\$700.00

Category:

## Description

This Data Scientist course aims to accelerate your career in Data Science and provides you with world-class training and skills required to become successful in this field. The Data Scientist course offers extensive training on the most in-demand Data Science and Machine Learning skills with hands-on exposure to key tools and technologies including Python, Web Scraping, NLP, Data Visualization, Statistics, and concepts of Machine Learning and Deep Learning.

### Skills you will learn:

Python Programming, Statistics for Machine Learning, understanding of data structure and data manipulation, machine learning model building, deep learning

#### Introduction

Introduction to the course

Introduction to Data Science

Introduction to Analytics

Design Thinking and Problem Statement

Mini Project 1

Project 1

#### Master Python Programming

Python Basics

Python Variables: int, float, string, bool, complex

Conditional statements

Loops

Python Collections: List, Tuple, Dictionary, Set, Frozenset

Mini Project 3

Functions and Methods

Class & Objects

Mini Project 4

Numpy

Working with CSV and Text files

Error Handling

Regular Expression

Project 2: Using Python concepts

#### Descriptive Statistics

Data and Types

Central Tendency: Mean, Median, Mode

Deviation: Range, Variance, Standard Deviation

BoxPlot and its importance

Mini Project 5

Frequency distribution and its importance

Mini Project 6

Scatter Plots and its importance

Mini Project 7

#### Data Visualization

Story Telling

Scipy

Pandas

Mini Project 8

Matplotlib: basic plots and advanced plots

Mini Project 9

Seaborn: basic plots and advanced plots

Mini Project 10

NLP: N-gram models of language

Project 3: NLP

Web Scrapping

Project 4: Web scraping and visualization

#### Inferential Statistics

Probability

Discrete Probability Distribution: Binomial Distribution, Poisson

Continuous Probability Distribution: Normal, t distribution, Exponential

Correlation

Mini Project 11

#### Data Science Methodology

Introduction

Types of learning

Data Acquisition

Data Wrangling

Model Development

Model Evaluation

Scikit-Learn package

#### Machine Learning

Regression: Simple linear, multiple linear, ridge, lasso, decision tree, random forest

Project 5

Classification: svm, decision tree, random forest, naïve bayes, bagging, boosting

Project 6

Clustering: K means, Hierarchical

Project 7

Mini Project 12

#### Deep Learning

Neural Network – ANN, CNN, RNN

Autoencoders

Long Short-term memory (LSTM)

Restricted Boltzman Machine (RBM)

Project 7

### Duration & Timings :

Total Hours – 100 – 120 Hours.

Training Type: Online Live Interactive Session.

Faculty: Experienced.

### Weekday Evening Schedule:

Start Date :Monday, June 3,2024.

Duration: 13 – 15 Weeks

Days: MON – THU (4 Days/ Week)

Time – 8:30 PM to 10:30 PM (EST)

### Inquiry Now

USA: +1 734 418 2465 | India: +91 40 4018 1306

## Reviews

There are no reviews yet.