\$700.00

Category:

## Description

Our data analyst certification helps you learn analytics tools and techniques, how to work with SQL databases, R and Python, how to create data visualizations, and apply statistics and predictive analytics in a business environment

### Skills you will learn:

Python Programming, R Programming, SQL Programming, Tableau, 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

Working with Database

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

#### SQL Programming

Introduction to Database

Introduction to SQL

SQL JOIN and OPERATORS

CRUD operations on Tables

Data Wrangling with SQL

Project 5

#### R programming

R Basics

Data types

Loops

Data Visualization

Regression: Simple and Multiple

Classification: KNN, Logistics

Clustering: K Means, Hierarchical

Project 6

#### Statistics

Probability

Probability Distribution – Discrete and Continuous

Hypothesis building

#### 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 7

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

Project 8

Clustering: K means, Hierarchical

Project 9

Mini Project 12

#### Deep Learning

Neural Network – ANN, CNN, RNN

Autoencoders

Long Short-term memory (LSTM)

Restricted Boltzman Machine (RBM)

Project 10

#### Working with Tableau

Introduction to Visualization

Concepts: Filter, Join, Hierarchy, Groups, Set

Charts and Dashboard

Forecasting and Clustering in Tableau

### Duration & Timings :

Total Hours – 100 – 120 Hours.

Training Type: Online Live Interactive Session.

Faculty: Experienced.

### Weekend Morning Schedule:

Start Date :Monday, March 11,2024.

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.