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Data Analytics Mentorship Program

$800.00

Category:

Description

About the course:

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

The following topics will be covered as part of Data Analytics Mentorship Program .

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

Association: Market Basket Analysis

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

Business Stories

Duration & Timings :

Duration – 100 Hours.

Training Type: Online Live Interactive Session.

Faculty: Experienced.

Access to Class Recordings.

Weekend Schedule 

Start Date :Monday, January 2, 2023.

Timing – 9:30 AM – 12:30 PM EST. 

Days: SAT & SUN (2 Days / Week). 

Duration – 16 Weeks.

 Inquiry Now         Discount Offer 

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

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