Data Science Course in Marathahalli

DATA SCIENCE - SYLLABUS

Introduction to Course

Environment Setup

Machine Learning overview

 

Python Crash Course

➢ OPTIONAL: Python Crash Course

➢ Python Crash Course - Part One

➢ Python Crash Course - Part Two

➢ Python Crash Course - Part Three

➢ Python Crash Course - Exercise Questions

➢ Python Crash Course - Exercise Solutions

 

NumPy

➢ Introduction to NumPy

➢ NumPy Arrays

➢ Coding Exercise Check-in: Creating NumPy Arrays

➢ NumPy Indexing and Selection

➢ Coding Exercise Check-in: Selecting Data from Numpy Array

➢ NumPy Operations

➢ Check-In: Operations on NumPy Array

➢ NumPy Exercises

➢ Numpy Exercises – Solutions

 

Pandas

➢ Introduction to Pandas

➢ Series - Part One

➢ Check-in: Labeled Index in Pandas Series

➢ Series - Part Two

➢ DataFrames - Part One - Creating a DataFrame

➢ DataFrames - Part Two - Basic Properties

➢ DataFrames - Part Three - Working with Columns

➢ DataFrames - Part Four - Working with Rows

➢ Pandas - Conditional Filtering

➢ Pandas - Useful Methods - Apply on Single Column

➢ Pandas - Useful Methods - Apply on Multiple Columns

➢ Pandas - Useful Methods - Statistical Information and Sorting

➢ Missing Data - Overview

➢ Missing Data - Pandas Operations

➢ GroupBy Operations - Part One

➢ GroupBy Operations - Part Two - MultiIndex

➢ Combining DataFrames - Concatenation

➢ Combining DataFrames - Inner Merge

➢ Combining DataFrames - Left and Right Merge

➢ Combining DataFrames - Outer Merge

➢ Pandas - Text Methods for String Data

➢ Pandas - Time Methods for Date and Time Data

➢ Pandas Input and Output - CSV Files

➢ Pandas Input and Output - HTML Tables

➢ Pandas Input and Output - Excel Files

➢ Pandas Input and Output - SQL Databases

➢ Pandas Pivot Tables

➢ Pandas Project Exercise Overview

➢ Pandas Project Exercise Solutions

 

Matplotlib

➢ Introduction to Matplotlib

➢ Matplotlib Basics

➢ Matplotlib - Understanding the Figure Object

➢ Matplotlib - Implementing Figures and Axes

➢ Matplotlib - Figure Parameters

➢ Matplotlib-Subplots Functionality

➢ Matplotlib Styling - Legends

➢ Matplotlib Styling - Colors and Styles

➢ Advanced Matplotlib Commands (Optional)

➢ Matplotlib Exercise Questions Overview

➢ Matplotlib Exercise Questions – Solutions

 

Seaborn Data Visualizations

➢ Introduction to Seaborn

➢ Scatterplots with Seaborn

➢ Distribution Plots - Part One - Understanding Plot Types

➢ Distribution Plots - Part Two - Coding with Seaborn

➢ Categorical Plots - Statistics within Categories - Understanding Plot Types

➢ Categorical Plots - Statistics within Categories - Coding with Seaborn

➢ Categorical Plots - Distributions within Categories - Understanding Plot Types

➢ Categorical Plots - Distributions within Categories - Coding with Seaborn

➢ Seaborn - Comparison Plots - Understanding the Plot Types

➢ Seaborn - Comparison Plots - Coding with Seaborn

➢ Seaborn Grid Plots

➢ Seaborn - Matrix Plots

➢ Seaborn Plot Exercises Overview

➢ Seaborn Plot Exercises Solutions

 

Data Analysis and Visualization Capstone Project Exercise

➢ Capstone Project overview

➢ Capstone Project Solutions - Part One

➢ Capstone Project Solutions - Part Two

➢ Capstone Project Solutions - Part Three

 

Machine Learning Concepts Overview

➢ Introduction to Machine Learning Overview Section

➢ Why Machine Learning?

