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High Data Science Training Training

Data Science Training using Python, Machine Learning, Power BI, and Artificial Intelligence in JavaTpoint


Data Science Training using Python, Machine Learning, Power BI, and Artificial Intelligence in JavaTpoint

Data Science 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 Science.

Data Science Training using Python

Python is a general-purpose interpreted, interactive, object-oriented, and high-level programming language. Currently, Python is the most popular language in the IT world. Python is adopted as a language of choice for almost all the domains in IT industries and developers, including Web Development, Cloud Computing, Infrastructure Automations, Software Testing, Mobile Testing, Big Data and Hadoop, Data Science, and many more. This course will set you on a journey in Python by playing with data, creating applications, and testing them.

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 understanding of different types of Data.

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

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

Data Science Training using Python

Data Science Careers

  • Business Intelligence Developer
  • Data Architect
  • Applications Architect
  • Infrastructure Architect
  • Enterprise Architect
  • Data Analyst
  • Data Scientist
  • Data Engineer
  • Machine Learning Scientist
  • Machine Learning Engineer
  • Statistician

Data Science Training using Python

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 in today's world. 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 cryptocurrencies 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.

Data Science Training using Python

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

  • 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

GUI Programing

  • Introduction to Tkinter Programming
  • About Tkinter Widgets
  • About Tk, label, Entry, Textbox, Button
  • What is Frame, message box and file Dialog etc.?
  • About Layout managers
  • Event Handling
  • How to display image?

Multi-threading Programing

  • Difference between multi-processing and multi-threading
  • What is the need of threads?
  • How to create child threads?
  • What are the functions and methods related to threads?
  • About thread synchronization and locking

Modules and Packages

  • Why Modules?
  • How to Import Modules?
  • Script v/s Module
  • Standard v/s third party Modules
  • Why packages

File Input Output

  • About File Handling
  • About Files modes
  • Understanding File Handling with Block


  • 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


  • 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
    • 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

Data Manipulation

NumPy Package

  • What is NumPy array?
  • Array Constructor
  • Introduction to Array
  • Range() function
  • 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 and iloc
  • head() and tail()

Exploratory Data Analysis (EDA)

  • About describe() function
  • groupby() function
  • crosstab() function
  • About Boolean slicing and query

Weilding Big Data through Pyspark

  • How big is Big Data?
  • Cluster computing
  • Hadoop Architecture
  • In-memory Computation
  • Apache Spark Architecture
  • Hadoop vs Spark
  • What is Spark?
  • Why Pyspark?
  • Databricks setup and forming cloud cluster
  • How to handle Missing Data
  • How to trigger SQL query in Pyspark
  • Dates and Timestamp

Data Manipulation and 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

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

Machine Learning

  • Introduction of Machine Learning
  • Machine Learning Programing
  • Real life examples based on ML
  • Data Processing revised
  • Types of Machine Learning
  • What are Features and labels?
  • Terminology related to Machine Learning
  • Supervised Learning
    • Introduction to Supervised Learning
    • Different Types of Supervised Learning
    • What is Classification?
    • What is Regression?
  • Unsupervised Learning
    • Introduction to Unsupervised Learning
    • What is Clustering?
    • Introduction to Hierarchical Clustering
    • Hierarchical Clustering understanding the Algorithm
    • Introduction to Fuzzy K-means
  • Classification
    • Introduction to KNN (K- Nearest Neighbors)
    • What is math behind KNN classification?
    • How to implement KNN classification?
    • How to understand hyperparameters?
    • Introduction to Naïve Bayes
    • How to implement Naïve Bayes?
    • Introduction to Random Forest Classification
    • How to implement Random Forest Classification
    • Introduction to Decision Tree Classification
    • Introduction to Logistic Regression
    • About Performance Matrix
    • Classification Model Selection in Python
  • Regression
    • What is math behind regression?
    • What is Simple linear regression?
    • What is Multiple linear regression?
    • What is Polynomial regression?
    • What is Simple Linear Regression?
    • What is Multiple Liner Regression?
    • How to predict Boston price?
    • About: Cost or loss functions
    • What is Mean absolute error?
    • What is Mean squared error?
    • What is Root mean squared error?
    • What is Least square error?
    • About Regularization
    • What is Backward Elemination?
  • Logistic Regression for Classification
    • Theory of logistic regression
    • About: Binary and multiclass classi?cation
    • How to implement titanic dataset
    • How to implement iris dataset
    • About: Sigmoid and SoftMax functions
  • Support Vector Machine (SVM)
    • Theory of SVM
    • How to implement SVM?
    • About: Kernel, gamma and alpha?
  • Decision Tree Classification
    • Theory of Decision Tree
    • What is node splitting
    • How to perform implementation with iris Dataset
    • How to visualize Tree
  • Ensemble Learning
    • What is Random Forest?
    • What is bagging and boosting?
    • What is Voting classifier?

