Data Science with Python

In this course, you will learn how to manipulate and analyze data using Python and its powerful libraries like Pandas and NumPy. You will explore various data visualization techniques with tools such as Matplotlib and Seaborn to present insights effectively. Additionally, the course will guide you through implementing machine learning algorithms to build predictive models, providing you with the skills to tackle real-world data challenges. By the end of the course, you’ll be well-equipped to analyze complex datasets and make data-driven decisions.

3 months

Basics & Foundation - Introduction to Python and Data Science

This week introduces the world of Data Science and lays the groundwork in Python programming. We'll explore what Data Science is, its applications, and begin with Python basics, diving into syntax, data types, and control structures to build a strong programming foundation.

Course Details:

Introduction to Data Science: Definition, scope, and applications.
Python Basics: Installation (Anaconda, Jupyter), syntax, data types, control structures, functions.

Practice Work:

Basic Python scripts focusing on variables, loops, and functions. Exercise: Implement a factorial function.

By the End of the Week:

Understanding of Data Science concepts and foundational Python skills.

Data Manipulation - Mastering Data with Python Libraries

Delving deeper into Python's capabilities, this week is all about handling data. We'll master the use of libraries like Numpy and Pandas for effective data manipulation, which is crucial for any data science project.

Course Details:

Introduction to Numpy and Pandas for data manipulation.

Practice Work:

Load, clean, and manipulate datasets using Pandas.
Exercise: Filter, sort, and group data from a CSV file.

By the End of the Week:

Proficiency in handling data with Python libraries.

Data Cleaning - Preparing Data for Analysis

Clean data is the backbone of reliable analysis. This week focuses on cleaning techniques, teaching how to handle missing values, normalize data, and encode variables to prepare datasets for further exploration and modeling.

Course Details:

Data cleaning techniques: Handling missing values, data normalization, encoding categorical variables.

Practice Work:

Clean a dataset, handle missing data, perform normalization.
Exercise: Create a pipeline for data preprocessing.

By the End of the Week:

Ability to prepare data for analysis effectively.

Visualization - Communicating Insights Visually

Data visualization is key to understanding and presenting data. This week we'll learn to use Matplotlib and Seaborn to transform data into insightful visuals, making complex data more comprehensible.

Course Details:

Data visualization with Matplotlib and Seaborn.

Practice Work:

Create different types of plots, visualize dataset characteristics.
Exercise: Build a data dashboard for insights presentation.

By the End of the Week:

Skills to communicate data insights visually.

EDA Insights - Uncovering Data Stories

Exploratory Data Analysis (EDA) is where the narrative of data begins. This week, students will learn to use EDA techniques to uncover patterns, trends, and anomalies in datasets, providing a deeper understanding of the data at hand.

Course Details:

Exploratory Data Analysis (EDA): Techniques and tools.

Practice Work:

Perform EDA on a dataset, identify correlations and trends.
Exercise: Write an EDA report for a given dataset.

By the End of the Week:

Enhanced ability to uncover data stories and patterns.

Stats Essentials - Statistical Tools for Data Scientists

A strong statistical foundation is vital for data science. This week introduces key statistical concepts, from descriptive statistics to hypothesis testing, equipping students with the tools to validate data-driven decisions.

Course Details:

Statistics for Data Science: Descriptive stats, probability, hypothesis testing.

Practice Work:

Apply statistical methods to datasets, test hypotheses.
Exercise: Conduct a statistical analysis on a sample dataset.

By the End of the Week:

Grounding in statistical methods for data analysis.

ML Fundamentals - Introduction to Machine Learning

This week marks the beginning of machine learning, where we'll explore foundational concepts of supervised and unsupervised learning. Students will start building simple models to predict outcomes.

Course Details:

Introduction to Machine Learning: Supervised vs. unsupervised learning.

Practice Work:

Implement basic ML models (linear/logistic regression). Exercise: Predict outcomes using regression on a real-world dataset.

By the End of the Week:

Basic understanding of machine learning concepts and model building.

Advanced ML - Deep Dive into Machine Learning Algorithms

Moving beyond basics, this week delves into advanced machine learning algorithms like Decision Trees, Random Forests, and SVM, providing students with tools for more complex data problems.

Course Details:

Advanced ML techniques: Decision Trees, Random Forests, SVM.

Practice Work:

Compare different ML algorithms on a dataset.
Exercise: Optimize and compare decision tree models.

By the End of the Week:

Deeper knowledge of ML algorithms and model selection.

Deep Learning - Basics of Neural Networks and Deep Learning

Enter the realm of artificial neural networks with this week's introduction to deep learning. Students will learn to construct and train neural networks using TensorFlow/Keras for tasks like image classification.

Course Details:

Deep Learning basics with TensorFlow/Keras: Neural Networks, CNNs, RNNs.

Practice Work:

Build and train neural networks for image classification.
Exercise: Implement a CNN for a small image dataset.

By the End of the Week:

Initial exposure to deep learning frameworks and architectures.

Big Data - Handling Large-Scale Data with Python

As data scales up, so do our methods. This week introduces big data concepts and tools like Dask and PySpark, teaching how to process and analyze large datasets efficiently in Python.

Course Details:

Big Data with Python: Concepts, Dask, PySpark basics.

Practice Work:

Handle large datasets using distributed computing.
Exercise: Perform operations on a big dataset using PySpark.

By the End of the Week:

Competence in processing large datasets with Python.

Time Series - Analyzing Time-Dependent Data

Time series data requires special handling. This week we'll analyze time-dependent data, learn forecasting techniques with ARIMA and SARIMA models, and apply this knowledge to practical scenarios.

Course Details:

Time Series Analysis: Decomposition, forecasting with ARIMA, SARIMA.

Practice Work:

Analyze and predict time series data.
Exercise: Forecast stock prices or weather patterns.

By the End of the Week:

Skills in time series data analysis and forecasting.

Capstone Project - Applying All Learned Skills

This week is dedicated to a capstone project where students apply all their knowledge. It's a chance to demonstrate proficiency in data science by tackling a real-world problem, from data collection to model deployment and presentation.

Course Details:

Project management, presentation skills, ethical considerations in Data Science.

Practice Work:

Capstone project: Choose, analyze, model, and present a dataset or problem.
Exercise: Peer review of projects, focusing on methodology and presentation.

By the End of the Week:

Practical experience, project showcase, and understanding of data science workflow in practice.