vlr training
Data Science for AI and Machine Learning Using Python

VLR Training, we’re excited to embark on a transformative journey with you into the world of data science and programming using Python. Our comprehensive course is designed to equip you with the skills and knowledge you need to excel in the dynamic field of data analysis and manipulation. Here are the modules that we will be exploring together in this enlightening course

About Data Scientist and Machine Learners

Data Scientists are responsible for analysis of data with a goal of discovering insights which in turn can provide a competitive advantage or address a pressing business problem.

His role is to analyze data from different angles, determine what it means and then recommend ways to apply that data. They employ sophisticated analytics programs, machine learning and statistical methods to prepare data for use in predictive and prescriptive modeling.

They are also responsible to communicate predictions and findings to management and IT departments through effective data visualizations and reports.

Data Science Machine Learning Online Training Course Details

Course Duration

4 Months


09:15 PM(IST)

Mode of Training


Prerequisites for Data Science Training

Everyone can get data science training.

  • Fresher’s/Graduates
  • Job Seekers
  • Managers
  • Data analysts
  • Business analysts
  • Operators
  • End users
  • Developers
  • IT professionals

Objectives Of Data Science Machine Learning Course

After the course completion of Data Science Machine Learning Training students will be able to:

  • Be a part of a data science team to work on various other analytics projects
  • Create statistical models and understand which insights can lead to accurate results
  • Understand how advanced analytics developed to create competitive advantage
  • Understand the roles and responsibilities of data scientists and BI analytics are different from each other
  • Analyze several types of data using python
  • Learn the tools and techniques with data transformation
  • Machine Learning

Why should you take the Data Science course?

  •  The number of Data Science and Analytics job listings is projected to grow by nearly 364,000 listings by 2020 – Forbes
  •  The average salary for a Data Scientist is $120k as per Glassdoor
  •  Businesses analyzing data will see $430 billion in productivity benefits over their rivals not analyzing data by 2020

Data science Machine Learning

➢ What is Python and brief history
➢ Why Python and who use Python
➢ Discussion on Python 2 and 3 Unique features of Python
➢ Discussion on various IDE’s
➢ Demonstration of practical use cases
➢ Python use cases using data analysis

➢ Installing python
➢ Setting up Python environment for development
➢ Installation of Jupyter Notebook
➢ How to access python course material using Jupyter. Write your first program in python

➢ Python built-in functions
➢ Number objects and operations
➢ Variable assignment
➢ String
• Introduction
• Operations and Functions.
➢ Print formatting with strings
➢ List
• Introduction
• Operations and Functions
➢ Tuple
• Introduction
• Operations and Functions
➢ Dictionary
• Introduction
• Operations and Functions
➢ Sets and Boolean
• Introduction
• Operations and Functions
➢ Object and data structures assessment test

➢ Introduction to Python statements
➢ If, elif and else statements
➢ Comparison operators
➢ Chained comparison operators
➢ Range function
➢ What are loops
➢ While loops
➢ Useful operators
➢ List comprehensions
➢ Statement assessment test
➢ Game challenge

➢ Introduction to Methods
➢ What are various types of functions
➢ Creating and calling user defined functions
➢ Function practice exercises
➢ Lambda Expressions
➢ Map and filter
➢ Nested statements and scope
➢ Args and kwargs
➢ Functions and methods assignment
➢ Milestone Project (Making tic-tac-toe in python)

➢ Process files using python
➢ Read/write and append file object
➢ File functions
➢ File pointer and operations
➢ Introduction to error handling
➢ Try, except and finally
➢ Python standard exceptions
➢ User defined exceptions
➢ Unit testing
➢ File and exceptions assignment

➢ Object oriented features
➢ Implement object oriented with Python
➢ Creating classes and objects
➢ Creating class attributes
➢ Creating methods in a class
➢ Inheritance
➢ Polymorphism
➢ Special methods for class
➢ Assignment – Creating a python script to replicate deposits and withdrawals in a bank with appropriate classes and UDFs.

➢ Collections module
➢ Datetime
➢ Python debugger
➢ Timing your code
➢ Regular Expressions
➢ StringIO
➢ Python decorators
➢ Python generators

➢ SQL Introduction
• Data Definition Language (DDL)
• Data Manipulation Language (DML)
➢ SQL Server Summary
• SQL Server Management Studio
• Create a new Database
• Queries
➢ Create Table
• Database Modelling
• Create Tables using the Designer Tools
• SQL Constraints
• NOT NULL / Required Columns
➢ CRUD Operation
• Introduction to SQL Query
• Commands like Select, Insert, Update, Delete
• The ORDER BY Keyword
• The WHERE Clause
• Operators
• LIKE Operator
• IN Operator
• BETWEEN Operator J Wildcards
• AND & OR Operators
• Alias
• Joins
• Different SQL JOINs

➢ Introduction in Excel
➢ Data Cleaning & Preparation
➢ Formatting & Conditional Formatting
➢ Lookup Function
➢ Analyzing data with Pivot Tables
➢ Charts
➢ Data Visualization/Dashboarding using excel
➢ Data Analysis using statistics

➢ Introduction to data analysis
➢ Why Data analysis?
➢ Data analysis and Artificial Intelligence Bridge and connecting it to database
➢ Introduction to Data Analysis libraries
➢ Data analysis introduction assignment challenge

➢ Introduction to Numpy arrays
➢ Creating and applying functions
➢ Numpy Indexing and selection
➢ Numpy Operations
➢ Exercise and assignment challenge

➢ Introduction to Series
➢ Introduction to DataFrames
➢ Data manipulation with pandas
➢ Missing data
➢ Groupby
➢ Operations
➢ Data Input and Output
➢ Pandas in depth coding exercises
➢ Text data mining and processing
➢ Data mining applications in Data engineering
➢ File system integration with Pandas
➢ Excel integration with Pandas
➢ Operations on Excel using Dataframe
➢ Data aggregation on Excel Data
➢ Data visualization using Excel data

➢ Matplotlib
• Plotting using Matplotlib
• Plotting Numpy arrays
• Plotting using object-oriented approach
• Subplots using Matplotlib
• Exercise and assignment challenge
• Matplotlib attributes and functions
• Matplotlib exercises
➢ Seaborn Visualization
• Categorical Plot using Seaborn
• Distributional plots using Seaborn
• Matrix plots
• Grids
• Heat Map
• Seaborn Exercises

➢ Variables and it’s types
➢ Formats of Data Types
➢ Distribution, Correlations
➢ Testing-Confidence level, Central tendency

➢ Introduction to Machine learning and it’s uses in real world
➢ Future of Machine learning
➢ Opportunity after learning Machine Learning
➢ Linear, Multiple and Logistic regression
➢ Supervised learning
➢ Unsupervised learning
➢ Decision Tree
➢ Random forest for regression
➢ Evaluation methods for decision tree and random forest
➢ K-means Clustering
➢ Rule Mining
➢ Capstone Project

➢ Introduction
➢ Libraries in NLP
➢ Classification of text
➢ Stemming Methods
➢ lemmatization techniques
➢ Neural network Elements
➢ Forward & Backward Propagation

➢ Comparison Between Power BI & Programming Based Data Visualization
➢ Need Of Power BI
➢ Types Of Data Sources Supported by Power BI for Report Development
➢ How To Build Report & Dashboard in Power BI
➢ How To Build Charts in Power BI
➢ Data Visualization Using Power BI Features
➢ Types of Graphs
➢ Multiple graphs combinations
➢ Multiple file formats supported in Power BI
➢ Data analysis without visualization
➢ Data analysis with visualization

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