Data Analytics Mentorship Program




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 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


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

Mini Project 3

Functions and Methods

Class & Objects

Mini Project 4


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



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


Discrete Probability Distribution: Binomial Distribution, Poisson

Continuous Probability Distribution: Normal, t distribution, Exponential


Mini Project 11

SQL Programming

Introduction to Database

Introduction to SQL


CRUD operations on Tables

Data Wrangling with SQL

Project 5

R programming

R Basics

Data types


Data Visualization

Regression: Simple and Multiple

Classification: KNN, Logistics

Clustering: K Means, Hierarchical

Project 6



Probability Distribution – Discrete and Continuous

Hypothesis building

Data Science Methodology


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


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 :

Total Hours – 100 – 120 Hours.

Training Type: Online Live Interactive Session.

Faculty: Experienced.

Access to Class Recordings.

Weekend Morning Schedule:

Start Date :Monday, July 22,2024.

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


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