PowerBI Training Certification with Python in Noida
PowerBI Training Certification with Python Course, Fees, and Jobs
PowerBI Training Certification with Python Course Description:
The Power BI Certification Training with Python course offered by Javatpoint is a comprehensive and hands-on program designed to equip learners with essential skills in data analysis, visualization, and predictive analytics using Microsoft Power BI and Python. This course is ideal for aspiring data analysts, business intelligence professionals, and anyone interested in leveraging data-driven insights to make informed business decisions.
PowerBI Training Certification Key Topics Covered:
- Introduction to Power BI and Python
- Data Extraction and Transformation using Power Query
- Data Modeling and DAX (Data Analysis Expressions)
- Creating Interactive Reports and Dashboards in Power BI
- Advanced Visualizations and Customizations
- Integrating Python in Power BI for Advanced Analytics
- Predictive Analytics using Python Libraries
- Data Insights and Decision-Making Strategies
PowerBI Training Certification Course Features:
Expert-led Training: Learn from industry experts with extensive experience in Power BI and Python.
Hands-on Projects: Work on real-world projects to apply your skills and reinforce your learning.
Comprehensive Curriculum: Cover the entire data analysis workflow, from data preparation to visualization and analysis.
Practical Exercises: Practice with datasets and gain confidence in solving data challenges.
Certification Guidance: Prepare for Power BI certification exams to boost your professional credentials.
PowerBI Training Certification Course Fees and Duration:
Please contact Javatpoint or visit their website for the most up-to-date information regarding course fees and duration.
Basic knowledge of data analysis concepts and familiarity with Python programming is beneficial but not mandatory. Students should have a strong interest in data analytics and a willingness to learn new tools and techniques.
Who Should Attend PowerBI Training Course:
- Aspiring Data Analysts and Business Intelligence Professionals
- IT Professionals seeking to enhance their data analytics skills
- Managers and Decision Makers looking to gain insights from data
- Students and Graduates interested in pursuing a career in data analytics
- Enroll now in the Power BI Certification Training with Python course at Javatpoint to unlock the potential of data-driven decision-making and advance your career in the world of data analytics.
PowerBI Training 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?
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:
- Adapt to the changing market conditions.
- Adapt new technologies.
- Innovate continually.
- Align IT with business goals.
- Optimize costs, while maintaining high customer satisfaction.
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- Integrate distributed operations and systems into a cohesive organization.