Data Science

Are you new to PHP or need a refresher? Then this course will help you get all…

21
90 Days
8
(5.00)

– Learn: Understand data analysis, statistics, and machine learning basics.

– Enhance: Dive into predictive modeling, AI, and big data technologies.

– Apply: Analyze real datasets and create data-driven solutions.

– Develop: Transition into roles like Data Scientist, Business Analyst, or AI Specialist.

Course info –

Data science is a multidisciplinary field that involves extracting insights, knowledge, and meaningful information from structured and unstructured data. It combines techniques from mathematics, statistics, computer science, and domain expertise to analyze and interpret data, often using advanced tools like machine learning and artificial intelligence.

What will you learn?

A data scientist works on tasks such as:

Cleaning and preparing raw data for analysis.
Discovering patterns, trends, and correlations in data.
Building predictive models to forecast outcomes.
Visualizing data to communicate findings effectively.
Solving real-world problems by leveraging data-driven approaches.

It’s a field that has applications in almost every industry—from healthcare and finance to entertainment and technology. Curious about how data science is used in a specific area?

Duration – 90 days

New Batch – Every 1^st & 3^rd Monday

Course Curriculum

The First Steps

List Creation & Operations
List Comprehensions, Slicing & Indexing
Tuple Creations, Operations & Slicing & Indexing
Practical Exercise: Implement List & Tuple to solve concise real-life problems.

Data Types and More

Dictionary creation, basic operations, manipulation & comprehensions
Set Creation and Manipulations Common Operations on Dictionary Sets and z
Practical Exercise: Work with dictionaries and sets to solve real- life problems.

NUMPV FUNDAMENTALS

Work with NumPy Arrays
Efficient Indexing and Slicing
Filtering and Boolean Indexing
Practical Exercise: Work with NumPy arrays to perform basic array operations, indexing, and ltering.

ADVANCED SQL CONCEPT & DATA MANIPULATION

Temporary Tables and Documentation
Aggregations and Grouping Data
Advanced SQL Operations and Joins
Practical Exercise: Perform more complex SQL operations, such as joining and aggregating data

FUNDAMENTALS OF STATISTICS & PROBABILITY

Understand Data Types
Central Tendency and Variance
Probability and Distribution Basics
Practical Exercise: Calculate mean, median, variance, and standard deviation for a dataset

ADVANCED STATISTICS & HYPOTHESIS TESTING

Hypothesis Testing Techniques Interpret Data Visualizations
Correlation, Regression, and ANOVA
Practical Exercise: Perform hypothesis tests and analyze real datasets using statistical techniques.

INTRODUCTION TO MACHINE LEARNING AND REGRESSION BASICS

Dive into Machine Learning
Data Preprocessing Essentials
Linear Regression for Predictive Modeling
Practical Exercise: Implement a simple linear regression model and evaluate its performance

MULTIPLE LINEAR REGRESSION & MODEL EVALUATION

Evaluate Models with MAE, MSE, RMSE
Multiple Linear Regression
Practical Model Evaluation with Real Data
Practical Exercise: Build and evaluate a multiple linear regression model using a real-world dataset.

LOGISTIC REGRESSION AND CLASSIFICATION METRICS

Master Logistic Regression
Classification Metrics for Model Assessment
Practical Exercise: Train and evaluate logistic regression models for binary and multiclass classification problems

SUPPORT VECTOR MACHINES (SVM) AND K-NEAREST NEIGHBORS (KNN)

Classification with SVM
K-Nearest Neighbors for Predictions
Choose Kand Distances
Practical Exercise: Bud and evaluate SVM and KNN models for classification problems

DECISION TREES AND ENSEMBLE METHODS

Understand Decision Trees
Prevent Overtting and Tree Pruning
Explore Random Forest and Gradient Boosting
Practical Exercise: Create decision tree models and explore the power of ensemble methods

MODEL EVALUATION AND VALIDATION TECHNIQUES

K-Fold Cross-Validation
Hyperparameter Tuning
In-Depth Classification Metrics
Practical Exercise: Apply K-fold cross-validation and hyperparameter tuning to improve model performance

