Data Science analyst

Description

Beginner To Adanced 

Module 1: Introduction to Data Science

• What is Data Science?
• Data Science lifecycle
• Real-world applications
• Roles: Data Analyst vs. Data Scientist vs. Data Engineer

Module 2: Python for Data Science

• Python basics: variables, data types, control structures
• Functions, loops, list/dict comprehension
• File handling
• Libraries:
  • NumPy (arrays, broadcasting, math)
  • Pandas (dataframes, series, manipulation)
  • Matplotlib / Seaborn (basic plots)

Project Idea: Titanic survival prediction (basic EDA + classification)

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Intermediate Level

Module 3: Data Wrangling & EDA

• Cleaning: missing values, outliers, duplicates
• Parsing dates, encoding categories
• EDA techniques
• Visualization: box plots, heatmaps, histograms

Module 4: Statistics & Probability

• Descriptive statistics
• Distributions (normal, binomial, Poisson)
• Central Limit Theorem
• Hypothesis testing
• Confidence intervals, p-values

Module 5: Databases & SQL

• Relational databases
• SQL operations (SELECT, JOIN, GROUP BY, etc.)
• Subqueries, window functions
• Connecting SQL with Python

Project Idea: Retail sales dashboard using SQL + Pandas

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Advanced Level

Module 6: Machine Learning (ML) Basics

• Supervised vs. unsupervised learning
• Scikit-learn API
• Regression: Linear, Ridge, Lasso
• Classification: Logistic Regression, k-NN, Decision Trees
• Model evaluation: accuracy, precision, recall, F1-score, confusion matrix

Module 7: Intermediate ML

• Cross-validation, grid search
• Random Forest, Gradient Boosting (XGBoost/LightGBM)
• Feature selection & engineering
• Handling imbalanced data (SMOTE, stratification)
• Pipelines and preprocessing

Module 8: Unsupervised Learning

• Clustering: k-means, DBSCAN, hierarchical
• Dimensionality reduction: PCA, t-SNE
• Association rules (Apriori, FP-Growth)

Project Idea: Customer segmentation using unsupervised techniques

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Expert Level

Module 9: Deep Learning

• Neural Networks basics
• Activation functions, loss functions, backpropagation
• Using TensorFlow or PyTorch
• CNNs (for images), RNNs/LSTMs (for sequences)
• Transfer Learning (e.g., ResNet, BERT)

Module 10: Natural Language Processing (NLP)

• Text preprocessing (tokenization, stemming, TF-IDF)
• Word embeddings: Word2Vec, GloVe
• Sentiment analysis
• Language modeling
• Transformers (e.g., Hugging Face, BERT/GPT)

Module 11: Big Data & Deployment

• Working with large datasets (Dask, PySpark)
• Cloud platforms: AWS, GCP, Azure (intro)
• ML model deployment:
  • Flask / FastAPI APIs
  • Docker basics
  • Streamlit dashboards

Capstone Projects:

1. End-to-end ML pipeline (data to deployment)
2. Deep Learning: Image classification or NLP task
3. Big Data analysis using PySpark or cloud

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Tools & Platforms

• Languages: Python, SQL
• Libraries: Pandas, NumPy, Scikit-learn, Matplotlib, Seaborn, TensorFlow/PyTorch
• Version Control: Git, GitHub
• Environments: Jupyter Notebook, VS Code, Google Colab
• Cloud: AWS S3, Lambda, EC2 (basic