What is Data Science?
Understand the core of data science and its importance in today’s world.
History and Evolution of Data Science
Explore how data science has evolved over time and its growing significance.
Key Components: Data, Algorithms, Models, and Insights
Dive into the building blocks of data science and how they create value.
Overview of Data Science Tools and Technologies
A glance at the key tools and technologies that every data scientist must know.
Understanding the Data Science Life Cycle
Learn the step-by-step process that data scientists follow to extract value from data.
Roles and Responsibilities of a Data Scientist
Gain clarity on what it takes to be a successful data scientist in the industry.
Mathematics for Data Science:
Linear Algebra: Vectors, matrices, eigenvalues, and eigenvectors.
Calculus: Derivatives, partial derivatives, and gradients.
Probability Theory: Bayes’ Theorem, probability distributions.
Statistics for Data Science:
Descriptive Statistics: Measures like mean, median, mode, variance, and standard deviation.
Inferential Statistics: Hypothesis testing, confidence intervals.
Correlation and Causality
Sampling Techniques: Random, stratified sampling.
Statistical Tests: t-tests, chi-square tests.
Introduction to Python Programming
Get familiar with the versatile programming language of data science.
Python Libraries for Data Science:
Learn to use key libraries like NumPy, Pandas, Matplotlib, and Seaborn.
Data Structures in Python:
Understand Lists, Tuples, Dictionaries, and Sets.
File Handling in Python:
Work with CSV, Excel, and JSON files.
Control Flow and Loops
Master conditionals and looping constructs in Python.
Functions and Lambda Expressions
Write modular, efficient code.
Error Handling and Debugging in Python
Techniques to ensure your Python code runs smoothly.
Importance of Data Cleaning in Data Science
Learn why clean data is the foundation of successful data analysis.
Handling Missing Values:
Strategies like mean, median imputation or removing missing values.
Data Transformation:
Scale, normalize, and standardize your data to make it model-ready.
Handling Categorical Data:
Techniques such as label encoding and one-hot encoding.
Detecting and Handling Outliers
Identifying and dealing with outliers in your dataset.
Feature Engineering and Feature Selection
Improve your data’s predictive power.
Data Aggregation and Grouping
Summarize data using grouping techniques.
Introduction to Data Visualization
Understand the importance of visualizing data and its insights.
Basic Visualizations:
Bar charts, line graphs, pie charts, histograms.
Advanced Visualizations:
Heatmaps, boxplots, violin plots.
Using Matplotlib and Seaborn:
Create static and statistical visualizations.
Interactive Visualizations with Plotly
Create dynamic, interactive plots.
Visualizing Large Datasets with Bokeh and D3.js
Explore advanced tools for large-scale visualizations.
Best Practices for Data Visualization
Learn how to choose the right chart for the right data.
What is Exploratory Data Analysis?
Learn the fundamentals of understanding your dataset through EDA.
Steps in EDA:
Data inspection, cleaning, and creating statistical summaries.
Univariate and Multivariate Analysis
Analyze single and multiple variables in your data.
Analyzing Distribution of Data
Explore the distribution to gain insights.
Correlation Analysis:
Pearson, Spearman, and Kendall correlations.
Outlier Detection and Analysis
Spot and manage outliers effectively.
Feature Importance & Dimensionality Reduction
Use techniques like PCA and LDA to improve model performance.
What is Machine Learning?
Get an overview of machine learning and its types.
Types of Machine Learning:
Supervised Learning
Unsupervised Learning
Reinforcement Learning
Overview of ML Algorithms:
Linear and Logistic Regression
Decision Trees, k-Nearest Neighbors (k-NN), Naive Bayes
Support Vector Machines (SVM)
Evaluating Model Performance:
Accuracy, precision, recall, F1-score, confusion matrix, ROC curve, AUC.
Overfitting and Underfitting
Learn how to balance model complexity.
Linear Regression:
Simple and Multiple Linear Regression.
Evaluation metrics: MSE, RMSE, MAE.
Logistic Regression:
Binary and multinomial classification with cost functions.
Decision Trees:
Building decision trees, Gini Index, entropy, pruning.
Random Forest:
Feature importance and model tuning in Random Forest.
Support Vector Machines (SVM):
Linear vs non-linear SVM, kernel trick, and hyperparameter tuning.