➢ Types of Machine Learning Algorithms

➢ Supervised Machine Learning Process

➢ Companion Book - Introduction to Statistical Learning

 

Linear Regression

➢ Introduction to Linear Regression Section

➢ Linear Regression -Algorithm History

➢ Linear Regression - Understanding Ordinary Least Squares

➢ Linear Regression - Cost Functions

➢ Linear Regression - Gradient Descent

➢ Python coding Simple Linear Regression

➢ Overview of Scikit-Learn and Python

➢ Linear Regression - Scikit-Learn Train Test Split

➢ Linear Regression - Scikit-Learn Performance Evaluation - Regression

➢ Linear Regression - Residual Plots

➢ Linear Regression - Model Deployment and Coefficient Interpretation

➢ Polynomial Regression - Theory and Motivation

➢ Polynomial Regression - Creating Polynomial Features

➢ Polynomial Regression - Training and Evaluation

➢ Bias Variance Trade-Off

➢ Polynomial Regression - Choosing Degree of Polynomial

➢ Polynomial Regression - Model Deployment

➢ Regularization Overview

➢ Feature Scaling

➢ Introduction to Cross Validation

➢ Regularization Data Setup

➢ L2 Regularization -Ridge Regression - Theory

➢ L2 Regularization - Ridge Regression - Python Implementation

➢ L1 Regularization - Lasso Regression - Background and Implementation

➢ L1 and L2 Regularization - Elastic Net

➢ Linear Regression Project - Data Overview

 

Feature Engineering and Data Preparation

➢ A note from Jose on Feature Engineering and Data Preparation

➢ Introduction to Feature Engineering and Data Preparation

➢ Dealing with Outliers

➢ Dealing with Missing Data : Part One - Evaluation of Missing Data

➢ Dealing with Missing Data : Part Two - Filling or Dropping data based on Rows

➢ Dealing with Missing Data : Part 3 - Fixing data based on Columns

➢ Dealing with Categorical Data - Encoding Options

 

Cross Validation, Grid Search, and the Linear Regression Project

➢ Section Overview and Introduction

➢ Cross Validation - Test | Train Split

➢ Cross Validation - Test | Validation | Train Split

➢ Cross Validation - cross_val_score

➢ Cross Validation - cross_validate

➢ Grid Search

➢ Linear Regression Project Overview

➢ Linear Regression Project – Solutions

 

Logistic Regression

➢ Early Bird Note on Downloading .zip for Logistic Regression Notes

➢ Introduction to Logistic Regression Section

➢ Logistic Regression - Theory and Intuition - Part One: The Logistic Function

➢ Logistic Regression - Theory and Intuition - Part Two: Linear to Logistic

➢ Logistic Regression - Theory and Intuition - Linear to Logistic Math

➢ Logistic Regression - Theory and Intuition - Best fit with Maximum Likelihood

➢ Logistic Regression with Scikit-Learn - Part One - EDA

➢ Logistic Regression with Scikit-Learn - Part Two - Model Training

➢ Classification Metrics - Confusion Matrix and Accuracy

➢ Classification Metrics - Precison, Recall, F1-Score

➢ Classification Metrics - ROC Curves

➢ Logistic Regression with Scikit-Learn - Part Three - Performance Evaluation

➢ Multi-Class Classification with Logistic Regression - Part One - Data and EDA

➢ Multi-Class Classification with Logistic Regression - Part Two - Model

➢ Logistic Regression Exercise Project Overview

➢ Logistic Regression Project Exercise – Solutions

 

KNN-K Nearest Neighbors

➢ Introduction to KNN Section

➢ KNN Classification - Theory and Intuition

➢ KNN Coding with Python - Part One

➢ KNN Coding with Python - Part Two - Choosing K

➢ KNN Classification Project Exercise Overview

➢ KNN Classification Project Exercise Solutions

 