Model Selection Technique

  • What is Cross Validation?
  • Grid and random search for hyper parameter tuning

Recommendation System

  • What is content based technique?
  • What is collaborative filtering technique?
  • How to evaluate similarity based on correlation
  • What are classification-based recommendations?


  • What is K-mean clustering?
  • What is Hierarchical clustering?
  • What is Elbow Technique?
  • What is Silhouette coefficient?
  • About Dendrogram

Text Analysis

  • How to install nltk?
  • How to tokenize words?
  • How to stop words customization?
  • Stemming and lemmatization
  • What is Feature Extraction?
  • What is Sentiment analysis?
  • What is Count Vectorizer?
  • What is Naïve Bayes algorithms?


  • How to read images?
  • How to understand gray scale images?
  • How to resize image?
  • How to classify face and eye?
  • How to use webcam in OpenCV?
  • How to build image dataset?
  • How to capture video?
  • How to classify face in video?
  • How to create model for gender prediction project?

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 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?

Live Data Streaming

  • Spark streaming with Python
  • Data in Motion
  • How to convert Terminal into a stream for live data and reading from that stream?
  • Spark stream Twitter Project

Reporting with Microsoft Power

  • Introduction to Power BI
  • Environment setup
  • Workflow of Power BI Desktop
  • How to explore the interface of Data Model
  • How does the Query Editor Interface?
  • How to connect Power BI Desktop to Source files?
  • How to keep and Remove Rows?
  • How to work with Filters?
  • How to remove Empty rows?
  • How to Append Queries?
  • How to format Data and Handle Formatting Errors?>
  • How to Pivot and Unpivot Data?
  • How to Split Columns?
  • How to remove Duplicates?
  • About "Join Kind"
  • About "Extract"
  • How to create and understand the concept of the FACT-Table
  • Performance Optimization

Data Model: Data and Relationship View

  • Introduction to Data Model
  • Understanding Relationships
  • M-Language vs DAX (Data Analysis Expressions)
  • Basics of DAX
  • DAX functions
  • How to apply DAX basics
  • How to create Measures with "Measures"?
  • Difference Between Calculated Columns and Measures

Visuals in the Report View

  • Introduction
  • Basic Visual Concepts
  • How to create our First Visuals?
  • Diving into Hierarchies and Drill Mode
  • Data Colors and Conditional Formatting
  • How to format Report Pages?
  • How to use Slicer?
  • About Default summarization and Sorting
  • How to sync Slicer?
  • About Filter Types: Visual, Page and Report
  • How to create combines Visuals and Waterfalls?
  • How to user Custom Visuals?

How to Deploy Project to the Cloud with Power BI Pro (Service)

  • Introduction
  • About Workspaces
  • How to work with Reports?
  • How to create Dashboards?
  • How to refresh Data with Gateways?
  • How to share Data from "My Workspace"?
  • How to publish an App (Application)



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.


  • 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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