Data Science October 2024 Batch
A Data Science course equips students to analyze and interpret data using tools like Python, pandas, and machine learning algorithms. It covers data cleaning, visualization, and predictive modeling, enabling insights from large datasets. Students gain hands-on experience, preparing for roles like data scientists across various industries.
₹199.00
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Course Overview
Our mentor-led courses provide a more interactive experience with 3-4 live classes every week, guided by expert instructors. You'll also have access to recorded sessions to review at your convenience. Benefit from real-time interaction, personalized feedback, and the support of mentors to keep you on track throughout your learning journey.
Schedule of Classes
Start Date & End Date
Oct 01 2024 - May 31 2025
Course Curriculum
1 Subject
Data Science October 2024 Batch
46 Learning Materials
Induction Session and Class Agenda
Induction Session
Agenda
Introduction to Python
Class 1
Class 2
Class 3
Class 4
Class 5
Class 6
Class 7
Class 8
Class 9
Class 10
Class 11
Class 12
Pre-Recorded Sesssions
Class 1 - Introduction to Data Science/Python Basics
Class 2 - Python Basics: Variables, data types, loops, conditions, & functions.
Class 3 - Data Acquisition: Data sources, data formats, Methods to collect and clean data
Class 4 - Data Exploration: Descriptive statistics, data visualization, & correlation analysis
Class 5 - Data Preparation: Data cleaning, feature scaling, encoding categorical data.
Class 6 - Overview of machine learning, types of machine learning algorithms, & supervised learning.
Class 7 - data cleaning project using Python & Pandas during the live session.
Class 8 - Linear Regression: Simple linear regression, multiple linear regression
Class 9 - Classification: Logistic regression, K-Nearest Neighbors, & model evaluation
Class 10 - Decision Trees: Introduction to decision trees, Gini index, & Information gain.
Class 11 - Random Forest: Introduction to random forests, bagging, and boosting.
Class 12 - supervised learning project using scikit-learn during the live session.
Class 13 - Introduction to unsupervised learning, clustering algorithms, and K-Means clustering.
Class 14 - Introduction to principal component analysis (PCA) and t-Distributed (t-SNE)
Class 15 - Introduction to NLP, tokenization, stemming, & lemmatization
Class 16 - Introduction to sentiment analysis, preprocessing, feature extraction.
Class 17 - Introduction to text classification, bag-of-words model, and Naive Bayes.
Class 18 - text classification project using NLP techniques during the live session.
Class 19 - Introduction to artificial neural networks, perceptron
Class 20 - neural network project using TensorFlow during the live session.
Class 21 - Introduction to CNN, convolutional layers, & pooling layers.
Class 22 - CNN project using TensorFlow during the live session.
Class 23 - Recurrent Neural Networks (RNN): Introduction to RNN, LSTM, and GRU.
Class 24 -Introduction to time series analysis, trend, seasonality, and autocorrelation.
Class 25 - RNN project using TensorFlow during the live session.
Class 26 -Introduction to forecasting, moving average, exponential smoothing
Notes
Numpy Study Material
Data Science Interview Questions
Data Visuals Study Material
Python Study Material
Excel
ML Algorithms
Course Instructor
Teachnook Team
161 Courses • 16947 Students
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