K-Means Clustering:
Use elbow method to optimize clusters.
Hierarchical Clustering:
Agglomerative vs divisive clustering, dendrograms.
Principal Component Analysis (PCA):
Reduce dimensions with PCA, applications in feature extraction.
DBSCAN:
Density-based clustering, noise handling.
Introduction to Neural Networks:
Learn about perceptrons and multi-layer perceptrons.
Activation Functions:
ReLU, sigmoid, tanh—key functions that drive neural networks.
Backpropagation Algorithm
Learn how neural networks are trained using backpropagation.
Introduction to Deep Learning:
Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Generative Adversarial Networks (GANs).
Working with Keras & TensorFlow
Hands-on experience with deep learning frameworks.
Image Classification with CNN
Build image classifiers using CNNs.
Time Series Prediction with RNN
Forecast future trends using RNNs.
Introduction to NLP
Learn how to process and analyze text data.
Text Preprocessing:
Tokenization, lemmatization, and stopword removal.
Feature Extraction:
Bag-of-Words, TF-IDF.
Sentiment Analysis and Text Classification
Sentiment analysis using NLP techniques.
Word Embeddings:
Word2Vec, GloVe for capturing semantic meaning.
Named Entity Recognition (NER):
Identify entities in text data.
Building Chatbots with NLP
Use NLP to create intelligent conversational agents.
Topic Modeling with LDA
Extract topics from large text corpora.
Introduction to Model Deployment
Learn the deployment pipeline and strategies for productionizing models.
Preparing Models for Deployment
Steps to ensure your model is ready for real-world use.
Deployment Using Flask or FastAPI
Deploy models as APIs.
Dockerizing ML Models
Containerize your models for scalability.
Cloud Deployment (AWS, Azure, Google Cloud)
Deploy models to cloud platforms.
Real-time Model Prediction & Monitoring
Manage model performance in real-time.
Version Control and Model Retraining
Ensure your model stays up-to-date with version control.
Introduction to Big Data
The challenges and opportunities of working with large datasets.
Hadoop Ecosystem:
HDFS, MapReduce for big data processing.
Apache Spark for Big Data Processing
Learn how to handle large-scale data with Spark.
Working with PySpark
Use PySpark for data analysis and machine learning.
NoSQL Databases:
MongoDB, Cassandra for large, unstructured datasets.
Distributed Computing for Machine Learning
Scale machine learning with distributed computing.
Introduction to Time Series Data
Learn the unique characteristics of time-based data.
Time Series Decomposition:
Trend, seasonality, residuals in time series data.
ARIMA Model:
Forecast future values using ARIMA.
Exponential Smoothing Methods
Time series forecasting with smoothing techniques.
Forecasting with Prophet (from Facebook)
Handle complex seasonal patterns in time series.
Handling Missing Data in Time Series
Techniques to manage gaps in time-based data.
Reinforcement Learning Overview
Learn how machines can learn optimal strategies through reinforcement learning.
Recommendation Systems:
Collaborative filtering, content-based filtering.
Anomaly Detection in Data
Techniques to spot outliers in datasets.
Graph Theory & Network Analysis
Analyze relationships using graphs.
Advanced Deep Learning Models:
Generative Adversarial Networks (GANs), Autoencoders.
Generative Models:
Learn how generative models create new data.
Project 1: Sales Forecasting with Time Series Analysis.
Project 2: Customer Segmentation using Clustering Algorithms.
Project 3: Sentiment Analysis of Social Media Data.
Project 4: Image Classification with Deep Learning.
Project 5: Predictive Maintenance in Manufacturing.
Project 6: Building a Recommendation System.
Preparing for Data Science Interviews
Tips and resources to ace your data science interviews.
Building a Data Science Portfolio
Showcase your skills with impactful projects.
Certifications in Data Science
Explore options like Coursera, edX, DataCamp.
Job Roles in Data Science:
Learn about roles like Data Scientist, Data Analyst, Machine Learning Engineer, AI Engineer.
Building a Resume & Job Search Strategies
Perfect your resume and strategy to land your dream job.
We help you identify your strengths, set goals, and align your learning path with industry trends. Take advantage of our internship opportunities and career counselling sessions to boost your professional journey.
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