DJANGO / FLASK

Building Large-scale, data-driven applications
Develop Enterprise-level web applications
Create Projects with a built-in admin interface and form handling
Provides Rapid prototyping with built-in tools and a well-defined structure

RECOMMENDATION ENGINES

Understand the core principles and algorithms behind recommendation systems
Build and implement content-based recommendation systems using Python
Design and deploy hybrid systems that combine multiple recommendation techniques for enhanced performance.
Evaluate recommendation models using industry-standard metrics and optimization Techniques

UNSUPERVISED LEARNING

Discover K-Means Clustering
Hierarchical Clustering Techniques
Clustering for Data Insights
Practical Exercise Implement Means clustering and hierarchical clustering on real data

TIME SERIES MODELLING WITH ARIMA AND SARIMA

Understand Time Series Data
Build ARIMA and SARMA Models
Practical Forecasting and Model Evaluation
Practical Exercise: Analyze and forecast time series data using ARIMA and SARMA models

INTRODUCTION TO DEEP LEARNING

Overview of Aritical Neural Networks
Build and Train Simple Neural Networks
Basic Deep Learning Concept
Practical Exercise Build and train simple neural network on a dataset using popular deep learning frameworks

DEEP LEARNING ARCHITECTURES AND TRAINING

Dive into CNNs and RNN
Train Deep Learning Models
Avoid Overtting with Regularization

OPEN CV (COMPUTER VISION)

Understand the fundamental principles of computer vision
Master basic and advanced image processing techniques, including geometric transformations, image filtering, and edge detection.
Detect and track objects in images and videos using classical methods
Create real-time applications like face recognition, motion tracking, and augmented reality.

NATURAL LANGUAGE PROCESSING (NLP)

Introduction to NLP, Challenges & Key Tasks
Pre-process Text Data
Create Text Classification Models
Practical Exercise: Perform text preprocessing and build a text classification model using NLP techniques.

DATA SCIENCE PROJECT-1

Understanding end-to-end Data Science
Model Development, Interpretation & business recommendation
Introduction to Deployment
Practical Exercise: Deploy a machine learning model as a web API and monitor its performance

DATA SCIENCE PROJECT-2

Understanding Deep Learning & NLP Business Problem
Model Evolution and Deployment
Practical Exercise: Deploy a machine Deep Learning & NLP as a web API and monitor its performance

POWER Bl

Introduction to Power Bl
Data Transformation and Modeling
Create Interactive Dashboards
Practical Exercise: Transform data and create interactive dashboards in Power Bl using real-world datasets.

TABLEAU

Explore Tableau Prep and Desktop
Visual Analytics and Calculations
Design Engaging Dashboards
Practical Exercise: Develop visualizations and dashboards
Tableau based on provided data.

INTRODUCTION TO GENERATIVE Al, TRANSFORMERS & LLMS

Generative Al, Models & their features
Transformers & their Architectures
Exploring LLMs and Hugging face Library
Practical Exercise: Text Generation/ Summarization using pretrained LLMs.

TRAINING AND FINE-TUNING LLMS

Fine-tuning LLMs for Specie Tasks
Evaluating models using metrics like BLEU and ROUGE
Retrieve, Augment, Generate (RAC) for ne-tuning
Practical Exercise: Implementing ne- tuning code with Hugging Face libraries using your own custom data.

ADVANCED FINE-TUNING AND MODEL EVALUATION

Advanced Fine-tuning Techniques
Prompt engineering and its impact on generated text
Text-to-Speech and Speech-to-Text Integration with Hugging Face
Practical Exercise: Fine-tuning and evaluation with Advanced Techniques and Text-toÂ Speech/ Speech-to-Text

BUILDING A REAL-LIFE CHATBOT

Designing and Developing a Chatbot
Integrating ne-tuned LLM models for text generation, dialogue, and text- to-speech/ speech-to-text
Building the chatbot interface and user interaction ow
Practical Exercise: Deploying customized chatbot using Gradio.

Student Ratings & Reviews

5.0

Total 2 Ratings

6 years ago

This was my first time taking an online course.
The content is excellent, and the instructors are also excellent.
How much you learn from this course is pretty much what you put into it.

6 years ago

Thank you! I enjoyed your course and humor.
Please keep working on making great stuff to share and help more people.
Look forward to more additional features to this project near future.

