Aadhrose Infotech

Data Science Mastery Certification Course

Course Descriptions

This career-oriented Data Science course is designed to equip aspiring data professionals with the full spectrum of skills required to succeed in data-driven roles such as Data Scientist, Data Analyst, or Machine Learning Engineer. The curriculum emphasizes real-world applicability, covering everything from foundational programming and statistical analysis to advanced machine learning, deep learning, and deployment.

Students will gain hands-on experience with industry-standard tools and technologies including Python, R, SQL, TensorFlow, Tableau, Docker, and cloud platforms. By the end of the course, learners will have built a portfolio of real-world projects, preparing them to showcase job-ready skills to prospective employers.

Whether you're pivoting into data science or seeking to level up your existing analytics capabilities, this course is structured to provide you with the technical expertise, practical insights, and professional readiness needed in today’s competitive job market.

Requirements for The Course

Educational Background :

Bachelor’s Degree in:

BE / B.Tech (from any branch)
BBA / B.Com / BSc (in any branch)

Master’s Degree in:

M.Tech / MBA / MCA / M.Sc. / M.Com (in any Branch)

Data Science tools :

Programming: Python, R, SQL
Libraries: scikit-learn, pandas, seaborn, matplotlib, keras, tensorflow, nltk, opencv, streamlit
Deep Learning: TensorFlow, PyTorch, Keras, Hugging Face
Big Data: PySpark, Hadoop, Airflow
Deployment: Flask, FastAPI, Docker, Streamlit, AWS/GCP
Data Sources: Kaggle, UCI, WHO, World Bank, GitHub
Versioning & Reproducibility: Git, DVC, MLflow

Python Programming

Write a Python program to demonstrate variables and data types

Use conditional statements (if, elif, else)

Use loops (for, while) with examples

Define and call functions

Use lists, dictionaries, sets, and tuples

Data Handling with NumPy and Pandas

Create and manipulate NumPy arrays

Perform vectorized operations using NumPy

Load and explore data using Pandas DataFrames

Handle missing values and duplicates

Grouping, filtering, and sorting data

Data Preprocessing and Cleaning

Handle null values using fill, drop techniques

Encode categorical variables (Label Encoding / One-hot Encoding)

Normalize and standardize data

Feature selection and extraction basics

Split dataset into train-test sets

Data Visualization

Plot graphs using Matplotlib (line, bar, histogram)

Use Seaborn for statistical plots (boxplot, pairplot, heatmap)

Create correlation matrix and heatmaps

Create pie charts and scatter plots

Dashboard basics using Streamlit (optional for visual app)

Machine Learning

Build a Linear Regression model (predict marks or price)

Build a Logistic Regression model (predict diabetes or pass/fail)

Apply K-Nearest Neighbors (KNN) classifier

Build a Decision Tree and Random Forest model

Evaluate models using accuracy, confusion matrix, precision, recall

Basics of AI Applications

Understand difference: AI vs ML vs DL

Create a rule-based Chatbot using Python

Perform Sentiment Analysis on text data (using TextBlob)

Use OpenCV to detect faces in an image

Use a pre-trained model (e.g., MobileNet or ResNet) to classify an image

Mini Project

House Price Prediction (Regression)

Student Performance Prediction

Fake News Detection

Movie Recommendation System

Face Mask Detection using AI

Weather Forecast using past data

Advanced Data Preprocessing

Outlier detection and treatment (Z-score, IQR)

Feature Engineering: binning, interaction features

Imbalanced data handling (SMOTE, undersampling, oversampling)

Dimensionality Reduction (PCA, LDA)

Time series data preprocessing (lag features, rolling stats)

Exploratory Data Analysis (EDA) Projects

EDA on COVID-19 dataset (cases, deaths, recovery trends)

EDA on E-commerce dataset (customer behavior, sales)

EDA on Finance dataset (stock prices, profit/loss analysis)

EDA on Biology/Health dataset (heart disease or cancer data)

EDA Report generation using Seaborn, Pandas Profiling

Intermediate Machine Learning Algorithms

Support Vector Machine (SVM) – Classification

Naive Bayes – Email Spam Detection

Gradient Boosting (XGBoost or LightGBM)

k-Means Clustering and DBSCAN – Unsupervised Learning

Model Tuning – Grid Search, Cross Validation

Natural Language Processing (NLP) Basics

Text Cleaning (stopwords, stemming, lemmatization)

Bag-of-Words & TF-IDF Vectorization

Text Classification (e.g., sentiment analysis, spam detection)

Named Entity Recognition using spaCy

Building a Resume Parser or News Classifier

Introduction to Deep Learning

Build a basic Neural Network using Keras/TensorFlow

Image Classification using CNN (MNIST or CIFAR-10)

Text Classification using RNN/LSTM

Transfer Learning (using pre-trained models like VGG, ResNet)

