Data Science – 6-Month Learning Path
Month 1: Introduction to Data Science & Python – Introduction to Data Science– Python programming fundamentals– Data structures and algorithms in Python– Libraries: NumPy, Pandas, Matplotlib– Data exploration and visualization Month 2: Data Wrangling & Exploratory Data Analysis (EDA) – Data cleaning and preprocessing– Handling missing data and outliers– EDA with Pandas, Matplotlib, and Seaborn– Feature engineering– Introduction to statistical concepts Month 3: Machine Learning Basics – Introduction to Machine Learning– Supervised vs. Unsupervised Learning– Regression (Linear, Multiple)– Classification (Logistic Regression, k-Nearest Neighbors)– Model evaluation and performance metrics Month 4: Advanced Machine Learning Techniques – Decision Trees, Random Forests, Gradient Boosting– Support Vector Machines (SVM)– Clustering (K-Means, Hierarchical)– Dimensionality reduction (PCA)– Time series forecasting Month 5: Deep Learning Fundamentals – Introduction to Neural Networks– Convolutional Neural Networks (CNNs)– Recurrent Neural Networks (RNNs)– Introduction to TensorFlow and Keras– Building and deploying deep learning models Month 6: Capstone Project – Work on a real-world project using data science techniques learned.– Present project outcomes and models.– Resume building, interview preparation, and job portal handling. – Capstone Project: Each course culminates in a capstone project where students work on realworld problems and build a professional portfolio. – Certifications: Upon completion, students receive two national-level certifications. – Internship Opportunities: A 6-month internship is provided to gain practical experience. – Placement Assistance: Lifetime placement support, resume building, and job interview preparation are available for all students.
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