Data Science Online Courses and Programs

Data Science

You can learn Data Science and Python from basics to advanced.

What you'll learn

Python
Stats
Machine Learning
Deep Learning
Computer Vision
NLP
Mock interview
Interview preparation
Resume building after every module
Data Bases

Course Curriculum

Python Basics

Python Introduction, Installation and Setup
Python Basics & Conditionals
Conditionals & Loops
Working with Loops
Working with Strings & Lists
List manipulation
Tuple, Set & Dictionary
Working with Functions
Functions, Generators & File Handling
Logging and debugging
Modules and Exception

OOPS

OOPs, Classes & Objects
OOPS, Abstraction & Inheritance
Inheritance, Polymorphism & Intro to Databases

Databases

Working with SQL & Python
SQL Continued, MongoDB installation & Working with MongoDB
Working with Cassandra & Python

Pandas

Introduction to Pandas
Pandas Basics
Pandas Data Manipulation
Working with Pandas

Numpy

Introduction to Numpy

Matplotlib

Working with Pandas & Matplotlib

Plotly

Working with Plotly

Seaborn

Working with Seaborn

Web Frameworks

Rest API, Flask & Working with Postman
Working with Flask & Debugging Calculator Application
Django Introduction & Working with Django

Python Projects with Deployment

Project Discussion Review Scraper with Deployment on Heroku, AWS and Azure
Project Discussion Advance Review Scraper

Stats

Different types of Statistics
Population vs Sample
Mean, Median and Mode
Variance, Standard Deviation
Sample Variance why n-1
Standard Deviation
Variables
Random Variables
Percentiles & quartiles
5 number summary
Histograms
Gaussian - Normal distribution
Standard Normal distribution
Application Of Zscore
Basics Of Probability
Addition Rule In Probability
Multiplication rule in probability
Permutation
Combination
Log Normal Distribution
Central Limit theorem
Statistics - Left Skewed And Right Skewed Distribution And Relation With Mean, Median And Mode
Covariance
Pearson And Spearman Rank Correlation
What is P Value
What is Confidence Intervals
How To Perform Hypothesis Testing - Confidence IntervalZ Test Statistics Derive Conclusion
Hypothesis testing part 2
Hypothesis testing part 3
Finalizing statistics

Machine Learning

Introduction to Machine learning
Linear Regression
Linear Regression live coding demonstration part-1
Linear Regression live coding demonstration part-2
Project Admission Prediction, Lasso, Ridge & Elastic Net
Project deployment in Heroku, Azure & AWS
Logistic Regression
Logistic Regression implementation
Decision Tree
Decision Tree Part 2 , Ensemble Tech, Random Forest & Boosting
KNN and SVM
Decision Tree Practical Implementation
Decision Tree Live Coding & Grid Search
Grid Search, Bagging Classifier & Random Forest
KNN, SVC, SVR & Stacking
Clustering
Clustering and PCA
PCA practical, DBSCAN and Naive Bayes
XG Boost, NLTK & TF-IDF

ML Projects

Detailed Project Report explanation
Project :- Real Estate Price Prediciton Part 1
Project :- Real Estate Price Prediciton Part 2
Deployment in Heroku using docker and circleci

ML Project 1 :- Real Estate Price Prediciton

Introduction
The Problem Statement and Data Description
The Application Flow
Code Intro and Logging
Validation and Transformation
DB Operations
Data Preprocessing
Clustering
Model Selection and Tuning
Prediction
Deployment

ML Project 2 :- Image Classification

Introduction
The Problem Statement and Data Description
The Application Flow
Code Intro and Logging
Validation and Transformation
DB Operations
Data Preprocessing
Clustering
Model Selection and Tuning
Prediction
Deployment

Time Series

Arima, Sarima, Auto Arima
Time series using RNN LSTM, Prediction of NIFTY stock price
Time series using RNN LSTM, Prediction of NIFTY stock price

DL ANN - Introduction

Introduction to Deep Learning
Importance of Deep learning
Why you should study Deep Learning? (Motivation)
ANN vs BNN
The first Artificial Neuron

Computer Vision - Introduction

Introduction to Course
Course Overview
Installing Anaconda, Pycharm & Postman
Working with Conda Envs
Pycharm Introduction
Pycharm with Conda
Pycharm with venv
Pycharm with Pipenv

NLP Overview

NLP Overview
NLP very basic

Interview Preparation - Introduction & Induction

Induction & Course Introduction
Impact of Data Science in today's world & Roles in Data Science

Interview Preparation - Transition Stories

Transition story
Transition story
Transition Story and Resume Discussion

Interview Preparation - AI Projects Discussion

Insurance Fraud Detection
Forest Cover Classification
Project Architecture Discussion
Year Wise Resume Discussion
Project Architecture Discussion
Project Architecture Discussion - 2
Brand Measure Project Discussion
Project Architecture Discussion - 3
Megatron Project Discussion
Python Discussion

Interview Preparation - Python

Python Discussion
Python Discussion

Interview Preparation - Databases

MYSQL and MongoDB Discussion
Interview Question Discussion - 2

Interview Preparation - Interview Questions Discussion

Interview Question Discussion
Resume Discussion
Interview Question Discussion - 3
Interview Question Discussion - 4
Interview Question Discussion - 5

Interview Preparation - Project Discussion

Vision-Based Attendance System
Face Recognition & Mlops Discussion
Mlops Discussion
Brand Measure Project Discussion
NLP Use Cases Discussion

Interview Preparation - Interview Questions Discussions

Interview Question Discussion - 1
Interview Question Discussion - 2
Interview Question Discussion

Interview Preparation - General Discussion

Discussion Session - 1
Discussion Session - 2

Data Science

What you'll learn

Course Curriculum

Course Instructor

Vijaya Mekala

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