Python with ML
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
- 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.
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?
How to give input?
Printing to the screen
Understanding the print() function
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
Python Conditional Execution
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?
A string is a sequence
Getting the length of a string using len
Traversal through a string with a loop
Strings are immutable
Looping and counting
The in operator
Introduction to List
How to create and access list
Traversing a list
What are List indices
Lists and functions
Basic List operations
List built-in methods
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?
How to create a set, and iteration over set
Python set operations
Python set methods
Set built-in methods
Introduction to dictionary
How to declare dictionary?
Properties of dictionary
Dictionary as a set of counters
Accessing Items from 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
Variable names and keywords
Operators and operands
Order of operations
Choosing mnemonic variable names
What is a Function?
Define and call a function
Types of Functions
Significance of Indentation (Space) in Python
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
Map(), filter(), reduce() functions
Definitions and uses
Function as arguments
Flow of execution
Fruitful functions and void functions
Functions as return statement
Function return statement
The while statements
Finishing iterations with continue
Definite loops using for
- Loop patterns
- Counting and summing loops
- Maximum and minimum loops
Exception Handling in Python
What is Exception Handling
Else in Exception Handling
Raise an exception
Common RunTime Errors in Python
Try …Except …else
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
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
How to Import Modules?
Script v/s Module
Standard v/s third party Modules
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
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?
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 (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?
Primary and foreign key, composite key
How to select distinct?
- SQL operators
- Addition (+)
- Subtraction (-)
- Multiplication (*)
- Division (/)
- Modulus (%)
- SQL Comparison Operators:
- SQL Logical Operators:
- IS NULL
- SQL like, where, order by, view, joins, aliases
- Inter Join
- Full (Outer) Join
- Left (Outer) Join
- Right (Outer) Join
- MySQL Functions
- String Functions:
- Numeric Functions:
- Date Functions:
Python for Data Analytics
What is NumPy array?
Introduction to Array
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
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
Data Frame and Basic Functionality
About: Merges and Joins
What is Data inspection?
What is Data fill?
Data Frame Manipulation
Indexing and missing Values
Grouping and Reshaping
How to load datasets
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
About Boolean slicing and query
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
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
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
- 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
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
What is Normality?
What is Mean Test?
What is ANOVA test?
What is Chi square test?
About Correlation and covariance
- 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?
- 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 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?
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?
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.