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Data Science Course Syllabus

Duration : 2 Months
Category :Data Science, PANDAS, NumPy

The objective of the Data Science Training program is to equip learners with the analytical, statistical, and programming skills required to extract meaningful insights from data and support data-driven decision-making in modern organizations. This course provides a comprehensive foundation in data analysis, visualization, machine learning, and real-world problem-solving using industry-standard tools and frameworks.

Learners will gain hands-on experience working with Python, NumPy, Pandas, SQL, data visualization libraries, and machine learning algorithms. The training covers the complete data science workflow—including data cleaning, feature engineering, model development, evaluation, and deployment. Emphasis is placed on industry best practices, practical projects, and real-world datasets to ensure learners build job-ready skills.

By the end of the course, learners will be able to analyze complex datasets, build predictive models, create compelling visualizations, and deliver data-driven solutions—preparing them for roles such as Data Analyst, Data Scientist, Machine Learning Engineer, and Business Analyst across diverse industry domains.

  • Python Basics
  • Data Types
  • Conditional Statements
  • Loops
  • Functions
  • Lists, Tuples, Sets, Dictionaries
  • List Comprehension
  • Exception Handling
  • File Handling
  • OOP Basics
  • NumPy
  • Pandas
  • Algebra
  • Functions
  • Logarithms
  • Matrices
  • Vectors
  • Calculus Basics
  • Derivatives
  • Partial Derivatives
  • Descriptive Statistics
  • Mean, Median, Mode
  • Variance
  • Standard Deviation
  • Percentiles & Quartiles
  • Probability
  • Conditional Probability
  • Bayes' Theorem
  • Probability Distributions
  • Normal Distribution
  • Binomial Distribution
  • Correlation
  • Covariance
  • Sampling
  • Central Limit Theorem
  • Hypothesis Testing
  • P-value
  • Confidence Interval
  • SQL Basics
  • Filtering & Sorting
  • Aggregate Functions
  • GROUP BY & HAVING
  • Joins
  • Subqueries
  • CTEs
  • CASE Statements
  • Window Functions
  • Views
  • Indexes
  • Missing Values
  • Duplicate Values
  • Outlier Detection
  • Data Type Conversion
  • Encoding
  • Normalization
  • Standardization
  • Feature Scaling
  • Train/Test Split
  • Data Leakage
  • Dataset Understanding
  • Univariate Analysis
  • Bivariate Analysis
  • Multivariate Analysis
  • Distribution Analysis
  • Correlation Analysis
  • Outlier Analysis
  • Finding Patterns & Insights
  • Matplotlib
  • Seaborn
  • Charts & Graphs
  • Histograms
  • Bar Charts
  • Scatter Plots
  • Box Plots
  • Heatmaps
  • Pair Plots

Supervised Learning

  • Linear Regression
  • Multiple Linear Regression
  • Polynomial Regression
  • Logistic Regression
  • KNN
  • Naive Bayes
  • Decision Tree
  • Random Forest
  • SVM
  • Gradient Boosting
  • XGBoost

Unsupervised Learning

  • K-Means
  • Hierarchical Clustering
  • DBSCAN
  • PCA
  • Dimensionality Reduction
  • MAE
  • MSE
  • RMSE
  • R²
  • Accuracy
  • Precision
  • Recall
  • F1 Score
  • Confusion Matrix
  • ROC-AUC
  • Cross Validation
  • Bias & Variance
  • Underfitting & Overfitting
  • Feature Selection
  • Feature Extraction
  • Encoding
  • Scaling
  • Transformation
  • Binning
  • Polynomial Features
  • Handling Imbalanced Data
  • SMOTE
  • ML Pipelines
  • Ensemble Learning
  • Bagging
  • Boosting
  • Random Forest
  • XGBoost
  • LightGBM
  • CatBoost
  • Hyperparameter Tuning
  • Grid Search
  • Random Search
  • Neural Networks
  • Perceptron
  • Activation Functions
  • Loss Functions
  • Forward Propagation
  • Backpropagation
  • Optimizers
  • ANN
  • CNN
  • RNN
  • LSTM
  • GRU
  • Transfer Learning
  • Text Preprocessing
  • Tokenization
  • Stop Words
  • Stemming
  • Lemmatization
  • Bag of Words
  • TF-IDF
  • Word Embeddings
  • Word2Vec
  • Text Classification
  • Sentiment Analysis
  • NER
  • Transformers
  • BERT
  • LLM Fundamentals
  • Prompt Engineering
  • Embeddings
  • Vector Databases
  • RAG
  • Chunking
  • Semantic Search
  • LLM APIs
  • LangChain Basics
  • Fine-Tuning Basics
  • Data Analysis Project
  • Classification Project
  • Regression Project
  • Clustering Project
  • Recommendation System
  • NLP Project
  • Deep Learning Project
  • End-to-End Data Science Project
  • Python Coding
  • SQL Problems
  • Statistics Questions
  • Machine Learning Questions
  • Case Studies
  • ML Project Discussion
  • Business/Data Interpretation
  • Data Science Interview Questions

Alexzender Alex

CSE Teacher

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Manager

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Web Designer

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