Python with BI

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.

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.

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.

• 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

 What is Python?

 Features of Python

 Installation of Python

 Execution of Python Program

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

 How to work on IDE?

 Debugging Process in IDE

 What is PIP?

 What is Cpython?

 What is Jython?

 What is Ironpython?

 What is Pypy?

 Python versions

 How to give input?

 Printing to the screen

 Understanding the print() function

 Python Comments

 Python Keywords

 Python program in debugger mode

 Python interpreter architecture

 Python byte code compiler

 Python virtual machine (PVM)

 Python script mode

 How to compile Python program explicitly

Python Data Types

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

 String, List, Tuple, range

 Dictionary

 Set, Frozenset

 Type Conversion

Python Conditional Execution

 Boolean expressions

 Logical operators

 Conditional execution

 Alternative execution

 Chained conditionals

 Nested conditionals

 Short-circuit evaluation of logical expressions

Python Loop Statement

 Introduction to while Loop

 Introduction to for Loop

 Understanding the range() function

 What is Break statement in for Loop?

 What is Continue statement in for Loop?

 What is Enumerate function in for Loop?

Python Strings

 A string is a sequence

 Getting the length of a string using len

 Traversal through a string with a loop

 String slices

 Strings are immutable

 Looping and counting

 The in operator

 String comparison

 String methods

 Parsing strings

 Format operator

Python List

 Introduction to List

 How to create and access list

 Traversing a list

 What are List indices

 Lists and functions

 Deleting elements

 Basic List operations

 List slices

 List Comprehension

 List built-in methods

 Aliasing

 List arguments

Python Tuples

 Introduction to tuple

 How to access tuple element

 Basic tuple operations

 How to compare tuples?

 How to create nested tuple?

 About Tuple functions and methods

 How to use tuples as keys in dictionaries?

 How to Delete Tuple?

 About Slicing of Tuple

 What is Tuple immutability?

Python Set

 How to create a set, and iteration over set

 Python set operations

 Python set methods

 Set built-in methods

 Python Frozensets

Python Dictionary

 Introduction to dictionary

 How to declare dictionary?

 Properties of dictionary

 Dictionary as a set of counters

 Accessing Items from Dictionary

 Python Hashing

 Updating Dictionary

 Copying Dictionary

 Advanced text parsing

 Dictionary basic operations

 Advanced text parsing

 Sorting the Dictionary

 Looping and dictionaries

 Dictionary built-in methods

Variables, expressions, and statements

 Values and types

 Variables

 Variable names and keywords

 Statements

 Operators and operands

 Expressions

 Order of operations

 Modulus operator

 String operations

 Comments

 Choosing mnemonic variable names

Python Functions

 What is a Function?

 Why functions?

 Define and call a function

 Types of Functions

 Built-in functions

 Significance of Indentation (Space) in Python

 Return Statement

 Adding new functions

 Types of Arguments in Functions

 Parameters and arguments

 Default Arguments, Non-Default Arguments

 Keyword Arguments, Non-keyword Arguments, Arbitrary Arguments

 Type conversion functions

 Scope of variables

 Anonymous Functions

 Math functions

 Random numbers

 Map(), filter(), reduce() functions

 Definitions and uses

 Generator function

 Decorator function

 Python Iterator

 Function as arguments

 Nested functions

 Flow of execution

 Fruitful functions and void functions

 Functions as return statement

 Function return statement

 Closure

Python Iteration

 Updating variables

 The while statements

 Infinite loops

 Finishing iterations with continue

 Definite loops using for

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

Advanced Python

Exception Handling in Python

 What is Exception Handling

 Try Expert

 Many Exceptions

 Else in Exception Handling

 Finally Keyword

 Raise an exception

 Common RunTime Errors in Python

 Try …Except

 Try …Except …else

 Try …finally

 Abnormal termination

 Python Errors

 Hashability

Python Class and Object

 Introduction to OOPs Programming

 Object Oriented Programing System and its Principles

 Basic concepts of Object and Classes

 How to define Python Classes

 Self-variable in Python

 Access Modifier

 What is inheritance and its Types?

 How inheritance Works

Python Regular Expressions

 What is Regular Expression?

 Regular Expression Syntax

 Extracting data using regular expressions

 What is the need of Regular expressions?

 Character matching in regular expressions

 Combining searching and extracting

 About Re module

 About Regular expression Patterns

 About Literal characters and Meta characters

 Functions/ methods related to rogex

Modules and Packages

 Why Modules?

 How to Import Modules?

 Script v/s Module

 Standard v/s third party Modules

 Why packages

File Handling

 About File Handling

 How to create a File

 Pass by reference vs Value

 Append Data to a File

 How to read a File

 How to Read File line by line

 About Files modes and its syntax

 Lambda Expressions

 Understanding File Handling with Block

Python Data Structure

 How to implement Lists and its methods?

