5 monthsData Science Certificate Program

Become a job-ready data scientist.

A complete, end-to-end online data science program — Python, statistics, SQL, machine learning, deep learning, NLP, Power BI, big data, cloud and MLOps — with real-world projects and live weekend classes.

Book a free demo → Apply now

Designed for freshers and working professionals · no prior experience required

5 monthsProgram duration

150+ hrsLive training

14Curriculum modules

WeekendsLive classes

CapstoneIndustry project

CareerDedicated support

Program at a glanceComplete End-to-End Data Science

🗓️ScheduleSat & Sun · 2.5 hrs
⏱️Time commitment~10 hrs / week
🐍Main languagePython
🎓CertificateFrom DataSpoof
💳Program fee₹1,29,999
📆EMIFrom ₹25,999 / month

Reserve your seat → Book a free demo Ask about EMI on WhatsApp

Our learners got placed at top companies

Program highlights

Everything you need to land a data science role.

🗓️5-month programA structured path from the basics to advanced techniques.
🎯For freshers & professionalsFresh graduates, career switchers and working professionals.
📺Weekend live classesSaturday and Sunday, 2.5 hours each.
⏱️150+ hours of live trainingLearn live with your instructor — not just recordings.
👨‍🏫Experienced facultyTaught by a practising data scientist and corporate trainer.
🧪Mock interviews & assignmentsPractice that builds confidence before the real thing.
🏗️Industry capstone projectAn end-to-end project for your portfolio.
🎓Certificate from DataSpoofShowcase your new skills to employers.
💼Resume & LinkedIn optimizationStand out to recruiters with a sharper profile.

Your learning path

From Python basics to production ML.

Everything is covered from the basics — each stage builds on the last.

🐍

Python01

📊

Statistics02

🗄️

SQL & databases03

🤖

ML & deep learning04

📈

Power BI & big data05

☁️

Cloud & MLOps06

Complete curriculum

14 modules. 280+ topics.

What is Python
Identifiers and Keywords in Python
Data Types & Type casting
Basic Operation and Operator in Python
Operators in Python
Indentation, Statements and Comment
Data Structures in Python — Array, String, Lists, Tuples, Set and Dictionaries
Conditional statements in Python
Loops in Python
Function in Python
Lambda functions
Classes and OOPs concept
Regular expression
File handling
Exception handling
Command line Arguments
Handling date and time

Introduction to Data Science
Data Science Lifecycle
Types of Data
Checking the quality of data
Exploratory data analysis
Feature Engineering
Tabular Data Preprocessing
Image Data Preprocessing
Text Data Preprocessing

Introduction to statistics
Types of statistics
Descriptive and Inferential
Descriptive Statistics
Variables and Types of Variables
Measure of Center and Measure of Spread
Measures of Central Tendency
Measures of Dispersion
Mean, Mode, Median
Range, Standard Deviation, Variance, Quartile, IQR
Covariance and Correlation between data

Inferential Statistics
Sample v/s Population
Hypothesis Testing
Null and Alternative hypotheses
Type I error vs Type II error
Establishing a rejection region and a significance level
What is the p-value and why is it one of the most useful tools for statisticians
Learning about T-test
One Sample, two Sample T-test
Analysis of Variance
Chi-square Analysis
Parametric and non-parametric tests

Introduction to Database
Component of Database
Types of Databases
 Relational Database,
 Object-Oriented Database,
 Distributed Database,
 NoSQL Database,
 Graph Database,
 Cloud Database,
 Centralization Database,
 Operational Database
 Vector Databases

Introduction to Structured Query Language
What is RDBMS-Relational Database Management System
Introduction and Types of SQL Operators
Creating Databases and Tables
Explore Entities relationships
DDL & DML Statement
Select Statement, Aggregate Functions
Insert into, Where, Order By, Distinct, Group By, Like, In, Between Operators,
Limit Aliases, and & or Clause
Update & Delete Query
SQL Joins-What are Joins, Inner Join, Left Join, Right Join, Full Join
Multiple Joins-Joining More than two tables

Subqueries
How to write Subqueries in SQL
Conditional Table Expression
Windows function in SQL
Views, functions, and Stored Procedure
Transactions
String, transformation, regex
Date time manipulation

