Certisured brings thoroughly crafted, Industry excellence data science transition program with certification for freshers and professionals from Non-IT Domain.



Instructor-led live classes with access to recordings of the classes

Python Programming is taught from scratch as this course is designed to be a transition from Non-IT to IT

This course covers the Data bases with SQL as the perspective in depth.

Languages and Tools covered

technology stack
technology stack
technology stack
technology stack
technology stack
About the



The industry has amassed a considerable amount of data. For the purpose of making judgments in the future, companies either produce a lot of data or purchase data. When a company gets hold of massive amounts of data, there is a need for this data to be cleaned, processed, and prepared to be used further as an input to predictive models or Machine learning algorithms. This is when tech organizations post a job posting seeking candidates with the skills to clean, pre-process, visualize data, and leverage algorithms to enable computers to learn from data, make predictions, and perform tasks without explicit programming.

Who should apply for this course?

  • Professionals With 1+ Years of experience in any domain (technical)
  • Students from the Non-IT domain who want to master Data Science
  • Students without experience.but, with keen interest in mathematics, logic and programming may apply too.
  • Professionals from non-it domains looking to transition into Data science


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Book the Demo & confirm your Batch


6 months

Why Did We Create

This Course

There is a huge accumulation of Data in the industry. Organizations are either generating a lot of data or buying data to take future decisions.

When a company gets hold of massive amounts of data, there is a need for this data to be cleaned, processed & prepared to be used further as an input to predictive models or Machine learning algorithms.

This is when tech organizations put out a job posting seeking candidates who leverages algorithms to enable computers to learn from data, make predictions, and perform tasks without explicit programming.



This course is a detailed industry excellence program. If you want just theoretical lessons without practical implementation, then this course is not for you. Instead, if you want to learn exactly how things are done in the industry and become a Data Scientist, you've come to the right place.

bootcamp timeline
Python Programming

This section deals with complete mastery of Full Stack Data Analyst, The modules taught will be more than adequate to understand the concepts of Business Intelligence, data pre-processing, and data analysis techniques used in data science.

Introduction to Programming

Module - ZERO [ For non-IT students]

  • Fundamentals of computer architecture

    • Hardware vs Software

    • Memory Management

    • Operating systems, Shell, Kernel & the ALU

  • Understanding the Binary world

  • Understanding the language of computer

    • Assemblers

    • Compilers

    • Interpreters

  • Introduction to Data Structures and their importance

  • Introduction to Algorithms.

Module 1 Introduction to Python Programming

  • Comparison with other programming language
  • Installing Python and pip
  • Scope of Python in Production
  • Working with IDE and interpreter
  • Features of Python
  • Python Versions
  • Creating your first python program
  • Python keywords and identifiers
  • Python Variables
  • Type of Conversion

Module 2 Data Types, Operators, Strings, Conditional statements & Loops

  • Python Data Types
  • Operators
    • Arithmetic operators
    • Relational operators
    • Conditional operators
    • Logical operators
  • Introduction to string
    • Basic string operation
    • String functions and methods
    • Deleting a string
    • String manipulation and concatenation
  • Conditional Statements
    • What are conditional statements?
    • Working with “if” condition
    • Working with “if-else” condition
    • Working with “if-elif” condition
    • Nested if condition
  • Loops
    • Introduction to Loops concepts
    • Working with ‘while’ loop
    • Working with ‘for’ loop
    • Nested loop
    • Break statement and Continue statement
    • Practical examples for each topic

Module 3 Sequence of Data (Data Types)

  • Python Lists
    • Lists are mutable
    • Getting to Lists
    • List indices
    • Traversing a list
    • List operations, slices and methods
    • Map, filter and reduce
    • Deleting elements
  • Python Tuples
    • Creating Tuples
    • Advantages of Tuple over List
    • Comparing tuples
    • Deleting a Tuple
    • Slicing of Tuple
    • Built-in functions with Tuple
  • Sets
    • How to create a set?
    • Iteration Over Sets
    • Python Set Methods
    • Python Set Operations
    • Union of sets
  • Dictionaries
    • How to create a dictionary?
    • Python Dictionary Methods
    • Copying dictionary
    • Python List cmp() Method
    • Programming examples for all type of data types

Module 4 Functions

  • What is a function?
  • How to define and call a function in Python?
  • Types of Functions
  • How Function Return Value?
  • Types of Arguments in Functions
  • Rules to define a function in Python
  • Nested Functions
  • Call By Value, Call by Reference
  • map(), filter(), reduce() functions
  • Anonymous Functions/Lambda functions

