recorded_Video]

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Day02 Python Overview & Numpy Basics (26-Nov-2020)

Discussed Concepts:

1. Literate Programming
2. Jupyter Notebook Environment
3. Markdown format for documentation
4. Python Overview
   1. input/output
   2. Conditional Statements in python
      • if
      • if else
      • if-elif-else
   3. List
   4. Tuple
   5. Dictionary
   6. Files
   7. Modules & Packages
5. Data Manipulation with NumPy
   1. Introduction
   2. NumPy Arrays
   3. NumPy Basics

Day02 Jupyter Notebook [.ipynb format], [.pdf format]

[Day02 Recorded Video Link]

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Day03 Data Manipulation with NumPy (27-Nov-2020)

1. Numpy Basics
2. Math
3. Random
4. Indexing
5. Filtering
6. Statistics
7. Aggregation
8. Saving/Retriving Data

Day03 Jupyter Notebook [.ipynb format], [.pdf format]

[Day03 Recorded Video Link]

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Day04 Data Analysis with Pandas (30-Nov-2020)

1. Series
2. DataFrame
3. Indexing
4. Features
5. Filtering
6. File imporitng
7. Save/Data File Exporting

Day04 Jupyter Notebook [.ipynb format]

[Day04 Recorded Video Link]

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Day05 Data Analysis with Pandas (01-Dec-2020)

1. Importing of Data from Multiple files
2. Combining/merging of DataFrames (JOIN)
3. Grouping
4. Statistics
5. Sorting data
6. Data Visualization
7. Data Cleaning / Data Preprocessing

Day05 Jupyter Notebook [.ipynb format]

[Day05 Recorded Video Link]

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Day06 Data processing and Cleaning (02-Dec-2020)

1. Standardization(standard scaler)
2. Roboust Scaling
3. DataRange(MinMax Scalar)
4. Normalization
5. replace the null values
6. How to find out null values

Day06 Jupyter Notebook [.ipynb format]

[Day06 Recorded Video Link]

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Day07 Data Cleaning and Visulization(Matplotlib) (03-Dec-2020)

1. Replace the missing values(bfill,ffill,limit,replace)
2. dropping the null values
3. Matplotlib

Day07 Jupyter Notebook [.ipynb format]

[Day07 Recorded Video Link]

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Day12 Data Visualization using Seaborn Using Seaborn Styles (05-Dec-2020)

Discussed Concepts:

1. Categorical scatterplots:

   • stripplot() (with kind="strip"; the default)
   • swarmplot() (with kind="swarm")

2. Categorical distribution plots:

   • boxplot() (with kind="box")
   • violinplot() (with kind="violin")

3. Setting the default style

4. stripplot() and swarmplot()

5. boxplot, violinplot

6. Regression Plot

7. barplot, pointplot and countplot

8. Creating heatmap

9. Creating pairplot

Day12 Jupyter Notebook [.ipynb format], [.pdf format]

[Day12_Recorded_Video]

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