 How implement Tuple and its methods?

 How implement Set and its methods?

 Difference between List, Tuple and Set

 How implement Dictionary and its methods?

Database

 Introduction to Database

 About Database concepts

 What is Database Package?

 Understanding Data Storage

 Basic data modeling

 Database Browser for SQLite

 Structured Query Language summary

 About Relational Database (RDBMS) concepts

 Hosting a Database on Cloud/Local System

SQL

 SQL (Structured Query Language) Basics

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

 How to create, alter and drop the DDL?

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

 How to select the DQL?

 SQL constraints

 Primary and foreign key, composite key

 How to select distinct?

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

Python for Data Analytics

NumPy Package

 What is NumPy array?

 Array Constructor

 Introduction to Array

 Range() function

 LINPAC

 How to create 2-D Arrays

 About: Vector Operation and Matrix Operation

 What is Array indexing and Slicing?

 Indexing in 1-D Arrays

 Indexing in 2-D Arrays

 Slicing in 1-D Arrays

 Slicing in 2-D Arrays

 Scalar Vectorization

 Array Comparison

Pandas Package

 Introduction to pandas

 Pandas and Data Manipulation

 What is Labeled and structured data?

 What are Series and DataFrame objects?

 What is Data Cleansing?

 What is Data normalization?

 What is Data visualization?

 Deleting and Dropping Columns

 Series

 Apply() function

 Creating Series

 Data Frame and Basic Functionality

 Head() function

 About: Merges and Joins

 What is Data inspection?

 What is Data fill?

 Mean() function

 Data Frame Manipulation

 Indexing and missing Values

 Grouping and Reshaping

How to load datasets

 From excel

 From CSV

 From HTML table

Accessing Data from DataFrame

  at and iat

 loc() Function and Iloc() function

 head() Function and tail() Function

Exploratory Data Analysis (EDA)

 About describe() function

 groupby() function

 crosstab() function

 About Boolean slicing and query

Data Cleaning

 About: Map() and apply() functions

 How to combine Data Frames?

 How to add and remove rows and columns?

 How to sort data?

 How to handle missing values?

 How to handle duplicates?

 How to handle data error?

 How to handle Date and Time?

 Hosting a Database on Cloud or local system

 CRUD Operation on Database Tables trough Python

 Processing and Cleaning Data through Pandas methods

 Dealing with missing values

Python for Data Visualization:

 Introduction to Data Visualization

Matplotlib package:

 Introduction to MatPlotlib Library

 How to use matplotlib.pyplot interface

 Types of charts

 How to plot Histogram and pie chart?

 About: Bar Chart, Stacked Chart, Scatter plot

 Outlier detection using Boxplot

 Adding data to an Axes object

 How to customize plots?

 How to customize Data appearance?

 How to create a grid of subplots?

 Area plot for Indexed Data

Seaborn package:

 Introduction to Seaborn library

 How to show Seaborn Plots?

 How to use Seaborn with Matplotlib defaults?

 How to set xlim and ylim in Seaborn?

 Visualizing networks and interconnections

Introduction to Statistics

 Sample and population

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

Probability Distribution

 Introduction to probability

 What is Conditional Probability?

 What is Normal Distribution?

 What is Uniform Distribution?

 What is Exponential Distribution?

 About Right and Left skewed Distribution

 What is Random Distribution?

 About Central Limit Theorem

 Probabilistically- Discrete variables

 Statistical interface rests upon probability

 Thinking Probabilistically- Continuous variables

Hypothesis Testing

 What is Normality?

 What is Mean Test?

 About T-test

 About Z-test

 What is ANOVA test?

 What is Chi square test?

 About Correlation and covariance

Advanced Excel:

•  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?
•  Working with templates:
  • How to design the structure of a template?
  • How to use templates for standardization of worksheets?
•  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?
•  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?
•  WhatIf Analysis
  • How to seek goal?
  • How to create data tables?
  • How to create scenario manager?
•  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:

•  Tableau Home
  • Tableau overview
  • Environment setup
  • About Navigations
  • About Design flow
  • About Files Types
  • About Data Terminology
•  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?
•  Tableau Worksheets:
  • How to add worksheets?
  • How to rename and reorder worksheets?
  • How to save and delete worksheets?
•  Tableau Calculation:
  • Numeric Calculation operations and Functions
  • String Calculation operations and Functions
  • Date Calculation functions
  • Table Calculation functions
  • Load expressions and operations
•  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

 How big is Big Data?

 How to store BIG DATA in Commodity Hardware's?

 Current processing frameworks

 Cluster Computing architecture insights

 Maintaining High Availability of data

 About Market domain and its growth?