Support Vector Machines

➢ Introduction to Support Vector Machines

➢ History of Support Vector Machines

➢ SVM - Theory and Intuition - Hyperplanes and Margins

➢ SVM - Theory and Intuition - Kernel Intuition

➢ SVM - Theory and Intuition - Kernel Trick and Mathematics

➢ SVM with Scikit-Learn and Python - Classification Part One

➢ SVM with Scikit-Learn and Python - Classification Part Two

➢ SVM with Scikit-Learn and Python - Regression Tasks

➢ Support Vector Machine Project Overview

➢ Support Vector Machine Project Solutions

 

Tree Based Methods: Decision Tree Learning

➢ Introduction to Tree Based Methods

➢ Decision Tree - History

➢ Decision Tree - Terminology

➢ Decision Tree - Understanding Gini Impurity

➢ Constructing Decision Trees with Gini Impurity - Part One

➢ Constructing Decision Trees with Gini Impurity - Part Two

➢ Coding Decision Trees - Part One - The Data

➢ Coding Decision Trees - Part Two -Creating the Model

 

Random Forests

➢ Introduction to Random Forests Section

➢ Random Forests - History and Motivation

➢ Random Forests - Key Hyperparameters

➢ Random Forests - Number of Estimators and Features in Subsets

➢ Random Forests - Bootstrapping and Out-of-Bag Error

➢ Coding Classification with Random Forest Classifier - Part One

➢ Coding Classification with Random Forest Classifier - Part Two

➢ Coding Regression with Random Forest Regressor - Part One - Data

➢ Coding Regression with Random Forest Regressor - Part Two - Basic Models

➢ Coding Regression with Random Forest Regressor - Part Three - Polynomials

➢ Coding Regression with Random Forest Regressor - Part Four - Advanced Models

 

Boosting Methods

➢ Introduction to Boosting Section

➢ Boosting Methods - Motivation and History

➢ AdaBoost Theory and Intuition

➢ AdaBoost Coding Part One - The Data

➢ AdaBoost Coding Part Two - The Model

➢ Gradient Boosting Theory

➢ Gradient Boosting Coding Walkthrough

 

Supervised Learning Capstone Project-Cohort Analysis and Tree Based Methods

➢ Introduction to Supervised Learning Capstone Project

➢ Solution Walkthrough - Supervised Learning Project - Data and EDA

➢ Solution Walkthrough - Supervised Learning Project - Cohort Analysis

➢ Solution Walkthrough - Supervised Learning Project - Tree Models

 

Naive Bayes Classification and Natural Language Processing (Supervised Learning)

➢ Introduction to NLP and Naive Bayes Section

➢ Naive Bayes Algorithm - Part One - Bayes Theorem

➢ Naive Bayes Algorithm - Part Two - Model Algorithm

➢ Feature Extraction from Text - Part One - Theory and Intuition

➢ Feature Extraction from Text - Coding Count Vectorization Manually

➢ Feature Extraction from Text - Coding with Scikit-Learn

➢ Natural Language Processing - Classification of Text - Part One

➢ Natural Language Processing - Classification of Text - Part Two

➢ Text Classification Project Exercise Overview

➢ Text Classification Project Exercise Solutions

 

Unsupervised Learning

➢ Unsupervised Learning Overview

 

K-Means Clustering

➢ Introduction to K-Means Clustering Section

➢ Clustering General Overview

➢ K-Means Clustering Theory

➢ K-Means Clustering - Coding Part One

➢ K-Means Clustering Coding Part Two

➢ K-Means Clustering Coding Part Three

➢ K-Means Color Quantization - Part One

➢ K-Means Color Quantization - Part Two

➢ K-Means Clustering Exercise Overview

➢ K-Means Clustering Exercise Solution - Part One

➢ K-Means Clustering Exercise Solution - Part Two

➢ K-Means Clustering Exercise Solution - Part Three

 

Hierarchical Clustering

➢ Introduction to Hierarchical Clustering

➢ Hierarchical Clustering - Theory and Intuition

➢ Hierarchical Clustering - Coding Part One - Data and Visualization

➢ Hierarchical Clustering - Coding Part Two - Scikit-Learn

 

