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High Python with BI Training

Python with BI

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Python with BI Hurry up!
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Python for Data Analytics

Data Analytics helps us to improve in making decisions and how algorithms optimize outcomes. Data science can improve the condition of humans from making rookie mistakes in investigating phenomena, acquiring new knowledge, and integrating previous knowledge with new ideas.

Data Analytics using Python training course is specially designed for Under-Graduates, Graduates, and working Professionals. This course is for both complete beginners with no Python programming experience or experienced developers who are looking to learn Data analytics for career protective. This course provides end-to-end learning of Data Analytics using Python Domain with deeper dives for creating a winning career for every profile.

Python for Data Analytics

Why to enroll in Data Analytics using Python Training course in JavaTpoint?

This course focuses on Innovative ideas. High-quality Training, Smart classes, and 100% job assistance. It opens the door of opportunities. Even experienced developers can find new and exciting topics to learn about Python and how they can use it for a deep visualization of different types of Data.

What the students will get during the Data Analytics using Python Training course in JavaTpoint?

The students will get career services, industry-expert mentors, and they will learn about real-world projects. In this course we also offers career counseling and advantages to learning on different platforms such as Sublime, Visual Studio, Atom, and many more.

Fields where Data Analytics Applications are useful

  • Banking Sector: Banking and financial institutions use data analytics to find out probable loan defaulters or customer churn-out rates. It also helps in detecting fraudulent transactions immediately.
  • Health care: Healthcare industries use data analytics for analyzing patient data for providing lifesaving diagnoses and treatment options. Data analytics help in discovering new drug development methods as well.
  • Retail: Data analytics helps retailers understand their customer needs and buying habits for predicting trends, recommending new products, and boosting their businesses.
  • Logistic: Logistics companies use data analytics for developing new business models and optimizing different routes.

Python for Data Analytics

Data Analytics Careers

  • Business Intelligence Developer
  • Data Analyst
  • Data Scientist
  • Data Engineer
  • Statistician

Future Scope

  • Companies' inability to handle data

Businesses and companies are regularly collecting data from transactions and through website interactions. Most companies are facing the common challenge of analyzing and categorizing the data they have collected and stored. The data scientist has become the savior in the situation of confusion like this. Companies can progress a lot with the proper and efficient handling of data, which results in productivity.

Python for Data Analytics

  • An astonishing incline in data growth

Data is generated by everyone daily with and without our notice. The interaction we have with data daily will only keep increasing as time passes. In addition, the amount of data existing in the world is regularly increasing at lightning speed. As data production is on the rise, the demand for data scientists is also increasing to help enterprises use and managing it well.

  • Virtual reality will be friendlier

We are witnessing how AI is spreading worldwide nowadays, and companies are dependent on it. With its current innovations, the Big data prospects will flourish more with advanced concepts like Deep Learning and neural networking. Currently, ML is being introduced, and it has been implementing in almost every application. Virtual Reality (VR) and Augmented Reality (AR) are experiencing monumental modifications.

  • Blockchain updating with Data Science

The Blockchain is referred to as the main popular technology dealing with crypto currencies like Bitcoin. For dealing with such big value data, data security has to play its role in securing the data details transactions. In this data science experts will be responsible for dealing with data issues and storing the data securely.

Course Curriculum

Introduction to Python

check icon What is Python?

check icon Features of Python

check icon Installation of Python

check icon Execution of Python Program

check icon Installation of IDE (Anaconda/PyCharm/Visual Studio/Sublime/Atom)

check icon How to work on IDE?

check icon Debugging Process in IDE

check icon What is PIP?

check icon What is Cpython?

check icon What is Jython?

check icon What is Ironpython?

check icon What is Pypy?

check icon Python versions

check icon How to give input?

check icon Printing to the screen

check icon Understanding the print() function

check icon Python Comments

check icon Python Keywords

check icon Python program in debugger mode

check icon Python interpreter architecture

check icon Python byte code compiler

check icon Python virtual machine (PVM)

check icon Python script mode

check icon How to compile Python program explicitly

Python Data Types

check icon About: Integer, Float, Complex Numbers, Boolean, nonetype

check icon String, List, Tuple, range

check icon Dictionary

check icon Set, Frozenset

check icon Type Conversion

Python Conditional Execution

check icon Boolean expressions

check icon Logical operators

check icon Conditional execution

check icon Alternative execution

check icon Chained conditionals

check icon Nested conditionals

check icon Short-circuit evaluation of logical expressions

Python Loop Statement

check icon Introduction to while Loop

check icon Introduction to for Loop

check icon Understanding the range() function

check icon What is Break statement in for Loop?

check icon What is Continue statement in for Loop?

check icon What is Enumerate function in for Loop?

