Full Stack Data Science Certification Training Course

Empower Your Data Journey with Full Stack Data Science Excellence

Course Fees : 38500 40500

Advance Fees : 1000

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Data science is in high demand globally, and India is no exception. In India, data scientist salaries typically range from ₹6,00,000 to ₹18,00,000 per year, with variations based on location and experience. 

Course Overview

Our Full Stack Data Science Certification Training program is designed to equip you with the complete skill set needed to excel in the dynamic field of data science. Whether you're new to the world of data or an experienced professional looking to enhance your capabilities, this comprehensive course will empower you to tackle the entire data science pipeline. From data collection and preprocessing to machine learning model development, deployment, and data visualization, you'll gain hands-on experience through real-world projects and interactive labs. Our expert instructors, who bring industry insights to the classroom, will guide you on this journey.


  • High Demand: Data science professionals are in high demand across industries, offering abundant job opportunities and career growth.

  • Competitive Salaries: Enjoy competitive salaries, with the potential for significant earning potential as you gain experience.

  • Versatility: Acquire a versatile skill set that allows you to work in various domains, including finance, healthcare, e-commerce, and more.

  • Data-Driven Decision-Making: Help organizations make informed decisions and gain a competitive edge by harnessing the power of data.

  • Hands-On Learning: Apply your skills to real-world projects and build a robust portfolio to showcase to potential employers.

Prerequisite: Basic programming skills (preferably Python), mathematics understanding, and a curious, problem-solving mindset.

Unlock your potential in the data science field with our Full Stack Data Science Certification Training. Whether you're a beginner or an experienced professional, our program equips you with the skills and knowledge needed for a successful career in data science. Join us today and embark on a journey towards becoming a proficient data scientist.


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Why TestoMeter

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    Interactive online classrooms
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    Industry Standard Syllabus

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    Comprehensive Skill Set: Cover the entire data science pipeline, from data collection to deployment.

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    Real-World Projects: Apply learning to hands-on, practical scenarios.

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    Expert Guidance: Learn from experienced instructors with industry insights.

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    100% Placement Support: Access dedicated career assistance for job placement.

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    Ethical Data Practices: Understand and adhere to ethical considerations in data science projects.

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    Portfolio Development: Build a robust portfolio showcasing your skills to potential employers.


Discover our comprehensive course curriculum designed to equip you with the knowledge and skills you need to succeed. Dive into a structured learning journey that covers all essential topics and prepares you for real-world challenges

 Installation and Working with Python

 Introduction, why python?

 Versions of Python


 PEP 8 standards

 Coding conventions

 Understanding Python variables  Identifier rules

 Literals

 Keywords

 IDLE and information

 Different ways of execution

 Scripting

 Python Operators

 Understanding python blocks

 Indentation, comments, docstring  Type casting, Unicode etc.

 Mutable and Immutable data types

 Declaring and using Numeric data types: int, float, complex

 Using string data type and string operations

 Defining list and list slicing, its methods

 Use of Tuple data type

 Conditional blocks using if, else and elif
 Nested if, elif ladder
 Simple for loops in python
 For loop using range, string, list and dictionaries

 Use of while loops in python

 Loop manipulation using: pass, continue, break
 Programming using Python conditional and loops block

 Different case studies

 Building blocks of python programs
 Understanding string built-in methods
 List manipulation using built-in methods  Tuple operation
 Set: its methods and manipulation
 Dictionary: its methods and manipulation  functions
 Modules and Packages

 What is OOP
 Class
 Reference variable
 Types of variables
 Types of Methods
 Importing Class
 Constructor
 OOP’s Concepts: Inheritance, Encapsulation, Polymorphism, Abstraction

 File handling in detail: txt, bin, csv

 What is Function?
 Advantages of functions
 Syntax and Writing function

 Calling or Invoking function

 Classification of Functions
 No arguments and No return values
 With arguments and No return values
 With arguments and with return values

 No arguments and with return values
 Recursion
 Python argument type functions:
 Default argument functions
 Required (Positional) arguments function

 Keyword arguments function
 Variable arguments functions
 ‘pass’ keyword in functions
 Lambda functions/Anonymous functions

 map ()
 filter ()
 reduce ()
 Nested functions
 Non local variables, global variables
 Closures
 Generators
 Iterators
 Decorators

 Introduction to files
 Opening file
 File modes
 Reading data from file

 Writing data into file
 Appending data into file

 Line count in File

 CSV module
 Creating CSV file
 Reading from CSV file

 Writing into CSV file

 What is Exception?
 Why exception handling?
 Syntax error v/s Run-time error
 Exception codes – AttributeError, ValueError, IndexError, TypeError...

