Data Analysis using Python Training Course

  Lecture-1 Python Environment Setup

·      Introduction to Python Language

·      Features, the advantages of Python over other programming languages

·      Python installation – Windows, Mac & Linux distribution for Anaconda Python

·      Deploying Python IDE

·      Basic Python commands

·      Data types

·      Variables

·      Keywords and more

·      Practical Exercise

  Lecture-2 Introduction to Python

·      The Companies using Python

·      Different Applications where Python is used

·      Discuss Python Scripts on UNIX/Windows

·      Values, Types, Variables

·      Operands and Expressions

·      Conditional Statements

·      Loops

·      Command Line Arguments

·      Writing to the screen

·      Practical Exercise

  Lecture-3 Sequences and File Operations

·      Python files I/O Functions

·      Numbers

·      Strings and related operations

·      Tuples and related operations

·      Lists and related operations

·      Dictionaries and related operations

·      Sets and related operations

·      Practical Exercise

  Lecture-4 Functions, OOPs, Modules, Errors and Exceptions

·      Functions

·      Function Parameters

·      Global Variables

·      Variable Scope and Returning Values

·      Lambda Functions

·      Object-Oriented Concepts

·      Standard Libraries

·      Modules Used in Python

·      The Import Statements

·      Module Search Path

·      Package Installation Ways

·      Errors and Exception Handling

·      Handling Multiple Exceptions

·      Practical Exercise

  Lecture-5 Database connection

·      Understanding the Database, need of database

·      Installing MySQL on windows

·      Understanding Database connection using Python.

·      Practical Exercise

  Lecture-6 NumPy for mathematical computing
 
·      Introduction to arrays and matrices

·      Broadcasting of array math, indexing of array

·      Standard deviation, conditional probability, correlation and covariance.

·      Reading and writing arrays on files

·      How to import NumPy module

·      Creating array using ND-array

·      Calculating standard deviation on array of numbers

·      Calculating correlation between two variables.

·      Practical Exercise

  Lecture-7 SciPy

·      Introduction to SciPy

·      Functions building on top of NumPy

·      Cluster, linalg, signal, optimize, integrate

·      Subpackages, SciPy with Bayes Theorem

·      Importing of SciPy

·      Applying the Bayes theorem on the given dataset.

·      Practical Exercise

  Lecture-8 Matplotlib for data visualization

·      How to plot graph and chart with Python

·      Various aspects of line, scatter, bar, histogram, 3D

·      The API of MatPlotLib,

·      Subplots.

·      Practical Exercise

  Lecture-9 Pandas Building Blocks

·      How To Work With The Tabular Data

·      How To Read The Documentation In Pandas

·      Practical Exercise

  Lecture-10 Pandas for data analysis and machine learning

·      Introduction to Python dataframes

·      Importing data from JSON, CSV, Excel, SQL database,

·      NumPy array to dataframe

·      Various data operations like selecting

·      Filtering, sorting, viewing, joining, combining

·      Working on importing data from JSON files

·      Selecting record by a group

·      Applying filter on top, viewing records

·      Theory On Pandas Data Structures

·      How To Construct The Pandas Series

·      How To Construct The DataFrame Objects

·      How To Construct The Pandas Index Objects

·      Data Indexing And Selection

·      Practical Exercise

  Lecture-11 Essential Functionalities in Pandas

·      How To Reindex Pandas Objects

·      How To Drop Entries From An Axis

·      Arithmetic And Data Alignment

·      Arithmetic Methods With Fill Values

·      Broadcasting In Pandas

·      Apply And Applymap In Pandas

·      How To Sort And Rank In Pandas

·      How To Work With The Duplicated Indices

·      Summarising And Computing Descriptive Statistics

·      Unique Values Value Counts And Membership

·      Data Handling

·      Practical Exercise

  Lecture-12 Data Cleaning And Preparation
·      Theory On Data Preprocessing

·      How To Handle Missing Values

·      How To Filter The Missing Values

·      How To Remove Duplicate Rows And Values

·      How To Replace The Non Null Values

·      How To Rename The Axis Labels

·      How To Descretize And Bin The Data Part

·      How To Filter And Detect The Outliers

·      How To Reorder And Select Randomly

·      Converting The Categorical Variables Into Dummy Variables

·      How To Use 'map' Method

·      How To Manipulate With Strings

·      Using Regular Expressions

·      Working With The Vectorized String Functions

·      Practical Exercise

  Lecture-13 Data Wrangling

·      Theory On Data Wrangling

·      Hierarchical Indexing

·      Hierarchical Indexing Reordering And Sorting

·      Summary Statistics By Level

·      Hierarchical Indexing With DataFrame Columns

·      How To Merge The Pandas Objects

·      Merging On Row Index

·      How To Concatenate Along An Axis

·      How To Combine With Overlap

·      How To Reshape And Pivot Data In Pandas

·      Practical Exercise

  Lecture-14 Data Grouping And Aggregation
·      Theory On Data GroupBy And Aggregation

·      Groupby Operation

·      How To Iterate Over Groupby Object

·      How To Select Columns In Groupby Method

·      Grouping Using Dictionaries And Series

·      Grouping Using Functions And Index Level

·      Data Aggregation

·      Practical Exercise

  Lecture-15 Time Series Analysis
·      Theory On Time Series Analysis

·      Introduction To Time Series Data Types

·      How To Convert Between String And Datetime

·      Time Series Basics With Pandas Objects

·      Date Ranges Frequencies And Shifting

·      Periods And Period Arithmetic’s

·      Time Zone Handling

·      Practical Exercise

  Lecture-16 Web scraping with Python

·      Introduction to web scraping in Python

·      Installing of beautifulsoup

·      Installing Python parser lxml

·      Various web scraping libraries

·      Beautifulsoup,

·      Scrapy Python packages

·      Creating soup object with input HTML

·      Searching of tree, full or partial parsing, output print

·      Practical Exercise

  Case Studies