Python Strings

check icon A string is a sequence

check icon Getting the length of a string using len

check icon Traversal through a string with a loop

check icon String slices

check icon Strings are immutable

check icon Looping and counting

check icon The in operator

check icon String comparison

check icon String methods

check icon Parsing strings

check icon Format operator

Python List

check icon Introduction to List

check icon How to create and access list

check icon Traversing a list

check icon What are List indices

check icon Lists and functions

check icon Deleting elements

check icon Basic List operations

check icon List slices

check icon List Comprehension

check icon List built-in methods

check icon Aliasing

check icon List arguments

Python Tuples

check icon Introduction to tuple

check icon How to access tuple element

check icon Basic tuple operations

check icon How to compare tuples?

check icon How to create nested tuple?

check icon About Tuple functions and methods

check icon How to use tuples as keys in dictionaries?

check icon How to Delete Tuple?

check icon About Slicing of Tuple

check icon What is Tuple immutability?

Python Set

check icon How to create a set, and iteration over set

check icon Python set operations

check icon Python set methods

check icon Set built-in methods

check icon Python Frozensets

Python Dictionary

check icon Introduction to dictionary

check icon How to declare dictionary?

check icon Properties of dictionary

check icon Dictionary as a set of counters

check icon Accessing Items from Dictionary

check icon Python Hashing

check icon Updating Dictionary

check icon Copying Dictionary

check icon Advanced text parsing

check icon Dictionary basic operations

check icon Advanced text parsing

check icon Sorting the Dictionary

check icon Looping and dictionaries

check icon Dictionary built-in methods

Variables, expressions, and statements

check icon Values and types

check icon Variables

check icon Variable names and keywords

check icon Statements

check icon Operators and operands

check icon Expressions

check icon Order of operations

check icon Modulus operator

check icon String operations

check icon Comments

check icon Choosing mnemonic variable names

Python Functions

check icon What is a Function?

check icon Why functions?

check icon Define and call a function

check icon Types of Functions

check icon Built-in functions

check icon Significance of Indentation (Space) in Python

check icon Return Statement

check icon Adding new functions

check icon Types of Arguments in Functions

check icon Parameters and arguments

check icon Default Arguments, Non-Default Arguments

check icon Keyword Arguments, Non-keyword Arguments, Arbitrary Arguments

check icon Type conversion functions

check icon Scope of variables

check icon Anonymous Functions

check icon Math functions

check icon Random numbers

check icon Map(), filter(), reduce() functions

check icon Definitions and uses

check icon Generator function

check icon Decorator function

check icon Python Iterator

check icon Function as arguments

check icon Nested functions

check icon Flow of execution

check icon Fruitful functions and void functions

check icon Functions as return statement

check icon Function return statement

check icon Closure

Python Iteration

check icon Updating variables

check icon The while statements

check icon Infinite loops

check icon Finishing iterations with continue

check icon Definite loops using for

  • check icon Loop patterns
    • Counting and summing loops
    • Maximum and minimum loops

Advanced Python

Exception Handling in Python

check icon What is Exception Handling

check icon Try Expert

check icon Many Exceptions

check icon Else in Exception Handling

check icon Finally Keyword

check icon Raise an exception

check icon Common RunTime Errors in Python

check icon Try …Except

check icon Try …Except …else

check icon Try …finally

check icon Abnormal termination

check icon Python Errors

check icon Hashability

Python Class and Object

check icon Introduction to OOPs Programming

check icon Object Oriented Programing System and its Principles

check icon Basic concepts of Object and Classes

check icon How to define Python Classes

check icon Self-variable in Python

check icon Access Modifier

check icon What is inheritance and its Types?

check icon How inheritance Works

Python Regular Expressions

check icon What is Regular Expression?

check icon Regular Expression Syntax

check icon Extracting data using regular expressions

check icon What is the need of Regular expressions?

check icon Character matching in regular expressions

check icon Combining searching and extracting

check icon About Re module

check icon About Regular expression Patterns

check icon About Literal characters and Meta characters

check icon Functions/ methods related to rogex

Modules and Packages

check icon Why Modules?

check icon How to Import Modules?

check icon Script v/s Module

check icon Standard v/s third party Modules

check icon Why packages

File Handling

check icon About File Handling

check icon How to create a File

check icon Pass by reference vs Value

check icon Append Data to a File

check icon How to read a File

check icon How to Read File line by line

check icon About Files modes and its syntax

check icon Lambda Expressions

check icon Understanding File Handling with Block

Python Data Structure

check icon How to implement Lists and its methods?

check icon How implement Tuple and its methods?

check icon How implement Set and its methods?

check icon Difference between List, Tuple and Set

check icon How implement Dictionary and its methods?