 Handling exception – try except block
 Try with multiple except
 Handling multiple exceptions with single except block  Finally block
 Try-except-finally
 Try with finally
 Case study of finally block
 Raise keyword
 Custom exceptions / User defined exceptions
 Need to Custom exceptions

 Introduction To DBMS

 Overview of SQL

 Data Manipulation Language

 Data Query Language (DQL)

 Built In Functions

 Set Operators

 Joins

 Sub Queries

NumPy: (Numerical Python)

 Introduction to Numpy

 Datatypes of ndarrays

 Dealing with ndarrays, copies and views

 Arithmetic operations,

 Indexing , Slicing, splitting arrays

 Shape manipulation

 Stacking together different data

Pandas: (Data Analysis)

 DataFrame and Series

 DataFrame operations

 Data Slicing, indexing

 DataFrame functions

 Reading the files- csv, excel

 Boolean filtering

 Storing file in various formats

 Useful DataFrame functions

 Stats using pandas

 Dealing with missing data

 Operations over the data

 Filtering Dataframes

 Descriptive Analysis with pandas

 Handling Missing Values

 Finding unique values and deleting duplicates

 Analysing Outliers

 Creating new categorical features from continuous variable

 Grouby operations

 Grouby statistical Analysis

Matplotlib: ( Data Visualization)

 Introduction to Matplotlib

 Formatting the graph: colors, markers, linestyle, etc

 Customization

 Plotting with list, arrays, pandas

 Line plot, Scatter plot, Pie plot, Bar plot, Histogram etc

Seaborn & Plotly: (Data Visualization)

 Different types of plotting

 Scatter

 Distance

 Histogram

 Pie plot

 KDE plot

 Joint Plot

 Pair plot

 Tableau Introduction

 Tableau Components

 Tableau Extensions

 View Sections

 Dash Board Components

 Data Connection in Tableau Interface

 Graphs/Charts/Bars

 Maps

 Understanding the concept of Artificial Intelligence

 Applications of AI

 Who uses AI?

 AI for Banking & Finance, Manufacturing, Healthcare, Retail and Supply Chain

 Understanding types of data to be dealt with

 AI Landscape - AI v/s ML v/s Deep Learning v/s Data Science

 Data Science based use cases

 Computer Vision Based Use cases

 NLP Based use cases

 AI Project Management & Lifecycle

 Size of Data and its impact in AI/ML Project lifecycle

 The process of Machine Learning

 Supervised & Unsupervised Learning

 Understanding -

 Regression & Classification Problems

 Clustering and Anomaly Detection

 Recommendation System and Dimensionality Reduction

 Time Series Forecasting

 What makes a Machine Learning Expert?

 What to learn to become a Machine Learning Developer?

 Types of Variables – Continuous and Categorical

 Interval, Ratio, Ordinal and Nominal

 Terms – Population, Sample, Descriptive Statistics and Inferential Statistics

 Mean, Median and Mode

 Range, Percentile and IQR

 Variance and Standard Deviation

 Correlation and Covariance

 Business logic with correlation Analysis

 Introduction to Hypothesis testing

 Building a business hypothesis

 One sample t test, independent t test, paired t test

 ANOVA – F test

 Chi Square Test

 Introduction to Machine learning,

 Types of Machine learning

 Concepts of Data Preprocessing,

 Data munging

 Importing the data, functioning over the data

 Arithmetic operations

 Categorical and Continuous data

 Feature Scaling, Selection, Engineering

 Binarization, Normalization, Label encoding

Linear Regression

 Importing data

 Data Selection, operations

 Splitting the data into training and testing

 Call the model, build the model and train

 Find the coefficients and intercept

 Accuracy measurements

 Regression Problem Analysis

 Mathematical modelling of Regression Model  Gradient Descent Algorithm

 Use cases

 L1 & L2 Regularization


Linear Regression – Case Study & Project

 Programming Using python

 Building simple Univariate Linear Regression Model  Multivariate Regression Model

