Data Science Training Programme
Abhishek singh
17 modules
English
Certificate of completion
Lifetime access
"Unlock the Power of Data with Our Comprehensive Data Science Training Programme - Gain Skills and Insights to Make Informed Decisions!"
Overview
The whole course duration is around 6 to 8 Months. Overall 65+ real time projects.
After the completion of this course, the selected students will get 3 months of internships.
- Comprehensive Data Science training course
- Covers key concepts, tools, and techniques
- Designed to help you excel in the field
- Gain in-demand skills
- Kick-start your Data Science career today
What you will learn
Python
Learn Python programming, mastering syntax, data structures, and algorithms, developing versatile skills for web development, automation, and data science.
Probability & Statistics
Master probability and statistics, gaining skills in data analysis, hypothesis testing, and probability models for informed decision-making and predictions.
Machine learning & Deep Learning
Explore machine learning and deep learning, understanding algorithms, training models, and applying neural networks for predictive analysis and pattern recognition.
Natural language Processing
Learn Natural Language Processing, decoding and analyzing human language, developing applications for text analysis, multi modal sentiment analysis, and many more.
Big Data Tools and Technologies
Learn about Big Data tools and technologies, mastering storage, processing, and analysis techniques for extracting valuable insights from vast datasets efficiently.
Tableau & PowerBI
Learn data visualization, analysis, and storytelling with Tableau and Power BI, mastering tools for impactful business insights through visuals.
Cloud Platform and ChatGPT
This section explores AWS, Azure, and GCP cloud platforms. It also details prompt engineering and demonstrates using ChatGPT for data science projects.
MLops & Airflow
Explore MLOPS principles and Airflow for orchestrating machine learning workflows, mastering deployment, monitoring, and scaling for efficient model management.
Modules
Chapter 1- Python
18 attachments
Introduction to Python
Identifiers and Keywords in Python
Data Types & Type casting
Basic Operation and Operator in Python
Indentation, Comment and Inputs
Conditional statements in Python
Loops in Python
Data Structures in Python
Function in Python
Classes and OOPs concept
Regular expression
File handling
Exception handling
Libraries and modules
API development
Project 1
Project 2
Project 3
Chapter 2- Advance Python
9 attachments
Numpy
Pandas
Matplotlib, Seaborn and Plotly
Command line Arguments
Handling Data and time
Image Processing using OpenCV
Advance Image Processing
Project 4
Project 5
Chapter 3- Statistics
25 attachments
Introduction to statistics
Types of statistics
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
Learning about T-test
One Sample, two Sample T-test
Anova, One Way Annova and Two way Annova
Chi-square Analysis
Parametric and Non-parametric tests
Project 6
Project 7
Project 8
Chapter 4- Probability
12 attachments
Introduction to probability
Bayes Theorem
Bernoulli’s Theorem
Independent & Dependent Events
Conditional Probability
Distribution and its Types
What is Central Limit Theorem
Skewness & Kurtosis
Sampling and different sampling techniques
What is Outlier and its Significance
Project 9
Project 10
Chapter 5- Knowledge on Database and its types
13 attachments
Introduction to Database
Types of Databases
Relational Database
Object-Oriented Database
Distributed Database
NoSQL Database
Graph Database
Cloud Database
Centralization Database
Operational Database
Components of Database
Project 11
Project 12
Chapter 6- SQL
24 attachments
Introduction to Structured Query Language
What is RDBMS
Introduction and Types of SQL Operators
Creating Databases and Tables
Explore Entities and Relationships
Entity Relationship diagram
UML diagram
Various Types of SQL statements
SQL Joins
Multiple Joins
Subqueries
How to write Subqueries in SQL
Views
functions
Stored Procedure
Transactions
String, transformation, and Regex
Date time manipulation
Windows function
Project 13
Project 14
Project 15
Project 16
Project 17
Chapter 7- Data Science
20 attachments
Introduction to Data Science
Methodologies in Data Science
Understanding the Business Problem
Domain Knowledge
Data Collection
Checking the quality of data
Data Preprocessing and Cleaning
Exploratory data analysis
Feature engineering
Model building
Model evaluation
Model tunning
Model deployments
Model monitoring
Model Optimization
Model security and Privacy
Continuous improvement
Project 18
Project 19
Project 20
Chapter 8- Machine learning
21 attachments
Introduction to Machine learning
Types of Machine learning
Linear models
Regularization algorithm
Generalized linear models
Decision Tree algorithm
Ensemble model
Bagging algorithm
Boosting algorithm
Voting algorithm
Stacking algorithm
Support Vector Machine
K nearest neighbors
Naive Bayes classifier
Segmentation Techniques
Association Rule algorithm
Project 21
Project 22
Project 23
Project 24
Project 25
Chapter 9- Advance Machine learning
11 attachments
Feature engineering algorithm
Feature reduction algorithm
Time series algorithm
Automated machine learning tools
Casual Machine learning
Explainable AI
Project 26
Project 27
Project 28
Project 29
Project 30
Chapter 10- Deep learning
23 attachments
Introduction to deep learning
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
Introduction to Tensorflow
Artificial Neural Network
Convolutional Neural Network
Recurrent Neural Network
Restricted Boltzmann Machine
Autoencoder
Self Organizing Maps
Neural Style transfer
Hybrid Learning algorithm
Introduction to Transformers
Introduction to Bert
Large Language models
Project 31
Project 32
Project 33
Project 34
Project 35
Chapter 11- Natural language processing
12 attachments
Introduction to NLP
Typical NLP Tasks
Understanding text data
Text preprocessing in Python
Extracting Features from Text
Cross-lingual NLP
Multilingual NLP
Transfer Learning in NLP
Ethical considerations
Project 36
Project 37
Project 38