Introduction to Machine learning
Types of Machine learning
Application of Machine learning
Linear models
Introduction to linear regression
Mathematics behind linear regression
Least squares method
How to evaluate regression model
Python implementations
Interpretation of Model coefficient
Assumptions of linear regression
Statistical test in linear regression
Python implementations
Interpretation of Model diagnostic plot
Introduction to Multiple linear regression
Features Selection and its types
Python implementations
Regularization algorithm
What is regularization
Lasso regression and Its implementation in Python
Ridge regression and Its implementation in Python
Elastic Net regression and Its implementation in Python
Generalized Linear models
What is GLM model
Introduction to logistic regression
Mathematics behind logistic regression
Cost Function
Odds & Odds ratio
Model Evaluation
Python Implementation and Interpretation
Decision Tree algorithm
Decision Tree Algorithms
Attribute Selection Measures
Entropy &Information Gain
Steps to Estimate Entropy &Information Gain
Issues with Decision Trees
Bias Variance Trade Off
Decision Tree Applications
Python Implementation
Ensemble model
Introduction to ensemble
Bagging, boosting, Stacking and Blending
Introduction to bagging
Random forest
Reason to use
Random Forest
Random Forest Types
Random Forest Applications
Python Implementation
Hyperparameter Tuning
Introduction to boosting
XgBoost, Adaboost, catboost, LightGBM
Implementation
Introduction to stacking
Implementation
Introduction to blending
Implementation
Support Vector Machines
Types of SVM
Hyperplane in the SVM algorithm
Large Margin Intuition
Kernel in SVM
SVM Implementation in Python
K nearest Neighbors
Introduction to KNN
Mathematics behind KNN
Implementation in Python
Naïve bayes classifier
Introduction to naïve bayes
Mathematics behind Naïve bayes
Curse of Dimensionality
Implementation in Python
Dimensionality reduction algorithm
What is dimensionality reduction
Introduction to Principal component analysis
Math behind it
Implementation in Python
Segmentation techniques
What is Clustering?
K-Means Clustering
When to use K-Means Clustering?
What is K?
Euclidean Distance
K-Means Clustering Example
Implementation in python
Agglomerative clustering
Implementation in python
Association rule algorithm
Introduction to association rule
Apriori algorithm
Market basket optimization
Implementation
Advance Machine learning
Casual Machine learning
Offline Machine learning
Adversarial Machine learning
Explainable AI
Knowledge Distillation
Time series analysis and forecasting
Various time series algorithm with their implementations
Various deployment strategies

Introduction
Real Life Applications of Deep Learning
Difference between Machine learning and deep learning
Challenges of Deep learning
Architecture of Deep learning projects
Various frameworks in deep learning
Deep learning with TensorFlow
Introduction to TensorFlow
Artificial Neural network
Introduction
Backpropagation
Weight and Bias
Activation function
Deep Neural Networks
Implementation
Convolution Neural Network
Introduction
Mathematics behind CNN
Famous CNN Architectures
Transfer Learning
Implementation
Recurrent Neural Network
Introduction
Application of RNN
Problems with RNN
Introduction to Long short-term memory
Implementation
Introduction to GRU
Implementation
Introduction to Seq2seq
Implementation
Introduction to encoder and decoder
Implementation
Introduction to transformer and BERT
Implementation
Autoencoder
Introduction
Architecture explanation
Application of autoencoder
Implementation
Generative Adversarial Network
Introduction
Generator
Discriminator
Implementation

Introduction to NLP
What is NLP?
Typical NLP Tasks
Understanding text data
Text preprocessing in Python
Extracting Features from Text
Bag-of-Words
TF-IDF Similarity score
Cosine similarity
Sentiment analysis
Question Generation
Named entity recognition
Custom Named entity recognition

Introduction to PowerBI
Components of PowerBI
Types of charts in PowerBI
How to add buttons in PowerBI
Using Power query to preprocess the data
Intro to the M Language
Data modelling in PowerBI
How to write and run DAX queries
How to create dashboard
How to add security on the dashboard
Publishing the dashboard
Refreshing the dashboard periodically

Introduction to the big data
Data warehouses
ETL and ELT
Introduction to the big data tools
Hadoop and MapReduce
Apache Spark
Hive and Pig
Apache Kafka
Apache Airflow

AWS
Introduction
Creating and setup an account
Introduction to the billing section
IAM
S3
EC2
Lambda
Glue
Sagemaker
Creating an ETL pipeline
Running machine learning code in Sagemaker
Analyzing the data using Athena
Monitoring the logs using CloudWatch
MapReduce on cloud (EMR)
Analyzing the data using QuickSight

Introduction to MLOPS
Data Management and Versioning
Experimentation and Model Development
Continuous Integration/Continuous Deployment
Model Serving and Deployment
Containerization and Orchestration
Monitoring and Logging
Model Maintenance and Retraining
Infrastructure and Resource Management
Security and Compliance
Free
ChatGPT hacks

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Tools you'll use

The industry-standard data science stack.

PythonSQLMySQLscikit-learnTensorFlowHugging FacePower BIGitHubHadoopApache SparkHiveApache KafkaApache AirflowAWSSageMakerAthenaQuickSightDVC

Career support

Get industry-ready with dedicated career support.

🔎Support with your job search

📄Help building a strong resume

🎤Interview preparation

🤝Connect with potential employers

💼LinkedIn profile optimization

🧭Guidance for career growth

Learn from experienced faculty

Abhishek Kumar Singh

Data Scientist · Corporate Trainer

8+ yrsData science, AI & big data

45+Corporate trainings delivered

Connect on LinkedIn

As a consultant and trainer, Abhishek specialises in teaching the latest data science, machine learning, deep learning and big data technologies — and in helping companies make the most of their data.