Module 5 Modules, Packages, Date & Time

  • Python Module
    • What is a Module?
    • Types of Modules
    • The import Statement
    • The from…import Statement
    • import * Statement
    • Creating User defined Modules
  • Python Packages
    • What is a Package?
    • Introduction to Packages?
    • Importing module from a package
    • Creating a Package
    • Popular Python Packages
  • Python Date and time
    • How to Use Date & DateTime Class
    • How to Format Time Output
    • Calendar in Python
    • The Time Module
    • Python Calendar Module
    • Practical examples

**Module 6 **

  • File and Error Handling
  • Python File Handling
  • What is a data, Information File?
  • How to create a Text Fill and Append
  • Data to a File and Read a File
  • Closing a file
  • Read, read line, read lines, write, write lines
  • Renaming and Deleting Files
  • Directories in Python
  • Python Error Handling
  • Python Errors
  • Common RunTime Errors in PYTHON
  • Chain of importance Of Exception
  • Exception Handling
  • Try & Except
  • Try, Except & else
  • Python Custom Exceptions
  • Ignore Errors
  • Practical Examples

**Module 7 **

Classes and Objects

  • Introduction to OOPs Programming
  • Object Oriented Programming System
  • OOPS Principles
  • Define Classes
  • Creating Objects
  • Class variables and Instance Variables


  • Basic concept of Object and Classes
  • Access Modifiers
  • How to define Python classes
  • Python Namespace–
  • What is Inheritance?
  • Types of Inheritance?
  • Polymorphism
  • Encapsulation
  • Abstraction
  • Interfaces
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Database Communication

Database Communication

  • What is Database?
  • Types of Databases?
  • What is DBMS, RDBMS?
  • Working with MysqlDB
  • How to create a database?
  • How to upload data into table
  • Integration of database and output of other program
  • Creation of MariaDB users
  • Working with remote database storage
  • Altering the table using python
  • Different types of Constraints in MySQL
  • Functions in MySQL
  • SELECT Statements in MySQL
  • Different types of Operators
  • If Conditions in MySQL
  • GROUP Functions in MySQL
  • Different types of JOINS in MySQL
  • Analytical Functions in MySQL
  • SET Operators in MySQL
  • Installation of mysql database connection
  • Creating of mysql database using python
  • Creating of mysql tables using python
  • Inserting only one record to table using python
  • Inserting multiple-records to table using “execute many” methods using python
  • Updating table records using python
  • Deleting table records using python
  • Selecting of table records using 'WHERE’ clause.
  • Fetching of multiple-records from two or more tables
  • using joins in tables using python
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Statistical Analysis with DataTab

What is DataTab?

Datatab is an advanced user-friendly statistics analysis web application used by professionals, researchers and data scientists to conduct data science experiements. DataTab is a smart tool with incredible features and intutive user interface to facilitie quick results on all inferential statisics related resaerch and experiments.

Descriptive Statistics

Getting started

  • How statistics is different from all other sciences
  • What is the fundamental thought process of a statistician and
  • how are statistical results used in decision making.
  • Where does data come from? - Events , Observation, Data &
  • Distribution
  • Different kinds of Data and the fundamental difference.
  • Different kinds of Statistics
    • Descriptive, Inferential & Prescriptive Statistics
  • Understanding a data experiment
  • Population vs Sample
  • Basic statistics with practical meaning
  • Measures of central tendency
  • Range, Class, and Frequency Tables
  • Variance & Standard deviation


  1. Normalization & why it's done
  2. Standard Score & how it helps
  3. Deviation Scores
  4. Understanding the Deviation Score with an Example

Hypothesis Testing

  1. What is a Hypothesis?
  2. What are Null & Alternative hypotheses - Examples
  3. Calculating the P-Value & Significance Level Alpha
  4. T-Tests (one sample, two samples)
  5. The Chi-Square Test of Independence
  6. Test of Independence and Tests of Homogeneity
  7. Conclusion, Exercises, & Solutions

Introduction to Probability

  1. Understanding the definition of probability
  2. When to add probabilities and when to multiply probabilities
  3. Data, Distribution and Probability Density Function: Practical uses
  4. The Central Limit Theorem and what it means
  5. Moments
  6. Covariance and Co-relation: Foundation and Interpretation

Conditional Probability & Bayesian Analysis

  1. Intuitive understanding of Conditional Probability
  2. Understanding Naive Bayes Probability theorem
  3. Application of Naive Bayes in Drug testing
  4. Hands-on Application of Bayesian analysis in spam filtering.

