Data Analysis using Python

Data Analysis using Python online training course will help to understanding of the data ecosystem and the fundamentals of data analysis, such as data gathering or data mining.

  • 50000
  • 55000
  • Course Includes
  • Live Class Practical Oriented Training
  • 120 + Hrs Instructor LED Training
  • 60 + Hrs Practical Exercise
  • 35 + Hrs Project Work & Assignment
  • Timely Doubt Resolution
  • Dedicated Student Success Mentor
  • Certification & Job Assistance
  • Free Access to Workshop & Webinar
  • No Cost EMI Option


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What you will learn

  • How to use several Python packages for business analysis, including pandas for data manipulation; StatsModels, SciPy, an...
  • To divide data into training and test datasets for validation
  • To visualize data. To estimate and interpret statistical models, such as OLS and logistic regression
  • Deal with different data sources: json, CSV, API. Use Numpy library to create and manipulate arrays.
  • Use the pandas module with Python to create and structure data.

Requirements

  • basic understanding of Computer Programming Languages.

Description

|| About Data Analysis using Python Training Course

Learn how to analyze data using Python. Data Analysis using Python online training 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! As you learn, you'll apply each concept immediately by writing code right in your browser that's automatically checked by our system to give you near-instant feedback on your progress.

 

Throughout this course you will learn the key aspects to data analysis. You will begin to explore the fundamentals of gathering data, and learning how to identify your data sources. You will then learn how to clean, analyze, and share your data with the use of visualizations and dashboard tools. This all comes together in the final project where it will test your knowledge of the course material, explore what it means to be a Data Analyst, and provide a real-world scenario of data analysis.

Course Content

Live Lecture

·      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              

Live Lecture

·      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              

Live Lecture

·      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              

Live Lecture

·      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              

Live Lecture

·      Understanding the Database, need of database

·      Installing MySQL on windows

·      Understanding Database connection using Python.

·      Practical Exercise              

Live Lecture

·      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              

Live Lecture

·      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              

Live Lecture

·      How to plot graph and chart with Python

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

·      The API of MatPlotLib,

·      Subplots.

·      Practical Exercise              

Live Lecture

·      How To Work With The Tabular Data

·      How To Read The Documentation In Pandas

·      Practical Exercise              

Live Lecture

·      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              

Live Lecture

·      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              

Live Lecture

·      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              

Live Lecture

·      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              

Live Lecture

·      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              

Live Lecture

·      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              

Live Lecture

·      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

Fees

Offline Training @ Vadodara

  • Classroom Based Training
  • Practical Based Training
  • No Cost EMI Option
60000 55000

Online Training preferred

  • Live Virtual Classroom Training
  • 1:1 Doubt Resolution Sessions
  • Recorded Live Lectures*
  • Flexible Schedule
55000 50000

Corporate Training

  • Customized Learning
  • Onsite Based Corporate Training
  • Online Corporate Training
  • Certified Corporate Training

Certification

  • Upon the completion of the Classroom training, you will have an Offline exam that will help you prepare for the Professional certification exam and score top marks. The BIT Certification is awarded upon successfully completing an offline exam after reviewed by experts
  • Upon the completion of the training, you will have an online exam that will help you prepare for the Professional certification exam and score top marks. BIT Certification is awarded upon successfully completing an online exam after reviewed by experts.
  • This course is designed to solve different Data Analysis Case studies on various Domains using Python. This certificate is very well recognized over 80 top MNCs from around the world and some of the Fortune 500 companies.