Data Science using Python Training

Course Duration : 14 Weeks

Level : All Levels

Instructor-Led Training : Yes

Live Classroom: Yes

Live-Online : Yes

Transform data into insights with Python

The Data Science with Python course teaches you to master the concepts of Python programming. Through this Python for Data Science training, you will learn Data Analysis, Machine Learning, Data Visualization, Web Scraping, & NLP. Upon course completion, you will master the essential Data Science tools using Python

Vetted curriculum by Industry experts

Real-Time Experienced Trainers

Hands-On Learning

Extensive Career support

Unlock the power of Data Science with our Python Training! Dive into this comprehensive course to harness Python’s powerful libraries for analyzing, visualizing, and interpreting complex data. Enroll today!

Weeks Duration

Hr/Week Therory

Hr/Week Lab

Students

Introduction to Data Science

Application of Data Science

The Machine Learning

Supervised, Unsupervised & Reinforcement Learning

Essential Python for Data Science

Types of Data
1. Numeric, Text, List, & Dictionary
The pandas
1. DataFrame and Series
2. CSV Files
3. Excel Spreadsheets
4. JSON

Scikit-Learn package

Model
Model Hyperparameters
The scikit-learn API

How is Python code organized

How do we use modules and packages

Python’s execution model

Names and namespaces Scopes

Regression

Simple Linear Regression

The Method of Least Squares

Multiple Linear Regression

Estimating the Regression Coefficients (β₀, β₁, β₂ and β₃)
Logarithmic Transformations of
Variables
Correlation Matrices

Conducting Regression Analysis Using Python

The Correlation Coefficient
The Statsmodels formula API
Analyzing the Model Summary
The Model Formula Language
Intercept Handling

Multiple Regression Analysis
Assumptions of Regression Analysis
Explaining the Results of Regression Analysis

Regression Analysis Checks and Balances
The F-test
The T-test

Binary Classification

Understanding the Business Context

Business Discovery
Testing Business Hypotheses Using Exploratory Data Analysis
Visualization for Exploratory Data Analysis
Intuitions from the Exploratory Analysis

Feature Engineering

Business-Driven Feature Engineering

Data-Driven Feature Engineering

A Quick Peek at Data Types and a Descriptive Summary

Correlation Matrix and Visualization

Skewness of Data
Histograms
Density Plots
Other Feature Engineering Methods
Summarizing Feature Engineering
Building a Binary Classification Model Using the Logistic Regression Function
Logistic Regression Demystified
Metrics for Evaluating Model Performance
Confusion Matrix
Accuracy
Classification Report
Data Preprocessing

Testimonial's

Ideal place for anyone willing to learn Data Science and Data Analytics. The teacher is very passionate about conveying the subject knowledge in a very understandable way for anyone. Covers basics to advanced concepts. Highly recommend Invictus !! “

N. Arnav

The classes are interactive and the trainer has complete knowledge of the subject. The trainer had given us hands-on which helped us in clarifying the concepts. A detailed description of all the concepts related to deep learning was given like activation function, convolutional layer, Maxpooling layer, etc. were given.

M. Arun

All relevant topics were taught and understood well. Proper hands-on were given with encouragement of high level of participation. Teachings helped me in clearing all the related assessments

R. Veer

Excellent teaching, The way of explanation is too good and easy to understand. The way the trainer teaches the theory along with practical sessions has helped me a lot in learning and understanding Data Science. The whole teaching is done practically. It’s really helpful.

K. Pranav

Sessions were very informative and interactive. I got to learn about the in-depth working of the algorithms. I got to do a lot of Hands-on projects with the trainer’s help.

D. Aaradhya