Data Science Professional Program Curriculum

1. Course Title:
Comprehensive Data Science Professional Program

2. Duration:
12 Weeks (Approximately 3 Months)
(Assuming 8-10 hours of learning per week, including lectures, practicals, and self-study)

3. Module-wise Breakdown:

Module 1: Foundations of Data Science & Python Programming (3 Weeks)

• Module Name: Introduction to Data Science & Python Essentials

• Topics Covered:

  • What is Data Science? Lifecycle of a Data Science project.

  • Roles in Data Science (Analyst, Engineer, Scientist, ML Engineer).

  • Introduction to Python: Why Python for Data Science?

  • Setting up the Python Environment (Anaconda, Jupyter Notebook, VS Code).

  • Python Basics: Variables, Data Types (Integers, Floats, Strings, Booleans).

  • Python Data Structures: Lists, Tuples, Dictionaries, Sets.

  • Control Flow: Conditional statements (if-elif-else), Loops (for, while).

  • Functions: Defining functions, arguments, return values, lambda functions.

  • File Handling: Reading from and writing to files (CSV, TXT).

  • Introduction to Object-Oriented Programming (OOP) concepts (Classes, Objects).

• Tools and Technologies Used:

  • Python 3.x

  • Jupyter Notebook / Google Colab

  • Anaconda / VS Code

• Practical Assignments/Mini-Projects:

  • Simple Python scripts for basic calculations and string manipulations.

  • A mini-project involving reading data from a CSV, performing basic calculations, and writing results to a new file (e.g., student grade calculator).

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  • Module 2: Applied Mathematics & Statistics for Data Science (2 Weeks)

  • Module Name: Essential Mathematics & Statistics

• Topics Covered:

  • Linear Algebra Basics: Vectors, matrices, operations (dot product, transpose).

  • Calculus Basics: Derivatives, gradients (intuitive understanding for optimization).

  • Probability: Basic probability concepts, conditional probability, Bayes’ theorem.

  • Descriptive Statistics: Measures of central tendency (mean, median, mode), measures of dispersion (variance, standard deviation, range, IQR).

  • Inferential Statistics: Population vs. Sample, Hypothesis testing (t-tests, chi-squared), p-value, confidence intervals.

  • Distributions: Normal, Binomial, Poisson.

• Tools and Technologies Used:

  • Python

  • NumPy (for numerical operations)

  • SciPy (for statistical functions)

  • Matplotlib (for basic visualizations of distributions)

• Practical Assignments/Mini-Projects:

  • Implement basic linear algebra operations using NumPy.

  • Calculate descriptive statistics for a given dataset.

  • Perform a simple hypothesis test on a sample dataset.

  • Visualize different probability distributions.

Module 3: Data Collection, Wrangling & Exploratory Data Analysis (EDA) (2 Weeks)

• Module Name: Mastering Data Manipulation & EDA

• Topics Covered:

  • Introduction to NumPy: Arrays, array indexing, numerical operations.

  • Introduction to Pandas: Series, DataFrames, data loading (CSV, Excel, SQL).

  • Data Cleaning: Handling missing values (imputation, deletion), duplicate data, outliers.

  • Data Transformation: Filtering, sorting, grouping (groupby), merging, concatenating, pivoting.

  • Feature Engineering Basics: Creating new features from existing ones, binning, encoding categorical variables.

  • Exploratory Data Analysis (EDA): Understanding data patterns, univariate and bivariate analysis.

  • Introduction to SQL: Basic SELECT queries, WHERE, GROUP BY, JOINs for data extraction.

  • Web Scraping Basics (Optional, using Beautiful Soup & Requests).

• Tools and Technologies Used:

  • Python, Pandas, NumPy

  • SQL (SQLite, or connection to a common DB like PostgreSQL/MySQL)

  • Beautiful Soup, Requests (optional for web scraping)

• Practical Assignments/Mini-Projects:

  • Clean and preprocess a messy real-world dataset (e.g., Titanic dataset).

  • Perform comprehensive EDA on a dataset, deriving insights and summarizing findings.

  • Write SQL queries to extract and aggregate data from a sample database.

  • (Optional) Scrape data from a simple website.

Module 4: Data Visualization Techniques (2 Weeks)

• Module Name: Visual Storytelling with Data

• Topics Covered:

  • Principles of effective data visualization.

  • Matplotlib: Basic plots (line, bar, scatter, histogram), customizing plots, subplots.

  • Seaborn: Statistical visualizations, enhanced aesthetics, complex plots (heatmap, pairplot, violin plot).

  • Interactive Visualization: Introduction to Plotly/Bokeh for creating interactive charts.

  • Business Intelligence (BI) Tools: Introduction to Power BI or Tableau for dashboard creation.

    • Connecting to data sources.

    • Creating basic charts and dashboards.

    • Filters and slicers.

• Tools and Technologies Used:

  • Python, Matplotlib, Seaborn

  • Plotly / Bokeh (introduction)

  • Power BI Desktop / Tableau Public

• Practical Assignments/Mini-Projects:

  • Create a variety of static plots using Matplotlib and Seaborn to visualize insights from a dataset.

