Data Data Science with AI Training In Kochi

Phase 1: Python Programming

Python setup, IDEs and Jupyter Notebook

• Variables, data types and type conversion

• Conditional statements and loops

• Data structures: List, Tuple, Dictionary, Set

• List comprehensions (basic, conditional, nested)

• Functions, lambda functions and modules

• Functional programming: map, filter, reduce

• OOP: classes, objects, constructors, inheritance, encapsulation

• File handling (CSV, JSON)

• Exception handling and debugging

• Hands-on coding exercises and dataset handling

Phase 2: Statistics & Applied Statistics

• Descriptive statistics: mean, median, mode

• Variance and standard deviation

• Data distributions (normal, skewness)

• Correlation vs causation

• Regression concepts (intuitive understanding)

• Inferential statistics: population vs sample

• Confidence intervals

• Hypothesis testing (p-value, significance level)

• A/B testing and real-world scenarios

• Python implementation using scipy

Phase 3: SQL / MySQL

• Relational database concepts

• SQL queries: SELECT, WHERE, ORDER BY, LIMIT

• Aggregation: COUNT, SUM, AVG, GROUP BY

• JOINs: INNER, LEFT JOIN

• Connecting MySQL with Python

• Mini project: sales database analysis

Phase 4: Power BI for Business Intelligence

• Power BI ecosystem, installation and interface overview

• Connecting to data sources (Excel, CSV, SQL, MySQL)

• Data cleaning and transformation using Power Query

• Sorting, filtering and shaping data

• Data modeling concepts: normalization and denormalization

• Relationships, cardinality and schema design (Star schema)

• DAX basics: calculated columns, measures and functions

• Advanced DAX: aggregation, filters, time intelligence

• Visualizations: charts, tables, KPIs, maps and dashboards

• Advanced visuals: conditional formatting, hierarchies, filters

• Interactive reports: drill-downs, bookmarks, buttons

• Dashboard design principles and publishing to Power BI Service

• Mini Project: Build and publish an interactive business dashboard

Phase 5: Data Analysis

• NumPy arrays, indexing, operations

• Pandas DataFrames and Series

• Data loading from CSV, Excel, SQL

• Data cleaning and preprocessing

• Handling missing values and duplicates

• Filtering, sorting, groupby, merging

• Exploratory Data Analysis (EDA)

• Real-world dataset analysis

Phase 6: Data Visualization

• Matplotlib: line, bar, histogram

• Seaborn: heatmaps, box plots

• Data storytelling concepts

• Mini project: visualization report

Phase 7: Machine Learning

• ML workflow and concepts

• Train-test split and overfitting

• Linear Regression

• Logistic Regression

• KNN algorithm

• Decision Trees and Random Forest

• Support Vector Machine (SVM)

• Model evaluation: accuracy, precision, recall, F1-score

• K-Means clustering

• Hierarchical clustering

• PCA (dimensionality reduction)

• Mini project: prediction model

Phase 8: AI, Deep Learning, NLP & Computer Vision

• AI overview and applications

• ANN: neurons, layers, backpropagation

• CNN: convolution, pooling, image classification

• RNN & LSTM for sequence data

• Deep learning mini project

• NLP: tokenization, stopwords, stemming, TF-IDF

• NLP mini project: sentiment analysis

• Computer Vision using OpenCV

• Face detection

• Face recognition systems

• Object detection (YOLO overview)

• Computer vision mini projects

Phase 9: Generative AI & LLM Applications

• Introduction to Generative AI and real-world use cases

• Large Language Models (LLMs) overview

• Transformer and Attention (high-level concept)

• Prompt Engineering: zero-shot, few-shot, role prompting

• LLM platforms: Gemini, Hugging Face, Ollama (overview)

• Using APIs for LLM applications

• Embeddings and cosine similarity

• RAG (Retrieval Augmented Generation) concept

• Vector database basics

• LangChain framework introduction

• Image generation using Stable Diffusion (demo)

• Introduction to Agentic AI systems

Phase 10: Capstone Project

• End-to-end project development

• Problem definition and data collection

• SQL data extraction

• Data preprocessing and EDA

• Model building and evaluation

• Final presentation

Data Science: The Engine of Artificial Intelligence

Data Science is the most sought-after skill in the 21st century. It powers everything from Netflix recommendations to self-driving cars. A career in Data Science allows you to solve "unsolvable" problems and drive innovation at scale.

This Data Science course covers a comprehensive curriculum including:

• Advanced Python & Statistics – Master the language and logic of AI.
• Machine Learning – Build predictive models that learn from data.
• Deep Learning & NLP – Explore neural networks and human language processing.
• Data Engineering – Learn how to clean, process, and store large-scale data.
• Model Deployment – Move your projects from development to production.
• Portfolio Building – Create high-impact case studies to impress top-tier recruiters.