DBSCAN-Density-based spatial clustering of applications with noise

➢ Introduction to DBSCAN Section

➢ DBSCAN - Theory and Intuition

➢ DBSCAN versus K-Means Clustering

➢ DBSCAN - Hyperparameter Theory

➢ DBSCAN - Hyperparameter Tuning Methods

➢ DBSCAN - Outlier Project Exercise Overview

➢ DBSCAN - Outlier Project Exercise Solutions

 

Principal Component Analysis and Manifold Learning

➢ Introduction to Principal Component Analysis

➢ PCA Theory and Intuition - Part One

➢ PCA Theory and Intuition - Part Two

➢ PCA - Manual Implementation in Python

➢ PCA - SciKit-Learn

➢ PCA - Project Exercise Overview

➢ PCA - Project Exercise Solution

 

Model Deployment

➢ Model Deployment Section Overview

➢ Model Deployment Considerations

➢ Model Persistence

➢ Model Deployment as an API - General Overview

➢ Note on Upcoming Video

➢ Model API - Creating the Script

➢ Testing the API

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Data Science Course in Marathahalli

The data science course in Marathahalli serves as a transformative step for aspiring data science professionals, blending mathematical concepts, statistical methodologies, and business insights. This powerful combination empowers individuals to extract meaningful insights from raw data patterns, enabling critical decision-making for businesses. Organizations today amass vast volumes of unstructured data, necessitating a process to separate critical information from irrelevant data. Data science facilitates this process through advanced structuring techniques, making it an indispensable field. The role of a data scientist has emerged as one of the most sought-after careers in the current job market. Numerous training centers in Marathahalli offer specialized programs in data science, equipping learners with in-depth knowledge and practical skills. These programs are designed to ensure that students gain expertise from seasoned professionals who guide them every step of the way. Participants develop a comprehensive understanding of data structuring, data manipulation, and other essential areas. Hands-on projects and assignments further solidify the foundational concepts of data science. The data science course can be pursued as a stand-alone program or as part of various specializations, including machine learning, artificial intelligence, data analytics, and Python for data science. Aspiring candidates should ideally have a basic grasp of mathematics and statistics before enrolling. These courses are accessible both online and offline, with institutes offering flexible batch timings to accommodate diverse schedules. By enrolling in a data science program in Marathahalli, candidates gain advanced skills to tackle complex data challenges, making them invaluable assets to organizations worldwide.

Scope of Data Science Course in Marathahalli

Data science is rapidly emerging as one of the most dynamic and fast-evolving fields today, with its potential and relevance expanding steadily. Businesses are increasingly relying on data-driven strategies, which has fueled a growing demand for professionals skilled in data science. Organizations across India actively seek individuals with deep expertise in this domain to help them analyze and interpret the vast quantities of data they accumulate.Leading companies in India leverage data science to gain a strategic edge in the marketplace, creating a strong demand for competent professionals. These organizations not only value such talent but also offer attractive salary packages to qualified candidates. A career in data science presents incredible opportunities for growth and holds immense promise for the future, making it an excellent choice for those aiming to thrive in a cutting-edge industry.

Placement Process of the Data Science Training institutions

The data science course in Marathahalli is provided by well-established institutions, renowned for their experienced trainers who have a proven track record in the field. Enrolled students receive comprehensive training that spans from fundamental concepts to advanced techniques, ensuring a thorough understanding of data science. To solidify their learning, participants engage in hands-on projects and assignments that simulate real-world scenarios. Upon completing the intensive training, candidates are guided through the process of crafting professional resumes. Once their resumes are ready, trainers focus on equipping them for interviews by offering extensive support. Mock interview sessions are conducted to simulate the actual interview experience, ensuring students are fully prepared and confident. The trainers leave no stone unturned in assisting candidates, aiming for successful job placements for everyone.

Conclusion

The institutes not only assist students in securing employment but also award certifications upon course completion. These credentials enhance the candidates' resumes, increasing their chances of landing roles at their preferred companies.