Data Science Training
Project Overview
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Project Information
Project Summery
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What You'll Learn
Gain in-demand Data Science and Machine Learning skills through real-world projects and hands-on training.
Master the art of building Machine Learning models in Python using libraries like NumPy, scikit-learn, and statsmodels.
Understand how to clean, organize, and prepare data for accurate model training.
Perform advanced analytics such as clustering, dimensionality reduction, and factor analysis.
Develop strong and optimized ML models ready for real-world applications.
Dive into specialized domains including Deep Learning, NLP, and Reinforcement Learning.
Identify and apply the most suitable Machine Learning algorithm for each type of problem.
Gain a solid foundation in NumPy and its essential role in data science workflows.
Visualize and interpret massive datasets using libraries like Matplotlib and Seaborn.
Learn modern Deep Learning techniques such as Neural Networks and Transfer Learning with TensorFlow 2.0.
Explore the fundamentals of Data Engineering and understand the use of big data tools like Hadoop, Spark, and Kafka.
Solve real-world business challenges by applying your analytical and technical expertise.
Create a strong portfolio of end-to-end Machine Learning and Data Science projects to enhance your career profile.
Our Training Process
A proven path to data science mastery
Enroll
Join the batch
Learn
Theory + Labs
Practice
Hands-on tasks
Project
Caseworks
Career
Placement support
Key Highlights
What makes our program unique
100% Practical
Project-first learning with real datasets.
Mentor Support
Industry mentors for doubt clearing & guidance.
Job Assistance
Resume review, mock interviews & placements.
Certification
Industry-recognized certificate on completion.
Course Content
Comprehensive curriculum designed by industry experts
Module 1 — Python
- Introduction
- String manipulation
- Data structures
- Control loops
- Functions
- Object oriented programming
- Modules and packages
- Graphical user interface (GUI)
- Exception & file handling
- Variables
- Pep8
- Advanced concepts
- API & Project
- Introduction to Data Science
- Introduction to Machine Learning
Module 2 — R Programming
- Fundamentals of R
- Vectors
- Matrices and arrays
- Lists
- Factors
- Data frames
- Programming structures
- Working with strings
- Plotting in base R
- Apply family
- Professional Projects
Module 3 — Machine Learning
- Introduction to Machine Learning (ML)
- Statistics for Data Science
- Data Visualization
- Exploratory Data Analysis
- Data reprocessing
- Linear regression
- Logistic Regression
- Decision trees & random forests
- Model evaluation techniques
- Dimensionality reduction using PCA
- KNN (K–Nearest Neighbours)
- Naive Bayes Classifier
- K-means clustering technique
- Support Vector Machines (svm)
- Time series forecasting
- Ensemble learning
- Stacking
- Optimization
- Capstone Projects
Module 4 —Deep Learning
- Machine Learning workflow.
- Artificial Neural Networks.
- The Activation Function.
- Building an ANN.
- Convolutional Neural Networks.
- Pooling and Flattening.
- Recurrent Neural Networks.
- RNN Intuition.
- The Vanishing Gradient Problem.
- Inferential Statistics.
- Data visualization with Matplotlib.
- Encoding categorical variables.
- Logistic Regression.
- Building an AutoEncoder.
- Data reprocessing.
Module 5 — Generative AI
- Large Language Models (LLMs).
- LLM’s Industry use cases.
- Prompting Techniques.
- One Shot Prompting.
- FewS hot Prompting.
- OpenAI – GPT 3.5 / GPT 4.
- LangChain.
- Hugging Face.
- Google PaLM
- Google Gemini / Gemini Pro / Gemini Pro Vision.
Module 6 — NLP
- Introduction.
- Language analysis.
- Natural Language Understanding.
- Tokenization & stemming.
- POS and NER.
- Scikit-Learn Primer.
- Text Feature Extraction.
- Text Classification.
- Semantics and Sentiment Analysis.
- Topic Modeling
- Keras overview.
- Dialogue Systems.
- Speech Recognition and Text-to-Speech.
- Create ChatBots.
Module 7 — AWS
- Identity & Access Management (IAM)
- Simple Storage Service (S3)
- Networking
- Elastic Compute Cloud (EC2) – I
- Elastic Compute Cloud (EC2) – II
- Database services
- Individual services
- Route53
- Managed application services
- Analytics applications
- Capstone projects
Module 8 — MySQL
- Introduction to MySQL
- Inserting data
- Crud commands
- String functions
- Basic database terminology
- Mysql constraints
- Aggregate functions
- MySQL stored procedure – I
- MySQL stored procedure – II
Module 9 — Tableau
- Tableau basics
- Maps, scatterplots & your first dashboard
- Joining and blending data, plus: dual axis charts
- Table calculations, advanced dashboards, storytelling
- Advanced data preparation
Module 10 — Power BI
- Introduction
- Connecting & shaping data with Power BI desktop
- Creating table relationships & data models in Power BI
- Analyzing data with dax calculations in Power BI
- Visualizing data with Power BI reports
- Artificial Intelligence (AI) visuals
Module 11 — Professional Projects
- Customer Segmentation
- Recommender System
- Build a Chatbot
Skills you will gain
Practical, job-ready skills to boost your career in Data Science.
Course Certification
Complete your Data Science training with MehulTech and earn the Certified Data Scientist credential to boost your career. This official certification is proof of your course completion and a clear indicator of your data science expertise.
Frequently Asked Questions
Everything you need to know about the course
Who should take this course?
Beginners, developers, analysts, and anyone looking to build a career in data science.
What is the mode of training?
Online live sessions, recorded videos, hands-on assignments, and capstone projects.
Do you provide placement assistance?
Yes — resume building, mock interviews, and placement support are included.
How long is the course?
Typically 4–6 months depending on the chosen batch and pace.