Introduction to Autoencoders

AI Mini Projects & Applications

AI-based Chatbot using NLP + ML

Real-time Face Detection using OpenCV

Emotion Detection using Deep Learning

Credit Card Fraud Detection using ML

Crop Recommendation System (for agriculture-based use)

Deployment and Visualization

Deploy ML model using Flask or Streamlit

Create interactive dashboards using Power BI or Tableau

Model explainability using SHAP or LIME

Version control and collaboration using GitHub

Advanced Machine Learning Techniques

Ensemble Learning (Stacking, Bagging, Boosting)

Advanced Gradient Boosting: CatBoost, LightGBM, XGBoost

Feature Selection using Mutual Info, Boruta, Recursive Feature Elimination (RFE)

Hyperparameter Tuning (RandomizedSearchCV, Bayesian Optimization)

Model Explainability (SHAP, LIME, ELI5)

Deep Learning Advanced Topics

Custom Neural Network (CNN) Architectures using Keras/TensorFlow

Transfer Learning with VGG, Inception, ResNet on medical/real-world datasets

Fine-tuning BERT or RoBERTa for NLP tasks (Hugging Face Transformers)

Attention Mechanisms, Transformers Basics

Generative Models: Variational Autoencoders (VAE), GANs (DeepFake creation basics)

Advanced NLP Projects

BERT-based Sentiment Analysis / Text Classification

Text Summarization and Translation (seq2seq with attention)

ChatGPT-style Conversational Bot using Transformers

Question Answering System using SQuAD dataset

Multi-lingual NLP using IndicNLP / mBERT (for Indian languages)

Computer Vision Advanced Topics

Object Detection using YOLOv5 or SSD

Image Segmentation using U-Net or Mask R-CNN

OCR (Optical Character Recognition) with Tesseract and Deep Learning

Image Captioning using CNN + LSTM

Real-time Face Recognition and Emotion Detection using Deep Learning

Time Series Analysis & Forecasting

Advanced Forecasting with ARIMA, SARIMA, Prophet

LSTM/RNN models for sequence prediction (e.g., stock, temperature)

Multivariate Time Series Forecasting

Anomaly Detection in Time Series

Real-Time Data Pipeline with streaming datasets

Big Data and Cloud Integration

Introduction to Big Data (Hadoop, Spark with PySpark)

Data pipelines using Apache Airflow

Store and retrieve data using AWS S3, Google Cloud Storage

Deploy ML models using AWS SageMaker or Google AI Platform

Scalable model training using TensorFlow on cloud GPUs/TPUs

AI Ethics, Explain ability & Responsible AI

Bias Detection and Mitigation in AI Models

Explainable AI (XAI) in Healthcare / Finance

Fairness and Transparency Analysis using SHAP, LIME

Security in AI (Adversarial Attacks & Defenses)

Privacy-preserving AI (Federated Learning Basics)

Industry Projects & Capstone

AI in Healthcare: Disease Prediction from Medical Images/Text

AI in Finance: Risk Assessment, Fraud Detection

AI in Agriculture: Crop Yield Prediction, Pest Detection

AI in E-Commerce: Recommendation Systems, Customer Lifetime Value

AI in Education: Predicting Student Dropout or Performance

AI for Social Good: Disaster Detection, Air Quality Monitoring, etc.

Soft Skills and Professional Development

Resume Building and Interview Preparation:

Mock Interviews
Behavioral Interview Tips

Course includes:

Level:
Expert
Lessons:
33
Mode:
Online / Offline
Certifications:
Yes
Language:
English & Tamil

Online Fees: ₹‎ 60K

Offline Fees: ₹‎ 80K

Join This course

K. M. IQBAL

Lead Data Scientist | AI Educator

K. M. IQBAL is a senior data scientist with expertise in machine learning, AI, and analytics. She has led projects across finance, healthcare, and retail, and specializes in teaching real-world data skills with a focus on job readiness.

Experience: 10 Years

188 Students

4 (36 Review)

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Data Science Mastery Certification Course

Data Science Mastery Certification Course

Course Descriptions

Requirements for The Course

Python Programming

Data Handling with NumPy and Pandas

Data Preprocessing and Cleaning

Data Visualization

Machine Learning

Basics of AI Applications

Mini Project

Advanced Data Preprocessing

Exploratory Data Analysis (EDA) Projects

Intermediate Machine Learning Algorithms

Natural Language Processing (NLP) Basics

Introduction to Deep Learning

AI Mini Projects & Applications

Deployment and Visualization

Advanced Machine Learning Techniques

Deep Learning Advanced Topics

Advanced NLP Projects

Computer Vision Advanced Topics

Time Series Analysis & Forecasting

Big Data and Cloud Integration

AI Ethics, Explain ability & Responsible AI

Industry Projects & Capstone

Soft Skills and Professional Development

Course includes:

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K. M. IQBAL

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