mrperfectpandit / DS-Curriculum

Brief Introduction

A complete guide to learn data science for beginners.

This learning path is intended for everyone who wants to learn data science and build a career in data field especially data analyst and data scientist. In this guide, there is a corresponding link in each section that will help you to learn..

Table of Contents

Table of Contents

1. Programming
2. Mathematics & Statistics
3. Machine Learning
   • Supervised Learning
   • Unsupervised Learning
4. Evaluation Metrics
5. Deep Learning
6. Advanced Natural Language Processing (NLP)
7. Capstone Projects

Programming

1. Python
   • Variables and Data Types
   • Operators and Expressions
   • Control Flow (if, elif, else, for, while)
   • Functions
   • Python Data Structure - Lists, Tuples, and Dictionaries
   • File Handling
   • Exception Handling
   • Modules and Packages
   • Lambda Functions

Data Processing Modules

1. NumPy

   • Array Creation and Manipulation
   • Mathematical Functions
   • Linear Algebra Operations
   • Statistical Functions
   • Broadcasting
   • Indexing and Slicing
   • File I/O

2. Pandas

   • Data Structures (Series, DataFrame)
   • Data Cleaning and Preprocessing
   • Indexing and Selecting Data
   • Grouping and Aggregating Data
   • Merging and Joining DataFrames
   • Reshaping and Pivoting Data
   • Handling Missing Data

3. Matplotlib

   • Basic Plotting (Line Plot, Scatter Plot, Bar Plot, Histogram)
   • Customizing Plots (Labels, Titles, Legends, Colors)
   • Subplots and Layouts
   • Plot Annotations and Text
   • 3D Plotting

4. Seaborn

   • Statistical Data Visualization
   • Distribution Plots (Histograms, Kernel Density Estimation)
   • Categorical Plots (Bar Plots, Box Plots, Violin Plots)
   • Scatter and Line Plots with Regression Analysis
   • Pair Plots and Heatmaps

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Mathematics & Statistics

1. Descriptive Statistics
2. Data Distributions
3. Statistical Testing
4. Exploratory Data Analysis
5. TOOLBOX: Pandas
6. TOOLBOX: Numpy
7. TOOLBOX: Matplotlib
8. TOOLBOX: Seaborn

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Machine Learning

• Supervised Learning

1. Linear Regression
2. Logistic Regression
3. Decision Tree
4. K-NN (K-Nearest Neighbors)
5. Naive Bayes
6. Support Vector Machine
7. Random Forest
8. XGBoost
9. TOOLBOX: Scikit Learn
10. CASE STUDY 1:
11. CASE STUDY 2:

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• Unsupervised Learning

1. K-Means Clustering
2. Hierarchical Clustering
3. DBSCAN (Density-Based Spatial Clustering of Applications with Noise)
4. Gaussian Mixture Models (GMM)
5. Principal Component Analysis (PCA)

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Evaluation Metrics

• Supervised Learning

1. Confusion Matrix
2. Accuracy
3. Precision
4. Recall
5. F Score
6. ROC (Receiver Operating Characteristic)
7. ROC AUC (Area Under Curve)
8. MAE
9. MSE

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• Unsupervised Learning

1. Elbow Method
2. Silhouette Score

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Deep Learning

1. Activation Functions
2. Linear Layer
3. CNN (Convolutional Neural Networks)
4. Optimization
5. Loss Functions / Objective Functions
6. Dropout
7. Batchnorm
8. Learning Rate Scheduler
9. TOOLBOX: Tensorflow
10. TOOLBOX: Keras

Advance topic Natural Language Processing (NLP)

1. Tokenization
2. Stopwords Removal
3. Stemming and Lemmatization
4. Bag of Words (BoW)
5. Term Frequency-Inverse Document Frequency (TF-IDF)
6. Word Embeddings (Word2Vec, GloVe)
7. Recurrent Neural Networks (RNNs)
8. Long Short-Term Memory (LSTM)
9. Gated Recurrent Units (GRU)
10. Attention Mechanism

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Capstone mini Project---

Capstone major Project---