Data Analysis Using Python Training Course Online Course

Data Analysis using Python Training Course

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English

Course Description

Learn how to analyze data using Python. This course will take you from the basics of Python to exploring many different types of data. You will learn how to prepare data for analysis, perform simple statistical analysis, create meaningful data visualizations, predict future trends from data. Data analyst responsible for conducting, analyzing, and interpreting data for key business decisions, and you want to learn how to use Python and its main packages.This course will help to expand your knowledge of and experience with toolsets for analysis methods, such as machine learning, and software so you can provide the best insights to your clients and advance your career. Data Analysis courses, covering everything you need to learn to work as a data analyst using Python. It's designed so that there are no prerequisites and no prior experience required. Everything you need to learn to work as a data analyst, you'll learn on this path!

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What you’ll learn

Live Class Practical Oriented Training
Timely Doubt Resolution
Dedicated Student Success Mentor
Certification & Job Assistance
Free Access to Workshop & Webinar
No Cost EMI Option
How to use several Python packages for business analysis, including pandas for data manipulation; StatsModels, SciPy, an...
To visualize data. To estimate and interpret statistical models, such as OLS and logistic regression
Use the pandas module with Python to create and structure data.
To divide data into training and test datasets for validation
Deal with different data sources: json, CSV, API. Use Numpy library to create and manipulate arrays.

Covering Topics

Lecture-1 Python Environment Setup

Lecture-2 Introduction to Python

Lecture-3 Sequences and File Operations

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

Lecture-5 Database connection

Lecture-6 NumPy for mathematical computing

Lecture-7 SciPy

Lecture-8 Matplotlib for data visualization

Lecture-9 Pandas Building Blocks

Lecture-10 Pandas for data analysis and machine learning

Lecture-11 Essential Functionalities in Pandas

Lecture-12 Data Cleaning And Preparation

Lecture-13 Data Wrangling

Lecture-14 Data Grouping And Aggregation

Lecture-15 Time Series Analysis

Lecture-16 Web scraping with Python

Case Studies

Curriculum

  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

Frequently Asked Questions

basic understanding of Computer Programming Languages.

The course offers a variety of online training options, including: • Live Virtual Classroom Training: Participate in real-time interactive sessions with instructors and peers. • 1:1 Doubt Resolution Sessions: Get personalized assistance and clarification on course-related queries. • Recorded Live Lectures*: Access recorded sessions for review or to catch up on missed classes. • Flexible Schedule: Enjoy the flexibility to learn at your own pace and according to your schedule.

Live Virtual Classroom Training allows you to attend instructor-led sessions in real-time through an online platform. You can interact with the instructor, ask questions, participate in discussions, and collaborate with fellow learners, simulating the experience of a traditional classroom setting from the comfort of your own space.

If you miss a live session, you can access recorded lectures* to review the content covered during the session. This allows you to catch up on any missed material at your own pace and ensures that you don't fall behind in your learning journey.

The course offers a flexible schedule, allowing you to learn at times that suit you best. Whether you have other commitments or prefer to study during specific hours, the course structure accommodates your needs, enabling you to balance your learning with other responsibilities effectively. *Note: Availability of recorded live lectures may vary depending on the course and training provider.