Database

check icon Introduction to Database

check icon About Database concepts

check icon What is Database Package?

check icon Understanding Data Storage

check icon Basic data modeling

check icon Database Browser for SQLite

check icon Structured Query Language summary

check icon About Relational Database (RDBMS) concepts

check icon Hosting a Database on Cloud/Local System

SQL

check icon SQL (Structured Query Language) Basics

check icon DML (Data Manipulation Language), DDL (Data Definition Language), DQL (Data Query Language)

check icon How to create, alter and drop the DDL?

check icon How to insert, update, delete and merge the DML?

check icon How to select the DQL?

check icon SQL constraints

check icon Primary and foreign key, composite key

check icon How to select distinct?

  • check icon SQL operators
    • Addition (+)
    • Subtraction (-)
    • Multiplication (*)
    • Division (/)
    • Modulus (%)
  • check icon SQL Comparison Operators:
    • =
    • !=
    • <>
    • >
    • <
    • >=
    • <=
    • !<
    • !>
  • check icon SQL Logical Operators:
    • ALL
    • AND
    • ANY
    • BETWEEN
    • EXISTS
    • IS NULL
    • OR
    • UNIQUE
  • check icon  SQL like, where, order by, view, joins, aliases
  • check icon Joins:
    • Inter Join
    • Full (Outer) Join
    • Left (Outer) Join
    • Right (Outer) Join
  • check icon MySQL Functions
  • check icon String Functions:
    • Char_length
    • Lower
    • Reverse
    • Upper
  • check icon Numeric Functions:
    • Max
    • Min
    • Sum
    • Avg
    • Count
    • abs
  • check icon Date Functions:
    • Curdate
    • Curtime
    • Now

Python for Data Analytics

NumPy Package

check icon What is NumPy array?

check icon Array Constructor

check icon Introduction to Array

check icon Range() function

check icon LINPAC

check icon How to create 2-D Arrays

check icon About: Vector Operation and Matrix Operation

check icon What is Array indexing and Slicing?

check icon Indexing in 1-D Arrays

check icon Indexing in 2-D Arrays

check icon Slicing in 1-D Arrays

check icon Slicing in 2-D Arrays

check icon Scalar Vectorization

check icon Array Comparison

Pandas Package

check icon Introduction to pandas

check icon Pandas and Data Manipulation

check icon What is Labeled and structured data?

check icon What are Series and DataFrame objects?

check icon What is Data Cleansing?

check icon What is Data normalization?

check icon What is Data visualization?

check icon Deleting and Dropping Columns

check icon Series

check icon Apply() function

check icon Creating Series

check icon Data Frame and Basic Functionality

check icon Head() function

check icon About: Merges and Joins

check icon What is Data inspection?

check icon What is Data fill?

check icon Mean() function

check icon Data Frame Manipulation

check icon Indexing and missing Values

check icon Grouping and Reshaping

How to load datasets

check icon From excel

check icon From CSV

check icon From HTML table

Accessing Data from DataFrame

check icon  at and iat

check icon loc() Function and Iloc() function

check icon head() Function and tail() Function

Exploratory Data Analysis (EDA)

check icon About describe() function

check icon groupby() function

check icon crosstab() function

check icon About Boolean slicing and query

Data Cleaning

check icon About: Map() and apply() functions

check icon How to combine Data Frames?

check icon How to add and remove rows and columns?

check icon How to sort data?

check icon How to handle missing values?

check icon How to handle duplicates?

check icon How to handle data error?

check icon How to handle Date and Time?

check icon Hosting a Database on Cloud or local system

check icon CRUD Operation on Database Tables trough Python

check icon Processing and Cleaning Data through Pandas methods

check icon Dealing with missing values

Python for Data Visualization:

check icon Introduction to Data Visualization

Matplotlib package:

check icon Introduction to MatPlotlib Library

check icon How to use matplotlib.pyplot interface

check icon Types of charts

check icon How to plot Histogram and pie chart?