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Data Science

Course Curriculum

The First Steps

List Creation & Operations

List Comprehensions, Slicing & Indexing

Tuple Creations, Operations & Slicing & Indexing

Practical Exercise: Implement List & Tuple to solve concise real-life problems.

Data Types and More

Dictionary creation, basic operations, manipulation & comprehensions

Set Creation and Manipulations Common Operations on Dictionary Sets and z

Practical Exercise: Work with dictionaries and sets to solve real- life problems.

NUMPV FUNDAMENTALS

Work with NumPy Arrays

Efficient Indexing and Slicing

Filtering and Boolean Indexing

Practical Exercise: Work with NumPy arrays to perform basic array operations, indexing, and ltering.

ADVANCED SQL CONCEPT & DATA MANIPULATION

Temporary Tables and Documentation

Aggregations and Grouping Data

Advanced SQL Operations and Joins

Practical Exercise: Perform more complex SQL operations, such as joining and aggregating data

FUNDAMENTALS OF STATISTICS & PROBABILITY

Understand Data Types

Central Tendency and Variance

Probability and Distribution Basics

Practical Exercise: Calculate mean, median, variance, and standard deviation for a dataset

ADVANCED STATISTICS & HYPOTHESIS TESTING

Hypothesis Testing Techniques Interpret Data Visualizations

Correlation, Regression, and ANOVA

Practical Exercise: Perform hypothesis tests and analyze real datasets using statistical techniques.

INTRODUCTION TO MACHINE LEARNING AND REGRESSION BASICS

Dive into Machine Learning

Data Preprocessing Essentials

Linear Regression for Predictive Modeling

Practical Exercise: Implement a simple linear regression model and evaluate its performance

MULTIPLE LINEAR REGRESSION & MODEL EVALUATION

Evaluate Models with MAE, MSE, RMSE

Multiple Linear Regression

Practical Model Evaluation with Real Data

Practical Exercise: Build and evaluate a multiple linear regression model using a real-world dataset.

LOGISTIC REGRESSION AND CLASSIFICATION METRICS

Master Logistic Regression

Classification Metrics for Model Assessment

Practical Exercise: Train and evaluate logistic regression models for binary and multiclass classification problems

SUPPORT VECTOR MACHINES (SVM) AND K-NEAREST NEIGHBORS (KNN)

Classification with SVM

K-Nearest Neighbors for Predictions

Choose Kand Distances

Practical Exercise: Bud and evaluate SVM and KNN models for classification problems

DECISION TREES AND ENSEMBLE METHODS

Understand Decision Trees

Prevent Overtting and Tree Pruning

Explore Random Forest and Gradient Boosting

Practical Exercise: Create decision tree models and explore the power of ensemble methods

MODEL EVALUATION AND VALIDATION TECHNIQUES

K-Fold Cross-Validation

Hyperparameter Tuning

In-Depth Classification Metrics

Practical Exercise: Apply K-fold cross-validation and hyperparameter tuning to improve model performance

DJANGO / FLASK

Building Large-scale, data-driven applications

Develop Enterprise-level web applications

Create Projects with a built-in admin interface and form handling

Provides Rapid prototyping with built-in tools and a well-defined structure

RECOMMENDATION ENGINES

Understand the core principles and algorithms behind recommendation systems

Build and implement content-based recommendation systems using Python

Design and deploy hybrid systems that combine multiple recommendation techniques for enhanced performance.

Evaluate recommendation models using industry-standard metrics and optimization Techniques

UNSUPERVISED LEARNING

Discover K-Means Clustering

Hierarchical Clustering Techniques

Clustering for Data Insights

Practical Exercise Implement Means clustering and hierarchical clustering on real data

TIME SERIES MODELLING WITH ARIMA AND SARIMA

Understand Time Series Data

Build ARIMA and SARMA Models

Practical Forecasting and Model Evaluation

Practical Exercise: Analyze and forecast time series data using ARIMA and SARMA models

INTRODUCTION TO DEEP LEARNING

Practical Exercise: Fine-tuning and evaluation with Advanced Techniques and Text-toÂ Speech/ Speech-to-Text