Course Curriculum

Module 1: Python Programming
  • Python Introduction and setting up the environment
  • Python Basic Syntax and Data Types
  • Operators in Python (e.g., Arithmetic, Logical, Bitwise)
  • Strings in Python
  • Lists, Tuples, Dictionary, Sets
  • Python conditional statements (e.g., if, if-else, if-elif-else)
  • Loops in Python (e.g., while, for, break, continue)
  • Getting Started with HackerRank use cases and working on them
  • List and Dictionaries comprehension
  • Functions
  • Anonymous Functions (Lambda)
  • Generators
  • Modules
  • Exceptions and Error Handling
  • Classes and Objects (OOPS) (including different types of methods, inheritance, polymorphism, operator overloading, overriding)
  • Date and Time
  • Files (including opening, closing, reading and writing files)
  • Tkinter
  • Python for Web Development
  • Hands-On Projects (Web Scraping, Sending Automated Emails, Building a Virtual Assistant)
Module 2: Data Analysis
  • Packages (Working on Numpy, Pandas, and Matplotlib)
  • Web Scraping (learning about tools, libraries and ethical considerations)
  • Exploratory data analysis (EDA) using Pandas and NumPy
  • Data Visualization using Matplotlib and Seaborn
Module 3: Database
  • MySQL
  • MongoDB
Model 4: Dashboard Tool
  • Power BI
  • Tableau
Module 5: Statistics
  • Descriptive Statistics
  • Central tendency, variance, standard deviation, covariance, correlation, probability
  • Inferential Statistics
  • Central limit theorem, hypothesis testing, one-tailed and two-tailed test, and Chi-Square test
  • P Value, Z Score and Z test
  • T test and F test
  • ANOVA test
Module 6: Machine Learning
  • Introduction to Machine Learning
  • Introduction to data science and its applications
  • Data Engineering and Preprocessing
  • Model Evaluation and Hyperparameter Tuning
  • Supervised Learning – Regression
  • Simple and Multiple Linear Regression
  • Supervised Learning – Classification
  • Logistic Regression
  • KNN Algorithm
  • Decision Tree and Random Forest
  • Naive Bayes and SVM ( Support Vector Machine )
  • Gradient Boosting and XGBoost
  • Unsupervised Learning – Clustering
  • Recommendation Systems
  • Deployment of ML models
  • End to end project work
Module 7: Deep Learning
  • Introduction to TensorFlow and Keras
  • Artificial Neural Networks (ANNs)
  • Activation Functions, Loss Function and Optimizers
  • Forward and Backward propagation
  • Regularizer and Dropout
  • Convolutional Neural Networks (CNNs)
  • Padding, Stride and Pooling
  • Transfer Learning
  • CNN Architectures
  • YOLO Algorithm
  • Recurrent Neural Networks (RNNs)
Module 8: Model's Performance Analysis
  • Principal Component Analysis (PCA)
  • Confusion Matrix and Accuracy Score
  • Precision, Recall and F1 Score
  • Design a prediction model to classify different types of flowers
  • Create a prediction model to predict whether a person is Diabetic or not
  • Analyze the Spam dataset and design a prediction model to predict whether a mail is spam or not
Model 9: Computer Vision
  • Introduction to OpenCV module
  • Work with images and videos
  • Face and Object detection using OpenCV
Module 10: NLP
  • This section delves into essential NLP techniques, including Named Entity Recognition (NER), text preprocessing, and text representation. You'll also explore practical applications like sequential modeling and building sentiment analysis systems, equipping you to tackle real-world language processing challenges.
Bonus Module: Projects & Case Study
  • Work on various End to end real world projects

Course Duration: 8 Months

Overview

Our Data Science Training Program is a comprehensive, hands-on curriculum designed to equip individuals with the skills and knowledge needed to thrive in the data-driven world. Whether you are a beginner or looking to advance your career, this program will help you master the tools and techniques essential for data analysis, machine learning, and artificial intelligence. Participants will gain real-world experience through interactive projects, case studies, and mentorship from industry experts.

Program Highlights

  • Interactive Learning: Live sessions with industry practitioners.
  • Hands-On Projects: Work on real-world datasets to build your portfolio.
  • Comprehensive Curriculum: Covers the full data science lifecycle.
  • Career Support: Resume reviews, mock interviews, and job placement guidance.
  • Flexible Schedule: Weekend and evening classes available for working professionals.

Why Choose Us?

  • Experienced Instructors: Learn from seasoned data scientists.
  • Cutting-Edge Tools: Gain proficiency in industry-standard tools.
  • Lifetime Access: Revisit recorded sessions anytime.
  • Community Support: Network with peers and alumni.

Certification

Upon successful completion, participants will receive a Data Science Certification that validates their expertise and can be showcased on LinkedIn and resumes.

Certificate

Got any queries?

Data Science with Python

Course Includes:

  • Instrutor: Tech Booster
  • Duration: 8 Months
  • book icon Lessons: 350+
  • Enrolled: 5000+ students
  • Language: English
  • Certificate: Yes
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