Chapter 12-PowerBI
12 attachments
Introduction to powerBI
Charts and Graphs
Power Query
Dax Expression
PowerBI Service
Row level and Page level Security
Scheduling, Data Gateway, and Mobile APP
Python, Machine learning and SQL in PowerBI
Project 39
Project 40
Project 41
Project 42
Chapter 13- Tableau
14 attachments
Introduction to Tableau
Working with Metadata
Data Blending
Filters
Charts and Graphs
Advance graphs and charts
Level of Details
How to create Dashboard and tell story
How to create story
Dashboard deployment
Dashboard Monitoring
Project 43
Project 44
Project 45
Chapter 14- Big Data
13 attachments
Introduction to Big data
Big Data Processing
Hadoop
Map Reduce
Apache Spark
Apache Airflow
Apache Kafka
Introduction to DataBricks
Project 46
Project 47
Project 48
Project 49
Project 50
Chapter 15- Docker
15 attachments
Introduction to Docker
Docker Architecture
Docker Installation
Docker Containers
Docker Images
Docker Volumes
Docker Networking
Docker Compose
Docker Swarm
Docker Security
Docker Orchestration
Docker Monitoring and Logging
Docker Integration with CI/CD
Project 51
Project 52
Chapter 16- Cloud platforms
11 attachments
Azure ML
AWS ML
Google ML
Project 53
Project 54
Project 55
Project 56
Project 57
Project 58
Project 59
Project 60
Chapter 17- MLops
27 attachments
Introduction to MLops
Architecture of MLops
Continuous Integration and Continuous Deployment (CI/CD)
Version control
Model training and evaluation
Model deployment and serving
Infrastructure management for ML systems
Model monitoring and performance tracking
Data versioning and management
Experiment tracking and management
Reproducibility and pipeline automation
Scalability and resource management
Governance, compliance, and security in ML systems
Data preprocessing and feature engineering
Hyperparameter tuning and optimization
Model testing and quality assurance
Error handling and fault tolerance in ML pipelines
Model retraining and updating
A/B testing and experimentation
ML model explainability and interpretability
Production monitoring and alerting
Model lifecycle management and retirement
Project 61
Project 62
Project 63
Project 64
Project 65
Certification
When you complete this course you receive a ‘Certificate of Completion’ signed and addressed personally by me.
Testimonials
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 Naveed
MS Student (UK)
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 Sharma
Undergrad Student (India)
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 Bashit
MS Student (UK)
Great experience. They have good expert team to delivery the best training Thank you
Aishwarya
MS Student (UK)
Before starting this course, I had no previous knowledge of machine learning, but I was keen to learn something new. The course is extremely well structured, presents the concepts (some very difficult) in a clear and almost intuitive manner with going too much into detail with mathematical proofs, making the course accessible to anyone. The mentors were fantastic and provided prompt responses, links to tutorials and test cases, which all helped me get through the course.
Prashant Kumar
Undergrad Student (India)
Dataspoof courses teaches data science from the beginner level so it is really easy to understand. It also have same amazing additional resources which is important to build up further knowledge in this field. So i highly recommend this platform to every one who likes to learn data science and it's tools. Thanks.
Duke
Undergrad Student (Myammar)
FAQs
What are the prerequisites for enrolling in the data science training program?
You just need a laptop and internet connection
What is the course duration
The duration of the program typically ranges from 6 to 8 months. This timeframe provides ample opportunity for in-depth learning and comprehensive coverage of the course content.
Will the training program include hands-on projects or real-world applications?
Yes, there will 65+ project based on the real world application
Is there a flexible schedule available for working professionals?
Yes, we offer a flexible schedule to accommodate working professionals. We understand the importance of balancing work commitments with professional development. Therefore, our courses provide options for scheduling that can fit around your work schedule. This allows you to pursue your training and upskilling at a time that is convenient for you.
What is the cost of the data science training program, and are there any payment options or scholarships available?
The program is priced at INR 30,000. We offer the convenience of payment in two equal installments (EMIs) if preferred. This allows you to manage the cost of the program in a more flexible and convenient manner.
Is the fees refundable?
Nope
Do you provide placement also?
While we do not offer direct job placements, we do provide an internship program for a duration of 3 months to a selected few students upon completion of the course. This internship opportunity allows students to gain practical experience and apply their learnings in a real-world setting. It serves as an additional stepping stone in their journey towards building a successful career.
Is the course accessible lifetime
Yes, the course provides lifetime access to the learning materials. Once you enroll in the program, you will have access to the course content for a duration of 3 years. This extended access allows you to revisit the materials, refer back to the lessons, and continue learning at your own pace even after completing the course.
About the creator
Abhishek singh
Our courses are designed with a focus on real-time implementation of Big Data, Machine Learning, and Cloud technologies in live projects. We emphasize hands-on experience and practical learning throughout our courses. Each course provides comprehensive knowledge of a technology, starting from the basics and progressing to advanced concepts. Our lectures are structured to explain the code in a manner that is easily understandable, even for individuals without a technical background.
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