He's passionate about empowering students to use data to drive innovation and solve real-world problems. His practical training helps you build data-driven projects from the ground up, developing your skills in data analysis, modelling and visualisation.

Organizations trained

DeloitteGSKShellCapgeminiMetLifeTruistJP MorganWells FargoElevance HealthFDICU.S. Department of EducationTravel + Leisure

Reviews

What our data science learners say.

Abhishek was approachable and always ready to help. Students were provided helpful learning material for each class. The course content spanned a wide range of topics, from the basics to advanced concepts, ensuring a solid understanding of each area. It was well-structured and gave a comprehensive overview of various aspects of data science, making it an enriching learning experience. I would highly recommend this course to anyone looking to deepen their knowledge in data science.

Pavan KoutharapuML Engineer · Way2News

The instructor is friendly and supportive, providing valuable interview guidance and short notes for each class. The flexible schedule and hands-on projects enhance learning. Regular feedback and a focus on real-world applications built my confidence. Overall, it was a fantastic course that significantly improved my skills.

Kaushal GoreData Analyst

I recently completed the Data Science course, and I must say it was an outstanding experience. The course content was comprehensive and well-structured, covering all essential aspects of Data Science from fundamentals to advanced techniques. The instructor was highly knowledgeable and passionate about the subject, making complex concepts easy to understand through engaging lectures and practical examples. One aspect that particularly stood out was the hands-on approach to learning. The seminar provided ample opportunities for practical application of the concepts learned, through coding exercises, real-world case studies, and projects. This not only enhanced my understanding but also equipped me with valuable skills that are directly applicable in the field of Data Science. Furthermore, the support from the instructor was exceptional. He was always available to clarify doubts and provide guidance. Overall, I would highly recommend this course to anyone interested in Data Science. Whether you're a beginner looking to start your journey in this field or an experienced professional seeking to expand your knowledge, this seminar offers tremendous value and expertise.

Petros CholidisGenAI Engineer

I would highly recommend DataSpoof to anyone looking to get started in data science or expand their existing knowledge. I was thoroughly impressed with the quality of instruction and the level of detail provided throughout the course. The instructor was knowledgeable and engaging and was always available to answer questions and provide feedback.

Rabia NaveedStudent · Brunel University

Dataspoof definitely go above and beyond, by being thoughtful and productive in all aspects. They were creative in coming up with ways to go beyond what we asked them to do. They clearly understood our training and learning requirements and were able to recommend the content and courses they we would need.

Abdul BasitStudent · Brunel University

It was really impressive and incredible to be the part of this learning journey. The way of teaching was quite different from other institutions, which made it more amazing and helpful. Everyone should try this once and figure out own experience.

Aman SharmaStudent · Vidyapeeth

Video reviews

Program fee

Invest in your data science career.

One program. Live classes, projects, certificate and career support — all included.

Complete End-to-End Program

Data Science Training

₹1,29,999

or EMI starting from ₹25,999 / month

Reserve your seat → Book a free demo first

Secure payment via Razorpay

Learning

5 months · 150+ hours of live training
Weekend live classes — Sat & Sun, 2.5 hrs each
14 modules from Python to MLOps
Assignments, case studies and mock interviews

Outcomes

Industry-oriented capstone project
Certificate from DataSpoof
Resume building & LinkedIn optimization
Dedicated career support

Questions about the fee?

We're happy to talk through EMI options and whether the program is right for you.

💬

Chat on WhatsApp+91 83182 38637 · EMI & demo questions

Fastest 🗓️

Book a free demo30-minute call on Calendly

Free ✉️

Email ussupport@dataspoof.in

Anytime

Certificate of completion Data Science Training Awarded by DataSpoof

FAQ

Frequently asked questions.

Still unsure? Book a free demo and ask us directly.

The program runs for around 5 months.

This Data Science course is of 5 months and we expect you to put 10 hrs per week to attend the live sessions and complete assignments, case studies, and project work.

Yes. EMI starts from ₹25,999 per month. For details, message us on WhatsApp at +91 83182 38637.

Classes run on Saturday and Sunday, 2.5 hours each day.

Yes. Book a free demo on our calendar, or message us on WhatsApp at +91 83182 38637 and we'll schedule one.

Python is the most widely used programming language in data science — and it's the main language in this program.

We provide 24/7 support to our learners for their Data Science doubt resolutions. You can get the required support using our dedicated chat support or directly raise the ticket through email dataspoof007@gmail.com

No, our job assistance is aimed at helping you land your dream job. It offers a potential opportunity for you to explore various competitive openings in the corporate world and find a well-paid job, matching your profile. The final hiring decision will always be based on your performance in the interview and the requirements of the recruiter.

Yes, certainly this data science training course will help you land in data science jobs upon online course completion.

Start your journey

Your data science career starts here.

Book a free demo to see how the classes work, or reserve your seat in the next batch.

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