Data Science

Description

Month 1: Foundations of Data Science

1. Understand the Basics:
   • Learn about the fundamentals of data science, including what it is, its applications, and its importance in various industries.
   • Familiarize yourself with key concepts such as data types, data structures, and basic statistics.
2. Learn Programming Languages:
   • Start learning Python, which is widely used in data science due to its simplicity and powerful libraries like NumPy, pandas, and scikit-learn.
   • Get comfortable with basic Python syntax, data types, control structures, and functions.
3. Introduction to Data Analysis:
   • Dive into data analysis with pandas, a Python library for data manipulation and analysis.
   • Learn how to load, clean, manipulate, and analyze data using pandas.
4. Statistics and Probability:
   • Study foundational statistical concepts such as mean, median, mode, variance, standard deviation, probability distributions, and hypothesis testing.
   • Practice applying statistical techniques to analyze data and draw meaningful insights.

Month 2: Exploratory Data Analysis (EDA) and Visualization

1. Exploratory Data Analysis (EDA):
   • Learn the importance of EDA in understanding datasets and identifying patterns, trends, and relationships.
   • Practice performing EDA techniques such as summary statistics, data visualization, and correlation analysis.
2. Data Visualization:
   • Explore data visualization libraries like Matplotlib and Seaborn in Python.
   • Learn how to create various types of plots, including histograms, scatter plots, bar charts, and heatmaps, to effectively communicate insights from data.
3. Advanced Data Manipulation:
   • Expand your skills in data manipulation with pandas by learning more advanced techniques like groupby operations, merging and joining datasets, and handling missing data.

Month 3: Machine Learning Fundamentals

1. Introduction to Machine Learning:
   • Learn the basic concepts of machine learning, including supervised learning, unsupervised learning, and reinforcement learning.
   • Understand the difference between classification and regression problems.
2. Supervised Learning:
   • Dive into supervised learning algorithms such as linear regression, logistic regression, decision trees, and k-nearest neighbors (KNN).
   • Learn how to train, evaluate, and tune machine learning models using scikit-learn.
3. Unsupervised Learning:
   • Explore unsupervised learning techniques like clustering (K-means clustering, hierarchical clustering) and dimensionality reduction (principal component analysis – PCA).
   • Understand when and how to apply unsupervised learning algorithms to real-world datasets.
4. Capstone Project:
   • Apply your knowledge and skills by working on a data science project from start to finish.
   • Choose a dataset of interest, perform exploratory data analysis, build and evaluate machine learning models, and communicate your findings through visualizations and insights.