Data Science Mentorship Program




About the course:

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

The following topics will be covered as part of Data Science 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

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

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 5

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

Project 6

Clustering: K means, Hierarchical

Project 7

Association: Market Basket Analysis

Mini Project 12

Deep Learning

Neural Network – ANN, CNN, RNN


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.

Access to Class Recordings.

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


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