check icon About: Bar Chart, Stacked Chart, Scatter plot

check icon Outlier detection using Boxplot

check icon Adding data to an Axes object

check icon How to customize plots?

check icon How to customize Data appearance?

check icon How to create a grid of subplots?

check icon Area plot for Indexed Data

Seaborn package:

check icon Introduction to Seaborn library

check icon How to show Seaborn Plots?

check icon How to use Seaborn with Matplotlib defaults?

check icon How to set xlim and ylim in Seaborn?

check icon Visualizing networks and interconnections

Introduction to Statistics

check icon Sample and population

  • check icon Measures of central tendency:
    • Arithmetic mean
    • Harmonic mean
    • Geometric mean
    • Mode
    • First quartile, Second quartile (median), Third quartile
    • Standard deviation
  • check icon Graphical exploratory Data Analysis
  • check icon How to plot and compute simple summary Statistics
  • check icon Quantitively exploratory Data Analysis

Probability Distribution

check icon Introduction to probability

check icon What is Conditional Probability?

check icon What is Normal Distribution?

check icon What is Uniform Distribution?

check icon What is Exponential Distribution?

check icon About Right and Left skewed Distribution

check icon What is Random Distribution?

check icon About Central Limit Theorem

check icon Probabilistically- Discrete variables

check icon Statistical interface rests upon probability

check icon Thinking Probabilistically- Continuous variables

Hypothesis Testing

check icon What is Normality?

check icon What is Mean Test?

check icon About T-test

check icon About Z-test

check icon What is ANOVA test?

check icon What is Chi square test?

check icon About Correlation and covariance

Advanced Excel:

  • check icon Data Visualization:
    • How to specify a valid range of values for a cell?
    • How to specify a list of valid values for a cell?
    • How to specify custom validations based on formula for a cell?
  • check icon Working with templates:
    • How to design the structure of a template?
    • How to use templates for standardization of worksheets?
  • check icon Sorting and Filtering Data
    • How to sort tables?
    • How to use multiple-level sorting and custom sorting?
    • How to filter data for selected view?
    • How to use advanced filter options?
  • check icon Working with Reports
    • How to create subtotals and multi-level subtotals?
    • How to create, format and customize Pivot tables and Pivot charts?
    • How to use advanced options of Pivot tables?
    • How to use external data sources?
    • How to create slicers?
  • check icon WhatIf Analysis
    • How to seek goal?
    • How to create data tables?
    • How to create scenario manager?
  • check icon Charts
    • How to use and format charts?
    • How to use 3D Graphs?
    • How to use Bar and Line chart together?
    • How to use Secondary Axis in Graphs?

Tableau:

  • check icon Tableau Home
    • Tableau overview
    • Environment setup
    • About Navigations
    • About Design flow
    • About Files Types
    • About Data Terminology
  • check icon Data Sources:
    • How to customize data view?
    • How to extract data?
    • How to use field operations?
    • How to edit metadata?
    • What is Data joining?
    • What is Data Blending?
  • check icon Tableau Worksheets:
    • How to add worksheets?
    • How to rename and reorder worksheets?
    • How to save and delete worksheets?
  • check icon Tableau Calculation:
    • Numeric Calculation operations and Functions
    • String Calculation operations and Functions
    • Date Calculation functions
    • Table Calculation functions
    • Load expressions and operations
  • check icon Tableau Sorting and Filter:
    • How to use basic sorting and basic filters?
    • How to use quick filters, conditional filters, top filters, context filters and Filter operations?

BONUS

check icon How big is Big Data?

check icon How to store BIG DATA in Commodity Hardware's?

check icon Current processing frameworks

check icon Cluster Computing architecture insights

check icon Maintaining High Availability of data

check icon About Market domain and its growth?

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The javaTpoint Advantage:

We partner with you to understand and address your unique transformation imperatives. We work in transparent consultation with you to devise best-in-class solutions and define the best course of action to implement them across your organization. Our integrated consulting and IT services will bring continuity and consistency to your strategic programs.

WE WILL HELP YOU WITH THE FOLLOWING:

  • 1. Adapt to the changing market conditions.
  • 2. Adapt new technologies.
  • 3. Innovate continually.
  • 4. Align IT with business goals.
  • 5. Optimize costs, while maintaining high customer satisfaction.
  • 6. Accelerate time-to-market for new products and services.
  • 7. Integrate distributed operations and systems into a cohesive organization.

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