  • Develop an interactive plot using Plotly or Bokeh.

  • Build a simple interactive dashboard using Power BI or Tableau with a provided dataset.

Module 5: Foundations of Machine Learning (2 Weeks)

• Module Name: Understanding Machine Learning Concepts

• Topics Covered:

  • Introduction to Machine Learning: What, why, and how.

  • Types of Machine Learning: Supervised, Unsupervised, Reinforcement Learning.

  • Common ML Tasks: Regression, Classification, Clustering.

  • The Machine Learning Workflow: Data collection, preprocessing, model training, evaluation, deployment.

  • Key Concepts: Features, labels, training set, test set, validation set.

  • Bias-Variance Tradeoff, Overfitting, Underfitting.

  • Cross-Validation techniques.

  • Model Evaluation Metrics:

    • Regression: MSE, RMSE, MAE, R-squared.

    • Classification: Accuracy, Precision, Recall, F1-score, Confusion Matrix, ROC-AUC.

• Tools and Technologies Used:

  • Python

  • Scikit-learn (for basic model building and evaluation utilities)

• Practical Assignments/Mini-Projects:

  • Implement train-test split and cross-validation on a dataset.

  • Calculate and interpret various evaluation metrics for given model predictions.

  • Discuss scenarios of overfitting/underfitting and how to identify them.

Module 6: Advanced Topics & Specializations (Optional Electives or Overview – 2 Weeks)

• Module Name: Exploring Advanced Data Science Domains

  • Natural Language Processing (NLP) Basics:

    • Text preprocessing: Tokenization, stemming, lemmatization, stop-word removal.

    • Feature extraction: Bag-of-Words, TF-IDF.

    • Basic Sentiment Analysis.

  • Introduction to Deep Learning:

    • Neural Networks concepts: Neurons, layers, activation functions.

    • Introduction to TensorFlow/Keras or PyTorch.

    • Simple ANN for classification/regression.

  • Introduction to Big Data Technologies (Overview):

    • Hadoop Ecosystem (HDFS, MapReduce).

    • Apache Spark (RDDs, DataFrames).

• Tools and Technologies Used:

  • Python, Statsmodels, Prophet (for Time Series)

  • NLTK, spaCy, Scikit-learn (for NLP)

  • TensorFlow/Keras or PyTorch (for Deep Learning)

  • (Conceptual understanding for Big Data tools)

• Practical Assignments/Mini-Projects:

  • Build a basic time series forecasting model.

  • Perform sentiment analysis on a dataset of reviews.

  • Build a simple neural network for image or tabular data classification

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  • Capstone Project (4 Weeks)

  • Description: Students will work individually or in small groups on a comprehensive data science project from start to finish. This involves defining a problem, collecting and cleaning data, performing EDA, building and evaluating machine learning models, and (optionally) deploying a simple version or creating a detailed report and presentation.

• Examples:

  • Predicting house prices with advanced feature engineering and model comparison.

  • Customer segmentation and targeted marketing strategy proposal.

  • Building a movie/product recommendation system.

  • Fraud detection system.

  • Analyzing social media sentiment on a specific topic.

• Deliverables:

  • Project proposal.

  • Well-documented code (Jupyter Notebook or Python scripts).

  • A comprehensive report detailing methodology, findings, and conclusions.

  • A final presentation of the project.

• Tools and Technologies Used: All relevant tools covered throughout the course.

6. Career Preparation Module (Parallel to Capstone or Final Week)

• Module Name: Launching Your Data Science Career

• Topics Covered:

  • Crafting an effective Data Science resume and cover letter.

  • Building a strong LinkedIn profile and professional network.

  • Portfolio development: Showcasing projects on GitHub.

  • Preparing for technical interviews (Python, SQL, ML concepts, case studies).

  • Behavioral interview preparation (STAR method).

  • Mock interview sessions with feedback.

  • Understanding the job market and different Data Science roles.

  • Negotiation skills.

• Activities:

  • Resume review workshops.

  • LinkedIn profile optimization sessions.

  • Multiple mock interviews (technical and HR).

  • Guest lectures from industry professionals.

 Mode of Delivery:

• Hybrid Model Recommended:

  • Live Online Interactive Sessions: For lectures, Q&A, and discussions (e.g., 2-3 sessions per week).

  • Recorded Sessions: For students to review concepts at their own pace.

  • Offline/In-Person Workshops (Optional): For intensive practical sessions, capstone project mentorship, or networking events, if feasible.

  • Self-Paced Learning: Reading materials, assignments, and mini-projects.

9. Tools/Platforms Used for Teaching:

• Live Sessions: Zoom, Microsoft Teams, Google Meet.

• Coding Environments:

  • Jupyter Notebook / JupyterLab

  • Google Colaboratory (for easy setup and GPU access for advanced topics)

  • VS Code with Python extension

• Version Control: GitHub / GitLab for code sharing and portfolio.

• Communication: Slack or Discord channel for student-instructor and peer-to-peer interaction.

• BI Tools: Power BI Desktop (free), Tableau Public (free).