Data Science Training Course Content

Module 1: Introduction to Data Science

• What is Data Science? Lifecycle & applications

• Roles: Data Analyst vs Data Scientist vs ML Engineer

• Tools overview: Python, R, SQL, Excel, Power BI, Jupyter Notebook

• Case studies in different industries

Module 2: Python for Data Science

• Python basics: variables, data types, operators

• Control flow (if, loops), functions, OOPs basics

• Libraries:

  • NumPy (arrays, numerical operations)

  • Pandas (DataFrames, data cleaning, manipulation)

  • Matplotlib & Seaborn (visualizations)

Module 3: Statistics & Probability

• Descriptive statistics (mean, median, mode, variance, std dev)

• Probability concepts & distributions (Normal, Binomial, Poisson)

• Inferential statistics (Hypothesis testing, t-test, chi-square test, ANOVA)

• Correlation & covariance

• Sampling techniques & Central Limit Theorem

Module 4: Data Wrangling & Cleaning

• Handling missing values

• Outlier detection & treatment

• Data transformation (scaling, normalization, encoding)

• Feature engineering & feature selection

Module 5: Exploratory Data Analysis (EDA)

• Univariate, bivariate & multivariate analysis

• Visualization techniques (histograms, boxplots, heatmaps, pair plots)

• Insights generation from datasets

Module 6: SQL for Data Science

• Database basics & SQL queries

• SELECT, WHERE, GROUP BY, HAVING, ORDER BY

• Joins & subqueries

• Window functions

• Connecting Python with SQL

Module 7: Machine Learning (ML)

Supervised Learning

• Regression (Linear, Multiple, Polynomial, Logistic Regression)

• Classification (KNN, Decision Trees, Random Forest, Naïve Bayes, SVM, XGBoost)

• Model evaluation metrics (accuracy, precision, recall, F1-score, ROC-AUC)

Unsupervised Learning

• Clustering (K-Means, Hierarchical, DBSCAN)

• Dimensionality Reduction (PCA, t-SNE)

Model Optimization

• Cross-validation

• Hyperparameter tuning (Grid Search, Random Search)

• Bias-variance tradeoff

Module 8: Advanced Topics

• Natural Language Processing (NLP) basics

  • Text preprocessing, Bag of Words, TF-IDF

  • Sentiment analysis

• Time Series Analysis

  • ARIMA, SARIMA, Prophet models

• Deep Learning (Intro)

  • Neural networks, TensorFlow/Keras basics

Module 9: Data Visualization & BI Tools

• Advanced visualizations with Python (Seaborn, Plotly)

• Dashboards with Power BI / Tableau

• Storytelling with data

Module 10: Big Data & Cloud (Intro)

• Hadoop & Spark basics

• Using Google Colab, AWS, Azure for ML models

• MLOps basics (CI/CD for ML)

Module 11: Capstone Projects

Real-world projects such as:

• Predicting house prices (Regression)

• Customer churn prediction (Classification)

• Market basket analysis (Unsupervised Learning)

• Sentiment analysis of tweets (NLP)

• Sales forecasting (Time Series)

• Building an interactive dashboard (Power BI/Tableau)

Module 12: Career Prep

• Data Science interview questions (Python, ML, SQL, Stats)

• Kaggle competitions & portfolio building

• Resume & LinkedIn optimization for Data Science roles

• Guidance for certifications (e.g., Microsoft, Google, IBM Data Science)