DATA SCIENCE COURSE

Significance of Data Science

Data Science and its Life Cycle

About Machine learning, Artificial Intelligence

Data Science Vs Machine learning Vs AI

Real-time applications of Data Science

 

Python – Programming Language

Lesson 1 – Python Fundamentals, Jupyter Notebook IDE

Lesson 2 – Python Basic Data types and Collections

Lesson 3 – Working with Functions, Modules, Packages

Lesson 4 – Object Oriented Programming

Lesson 5 – Working with NumPy

Lesson 6 – Working with Pandas

Lesson 7 – Working with Scikit-learn

Lesson-8 – Data visualization (matplotlib, seaborn)

Lesson-9 – Statistics with Python

 

Statistics

Lesson 1 – Introduction

Lesson 2 – Sample or population data

Lesson 3 – The fundamentals of descriptive statistics

Lesson 4 – Measures of central tendency, asymmetry, and variability

Lesson 5 – Practical example: descriptive statistics

Lesson 6 – Distributions

Lesson 7 – Estimators and estimates

Lesson 8 – Confidence intervals: advanced topics

Lesson 9 – Practical example: Inferential Statistics

Lesson 10 – Hypothesis testing: Introduction

Lesson 11 – Practical examples: hypothesis testing

 

Machine learning

Lesson 1: Introduction to Artificial Intelligence and Machine Learning

Lesson 2: Data Wrangling and Manipulation

Lesson 3: Supervised Learning – Regression

Lesson 4: Feature Engineering

Lesson 5: Supervised Learning – Classification

Lesson 6: Unsupervised learning

Lesson 7: Time Series Modeling

Lesson 8: Ensemble Learning

Lesson 9: Recommender Systems

Lesson 10: NLP

 

 

R – Programming Language

Lesson 1 – R basics, R-studio

Lesson 2 – Data structures

Lesson 3 – R Programming fundamentals

Lesson 4 – Working with Data

Lesson 5 – Strings and Dates

Lesson 6 – Sorting, merging data with R

Lesson 7 – Working with dplyr, reshape2, tidyr packages

Lesson-9 – Data Visualization with R (ggplot2)

Lesson 8 – Statistics with R

 

Deep Learning overview

Lesson 1: introduction to neural networks, ANN and deep learning

Lesson 2: understanding ANN, RNN, CNN, RCNN

Lesson 3: Feed Forward, Backward propagation

Lesson 4: gradient and stochastic gradient in neural networks

Lesson 5: Tensor flow

Lesson 6: Keras

 

 

Additional Things:

• Case studies
• Real time project

total 60 hours    daily 1.5, 45 Days