Introduction to DATAtab

  1. Importing your data
  2. Data pre-processing
  3. Defining the NULL, NAN, and NV
  4. Color Setting
  5. Defining the Variables
  6. Discretization
  7. Data Labelling and Encoding in DATAtab

Basic visualization using DATAtab

  1. Box plot, Bar chart, Histogram, Scatter plot
  2. line chart, Bland-Altman plot, Pareto chart

Advanced computation

  1. Correlation
  2. Regression Analysis
  3. Hypothesis Testing
  4. t-Test
  5. Mann-Whitney U-Test
  6. Chi-Square Test
  7. Anova
  8. Survival Analysis
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Machine Learning

Introduction to Machine learning

  1. What is Machine learning and real world examples
  2. Machine, it's language and evolution into AI
  3. A short trip on evolution of mathematics and science.

1.Introduction to python- why Python? 2.Installing the IDE's and related libraries. 3. Python hands on 4. Running python scripts 5. MatplotLib 6. Introduction to Pandas Introduction to Numpy

  1. Types of data
  2. Mean, Median , Mode using python
  3. Standard deviation using python
  4. Probability Density function and Probability mass function
  5. Common data distribution, PDF, PMF, Lorenz Curves and Poisson's
  6. Hands On Activity Covariance and Correlation
  7. Probability Basics
  8. Problems on Conditional Probability
  9. Solution Conditional Probability
  10. Baye’s Theorem
  1. Supervised vs. Unsupervised Learning, and Train/Test
  2. Linear Regression
  3. Polynomial Regression
  4. Logistic Regression with hands on activity
  5. Multivariate Analysis: Predict housing Prices
  6. Preventing Overfitting of a Hypothesis
  7. Bayesian Methods
  8. Implementing a Spam Classifier with Naive Bayes
  9. K-Means Clustering
  10. Hands in Activity Clustering people based on income and Education
  11. Measuring the chaos in data: Entropy
  12. Decision Trees
  13. Random Forest
  14. Project: Apply Decision Trees concepts
  15. Ensemble Learning
  1. Bias Variance Tradeoff
  2. Hands On Activity K-Fold Cross-Validation to avoid over fitting Data
  3. Cleaning and Normalization
  4. Hands On Activity Cleaning web log data
  5. Normalizing numerical data
  6. Hands On Activity Detecting outliers
  7. Feature Engineerin
  1. Meditations on reference systems(Cartesian, spherical etc) and linear algebra
  2. Support Vector Machines(SVM)
  3. Project: Apply SVM concepts using Scikit
  4. Principal Component Analysis(PCA) and Dimensionality Reduction
  5. PCA example with the iris data set
  6. Data Warehousing: ETL and ELT concepts
  1. User-Based Collaborative Filtering
  2. Item-Based Collaborative Filtering
  3. Project: Finding Similar Movies
  4. Improving the Results of Movie Similarities
  5. Project: Movie Recommendations Engine
  6. Improve the recommenders results
  1. Spark and RDD
  2. Introduction to MLLib
  3. Exercise Decision Trees in Spark
  4. K-Means Clustering in Spark
  5. TF -IDF : Term frequency and Inverse
  6. Searching documents with Spark
  1. AB Testing
  2. T-Tests and P-Values.
  3. Hands-on With T-Tests
  4. K-Means Clustering in Spark
  5. When to stop an Experiment
  6. AB Testing Summary
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Deep Learning

Deep Learning

  1. Logistic regression
  2. Neural Network Game
  3. Deep Learning concepts
  4. Biological Motivation of Neural Networks and
  5. Historical
  6. Deep Learning using Tensorflow on Google's
  7. tensorflow playground
  8. Tensor Flow Theory
  9. Keras
  10. Convolution Neural Networks(CNN’s)
  11. CNN for pattern recognition in hand writing
  12. recognition
  13. Recurrent Neural Networks(RNN)
  14. Natural Language Processing
  15. Sentiment Analysis.
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Model Deployment

Model Deployment

  1. Cloud Computing
  2. Aws Cloud Concepts with Deployment
What our

students say

My training at Certisued has really contributed towards my long-term goals of becoming a Quantitative Strategist. We were guided constantly under Vijay sir, which gave us the liberty of taking risks. My core project was based on Machine learning using stocks data and applying some advanced algorithms to it. I am recommending this to every curious enthusiast out for sure!

