Data Scientist — Machine Learning Fundamentals

Dive deeper into Python by learning about essential data structures and control flow. - Description: Learn about fundamental Python data structures: lists, tuples, dictionaries, and sets. Understand their differences and when to use each. Explore control flow using if, elif, and else statements and loops (for and while). - Resources/Activities: - Expected Outcome: Be proficient in using Python data structures and control flow statements to write basic programs.

Learn NumPy, a crucial library for numerical computing in Python, and understand basic data manipulation techniques. - Description: Introduce the NumPy library and its use for numerical operations, particularly the creation and manipulation of arrays. Learn about array indexing, slicing, and basic mathematical operations. - Resources/Activities: - Expected Outcome: Familiarity with NumPy and its basic functionalities for numerical operations and data handling.

Learn Pandas, a library for data analysis and manipulation. Explore how to load, clean, and explore datasets using DataFrames. - Description: Introduce the Pandas library and its DataFrame structure. Learn how to load data from various formats (CSV, Excel) into a DataFrame. Understand how to explore data (viewing the first few rows, checking for missing values), cleaning strategies and basic exploratory data analysis (EDA). - Resources/Activities: - Expected Outcome: Ability to load, explore, and clean datasets using Pandas DataFrames.

Learn to visualize data using Matplotlib, a foundational library for creating plots and charts in Python. - Description: Introduce Matplotlib and how to create various types of plots, including line plots, scatter plots, histograms, and bar charts. Learn to customize plots with labels, titles, and legends. - Resources/Activities: - Expected Outcome: Proficiency in creating and customizing basic data visualizations using Matplotlib.

Gain a conceptual understanding of machine learning and its different types. - Description: Introduce the core concepts of machine learning: supervised learning, unsupervised learning, and reinforcement learning. Discuss common machine learning tasks, such as classification, regression, and clustering. Explain the concepts of training, testing, and model evaluation. - Resources/Activities: - Expected Outcome: Understanding of key machine learning concepts, including different types of machine learning and common machine learning tasks, training, and testing.

Learn to implement a simple machine learning model (linear regression) using Scikit-learn. - Description: Introduce Scikit-learn, a popular Python library for machine learning. Learn how to train a linear regression model on a simple dataset. Understand the process of splitting data into training and testing sets, training the model, and evaluating its performance. - Resources/Activities: - Expected Outcome: Ability to implement a simple machine learning model (linear regression) using Scikit-learn, from data loading to model evaluation.