Who Should Enroll?

This course is ideal for:

• Aspiring data scientists and analysts seeking structured, practical training.
• Developers and software engineers transitioning into data-driven roles.
• Beginners looking for a clear, step-by-step path into data science.
• Professionals aiming to strengthen their skills in Python, SQL, and machine learning with applied projects.

What You'll Learn

Our curriculum is structured into 8 rigorous modules that combine technical depth with real-world applicability:

1.Python for Data Science

• Learn core programming concepts and manipulate datasets using NumPy and Pandas.

2.Data Visualization

• Master Matplotlib, Seaborn, and Plotly to turn raw data into visual insights.

3.Statistics & Probability

• Build strong statistical reasoning for data interpretation and model validation.

4.SQL for Data Science

• Query, join, and aggregate data from databases using SQL integrated with Python.

5.Exploratory Data Analysis (EDA) & Feature Engineering

• Detect patterns, clean messy data, and prepare features for modeling.

6.Classical Machine Learning

• Train and evaluate supervised and unsupervised models including regression, classification, and clustering.

7.Time Series Analysis (Intro)

• Identify temporal trends and build simple forecasting models.

8.Model Deployment & Final Project

• Export models, create interactive dashboards with Streamlit, and version projects using Git.

Learning Outcomes

By the end of this course, you will be able to:

• Write efficient Python code for data analysis and preprocessing.
• Visualize complex datasets to uncover actionable insights.
• Apply statistical techniques for data-driven decision-making.
• Manipulate databases using advanced SQL queries.
• Engineer features and perform exploratory data analysis.
• Train, evaluate, and interpret machine learning models.
• Deploy machine learning solutions as interactive web apps.
• unview and present an end-to-end data science project on GitHub.

Real-World Projects Include

• Cleaning and analyzing public datasets using Pandas.
• Building a visual dashboard of company sales
• Writing SQL queries to analyze business KPIs
• Predicting housing prices or customer churn
• Clustering retail locations based on consumer behavior
• Forecasting sales using historical time-series data
• Deploying a model using Streamlit and hosting on GitHub

Certification

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What you'll learn

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