Mannat Soni

Computer Science Student, Punjab University

It's that not every hero wears a cape, it's that not every person can help, its that not every well educated person can teach but you as a person has taught us very well till now with some awesome techniques and without using those primitive techniques like reading out the PDFs and stuff. It was a great experience. Thank you for making this course so interactive and fun learning.


Student, Manipal Univeristy

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Data Analyst


Machine Learning Engineer

Workshops across india

Manipal University

Asked Question

Some of the frequently asked questions about the course are answered here


what is Artificial Intelligence and Machine Learning?

Artificial Intelligence (AI) is the development of computer systems capable of performing tasks that typically require human intelligence. Machine Learning (ML) is a subset of AI that focuses on training algorithms to learn patterns from data, enabling systems to improve performance over time. AI encompasses a broader scope, while ML specifically involves algorithms adapting and making predictions based on data. Both AI and ML aim to enhance computer capabilities, enabling them to execute complex tasks and make informed decisions without explicit programming.


What will I learn from this course?

In this AI and Machine Learning (AIML) course, you will learn foundational concepts of AI, including machine learning algorithms and techniques. You'll gain practical skills in data preprocessing, feature engineering, and model evaluation. Additionally, you'll understand how to apply AI and ML methods to solve real-world problems, such as classification, regression, and clustering tasks. By the end of the course, you'll be equipped with the knowledge and tools to develop AI-powered solutions and analyze data effectively.


I do not know anything about Python programming. Can I join this course?

YES, YOU CAN. We assure you that you do not have to know anything about programming or coding. We will teach Python programming from the scratch just like how you initially learn alphabets in any general conversation languages like English. With our one-to-one mentorship and your continuous effort, you will become a fluent Python programmer


Upon completing the AI and Machine Learning course, what career paths become available?

The Upon completing the AI and Machine Learning (AIML) course, you'll be qualified for various career paths in technology and data-driven industries. Some potential roles include Machine Learning Engineer, Data Scientist, AI Researcher, Data Analyst, Business Intelligence Analyst, and AI Consultant. You could work in diverse sectors such as healthcare, finance, e-commerce, and more, applying AI and ML techniques to solve complex problems and drive innovation.


Do I get job assistance for this course?

We provide 100% job assistance. Our program is designed in such a way that we groom you to become industry-ready with Certisured’s unique placement program. We make sure that our students are well placed and industry ready in all the aspects which includes resume preparation, video resume, linked profile optimization, portfolio creation, blogs by students, and a certification that has a high value in the industry.


What is the duration of the course?

The course is set out for a duration of 6 months. The course is tailored to accommodate enough time to learn the data analysis concepts and also practice extensively to become a master in data analytics using Python, Power BI, and SQL.


What kind of datasets do I get to work on?

All the datasets used for class demonstrations and provided for mini projects/Capstone projects are industry relevant exclusive real-life datasets. You will get an exposure to industry level Artificial Intelligence and Machine Learning pipeline while working on these real-life datasets.


Is this an online course or an offline course?

The course is offered in both online and offline modes. For offline classes, you will find Certisured to be one of the best and most state-of-the-art learning environments for offline classes. For online classes, we deliver the same offline-level immersive instructor-led training with the help of our unique Smart Panel.


Can you please tell me about the certificate?

A Course Completion Certificate will be awarded upon the completion of the course. A Course Excellence Certificate will be awarded upon successful completion of the course and successful completion of the mini projects and capstone projects as defined. The certificates awarded by Certisured are well recognized in the industry and have lifetime validity.


What are the payment options?

You will have the following options to make a payment.

  • One-shot payment: A special discount will be given for one-shot payments.
  • Payment on installments: You can also make the payment in 0% interest installments.

We accept payments through cash, UPI payments, debit card, and credit card.


Is there any refund policy



Pricing & Discounts

Early bird entries will get a discount of up to 15% on the standard package. A new batch starts every month 15th and any student enrolled on or before the 7th of the respective month will be eligible for the early bird offer.

Explore Course Information and Exclusive Deals

Use the appropriate coupon codes to get 15%

Extra benefits !
  • Complete course and mentorship
  • 5+ hands on projects
  • Assistance with Portfolio, Resume and Placement
  • Mock Interview Preparation
  • Assured placement support

Register for the demo class

If you want to know more details or talk to the instructor, register below for the demo class. Those who attend the demo class will get a coupon code too!