Data Science Training Course Syllabus

A comprehensive, hands-on Data Science Training Course designed for beginners, this program spans approximately 18 weeks of structured learning, combining foundational programming, statistical analysis, machine learning, and big data technologies. With a balanced mix of theory and real-world projects, learners will gain proficiency in Python, R, SQL, machine learning frameworks, data visualization tools, and big data platforms. The course concludes with a capstone project that integrates all skills, preparing learners for industry roles in data science and AI.

Module 1: Python for Data Science

Estimated time: 30 hours

• Python basics and syntax
• Data structures: lists, dictionaries, tuples
• NumPy for numerical computing
• Pandas for data manipulation and exploratory data analysis

Module 2: Statistics & Probability

Estimated time: 30 hours

• Descriptive statistics: mean, median, variance, standard deviation
• Inferential statistics: hypothesis testing, confidence intervals
• Probability distributions: normal, binomial, Poisson
• Statistical tests: t-tests, ANOVA

Module 3: Machine Learning with Scikit-learn

Estimated time: 45 hours

• Supervised learning: classification and regression
• Unsupervised learning: clustering and dimensionality reduction
• Model evaluation metrics: accuracy, precision, recall, F1-score
• Cross-validation and hyperparameter tuning

Module 4: Deep Learning with TensorFlow & Keras

Estimated time: 45 hours

• Neural networks fundamentals and activation functions
• Convolutional Neural Networks (CNNs) for image data
• Recurrent Neural Networks (RNNs) for sequence data
• Training and evaluating deep learning models

Module 5: R Programming for Data Science

Estimated time: 30 hours

• Data frames and data wrangling with dplyr
• Data visualization using ggplot2
• Statistical modeling in R
• Exploratory data analysis with R

Module 6: SQL for Data Science

Estimated time: 22 hours

• SQL fundamentals: SELECT, WHERE, GROUP BY
• Joins and subqueries
• Aggregations and window functions
• Querying structured data for reporting and analysis

Module 7: Data Visualization with Tableau & Power BI

Estimated time: 30 hours

• Building interactive dashboards
• Creating charts and filters
• Using calculated fields and visual analytics
• Data storytelling with Tableau and Power BI

Module 8: Big Data & Spark for Data Science

Estimated time: 30 hours

• Hadoop ecosystem overview
• Spark RDDs and DataFrames
• Data processing with PySpark
• Spark MLlib for scalable machine learning

Module 9: Capstone Project

Estimated time: 30 hours

• End-to-end data science case study
• Data wrangling, modeling, and evaluation
• Visualization and presentation of insights

Prerequisites

• Basic computer literacy
• Familiarity with high school-level mathematics
• No prior programming experience required, but helpful

What You'll Be Able to Do After

• Analyze and visualize data using Python and R
• Apply machine learning and deep learning techniques to real-world datasets
• Query and manage data using SQL
• Build interactive dashboards with Tableau and Power BI
• Process big data using Hadoop and Spark