 Apply Data Transformations

 Identify Multicollinearity in Data Treatment on Data  Identify Heteroscedasticity

 Modelling of Data

 Variable Significance Identification

 Model Significance Test

 Bifurcate Data into Training / Testing Data set  Build Model on Training Data Set

 Predict using Testing Data Set

 Validate the Model Performance  MAPE, RMSE and R2 Value


Multi Linear Regression

Importing Datasets

Data preprocessing

Feature scaling

Training and Testing split

Characterizing regression

Find coefficients and intercepts

Logistic Regression

 Assumptions

Reason for the Logit Transform

Logit Transformation

 Hypothesis

Variable and Model Significance

Maximum Likelihood Concept

ROC Curve

Model Specification

Confusion Matrix

Accuracy, Recall, Precision and F1 Score

How to handle overfitting and underfitting

Decision Tree Classification

Forming a Decision Tree

Components & Mathematics of Decision Tree

Regression and Classification Tree

Entropy and Information Gain

Gini Impurity

Decision Tree Evaluation

Feature Importance

Tree Visualization

Hyperparameter Tuning using Grid Search Decision Boundary Analysis


Support Vector Machine

 SVM model

 Importing data, data Selection, operations

 Preprocessing

 Splitting the data into training and testing

 Call the model, build the model and train

 Predictions, Evaluating the algorithm

 Naïve Bayes Classification

 Naïve Bayes working flow

 Probability learning, Applications

 Splitting the data into training and testing

 Stepwise model building

 Predictions & Evaluating the algorithm

KNN Classifier

 Importing data, data Selection, operations

 Pros and cons

 Preprocessing

 Distance metrics

 Splitting the data into training and testing

 Feature scaling

 Call the model, build the model and train

 Predictions, Comparing error rate-k value


KMeans clustering

 Introduction to Unsupervised learning model

 K-means clustering in detail

 Importing the dataset

 Finding the clusters

 Visualize the clusters

 Applying the transformation

 Building the model

 Visualize complete clustered model.


Random Forest Regression

 Concept of Random Forest

 Random Forest Mathematics

 Ensemble learning

 How Random Forest works?

 Advantages and Disadvantages

 Preparing data

 Training the regressor

 Evaluating regressor

 Practical Examples & Case Study

 Examples & use cases using Random Forests

Gradient Boosting Methods

 Understanding Adaboost

 Conceptual understanding and mathematical idea behind adaboost

 Implementing Adaboost with python

 Gradient Boosting trees – understanding logical idea

 Gradient boosting – how gradient descent is used

 Implementing GBM

 Challenges with Gradient boosting

 XGBoost

 Implementing XGBoost with pyhon

 LightGBM

 CatBoost

 Applications of Dimensionality Reduction

 Covariance and Eigen Vectors

 Matrix Decomopsition

 Dimensionality Reduction, Data Compression

 Curse of dimensionality

 Multicollinearity & Factor Analysis

 Concept and Mathematical modelling

 Programming with python

 Streamlit Framework (Python framework for data visualization)

 10+ Projects throughout the course

 Introduction to Agile Methodology

 Support in Technical portfolio building

 Technical round preparation

 Mock Interview

 Resume building assistance

 Daily 1 hr (min)

Sample certificate

Get a sneak peek of the certificate you'll receive upon completing a course exam on TestoMeter! Take a look at what you'll earn as a symbol of your accomplishment.


Rigorous evaluation of data collection, analysis, modeling, and deployment skills through real-world projects and comprehensive exams.

Suraj Tat

Data Engineer

Company: Datametica

Congratulations !!!

Suraj Tat

Data Engineer at Datametica


Akansha Godhke

Data Engineer

Company: Inteliment Technologies

Congratulations !!!

Akansha Godhke

Data Engineer at Inteliment Technologies


Rahul Gaware

Data Analyst

Company: Saama Technologies

Congratulations !!!

Rahul Gaware

Data Analyst at Saama Technologies

(10 LPA)

Shurti verma

Data Scientist

Company: Cuelogic Technologies

Congratulations !!!

Shurti verma

Data Scientist at Cuelogic Technologies

(7 LPA)

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