Data Science in 30 days

Hello there! My name is Elvis Wangari, and welcome to the "Data Science in 30 Days" series.

In this series, we'll take you from the basics of data science to advanced concepts and practical implementations. Whether you're a beginner or an experienced professional looking to refresh your skills, this course is for you. Each lesson is packed with theoretical concepts, practical examples, and hands-on projects, giving you the confidence to tackle data science projects on your own.

Let’s get started on this exciting journey!

Curriculum: Data Science in 30 Days

Note: This curriculum is subject to updates to provide the most relevant content.

Week 1: Foundations of Data Science (Beginner Level)

Day 1: Introduction to Data Science

• What is Data Science?
  • Overview of data science as a field and its importance.
  • Real-world applications of data science (e.g., healthcare, marketing, finance).
• Why Data Science?
  • How data science is shaping industries and businesses.
  • Careers in data science: Data Analyst, Data Scientist, ML Engineer.

Day 2: Data Types and Sources

• Types of Data
  • Structured, unstructured, and semi-structured data.
  • Categorical vs. numerical data.
• Data Collection
  • Data collection methods: surveys, web scraping, APIs, databases.
  • Importance of clean, reliable data.

Day 3: Python for Data Science

• Python Basics
  • Variables, loops, functions, data structures (lists, tuples, dictionaries).
  • Working with Jupyter Notebooks.
• Introduction to NumPy and Pandas
  • NumPy arrays and operations.
  • Pandas for data manipulation: Series and DataFrames.

Day 4: Data Cleaning and Preprocessing

• Data Cleaning Techniques
  • Handling missing data (imputation, removal).
  • Handling outliers and inconsistencies in the dataset.
• Data Preprocessing
  • Normalization, scaling, and transformations (log, sqrt, etc.).

Day 5: Exploratory Data Analysis (EDA)

• Descriptive Statistics
  • Mean, median, mode, variance, standard deviation.
  • Summarizing the dataset using Pandas.
• Data Visualization
  • Visualizing data using Python libraries (matplotlib, seaborn).
  • Scatter plots, histograms, bar charts, and heatmaps.

Day 6: Introduction to Statistics for Data Science

• Probability Concepts
  • Basic probability, events, and independence.
  • Bayes’ Theorem.
• Distributions
  • Normal, binomial, and Poisson distributions.
  • Central Limit Theorem and its importance.

Day 7: Project Day: Mini Data Analysis

• Project: Analyze a dataset (e.g., a CSV file with sales data).
  • Clean and preprocess the data.
  • Perform EDA using visualization techniques and descriptive statistics.
  • Present findings with visualizations and summaries.

---

Week 2: Intermediate Data Science Concepts

Day 8: SQL for Data Science

• SQL Basics
  • Introduction to databases and SQL.
  • Writing basic queries (SELECT, WHERE, JOIN, GROUP BY).
• SQL for Data Analysis
  • Using SQL to extract and manipulate data.

Day 9: Data Visualization with Python

• Advanced Visualization Techniques
  • Creating advanced plots using seaborn (pair plots, violin plots, etc.).
  • Interactive visualizations with Plotly.
• Visualization Best Practices
  • Data storytelling: How to present insights effectively.

Day 10: Introduction to Machine Learning (ML)

• Machine Learning Basics
  • Supervised vs. unsupervised learning.
  • Machine learning workflow: training, validation, testing.
• Types of Machine Learning Algorithms
  • Introduction to regression, classification, and clustering.

Day 11: Linear Regression

• Simple Linear Regression
  • Concept of linear regression and its applications.
  • Fitting a linear model to data.
• Multiple Linear Regression
  • Handling multiple input features.
  • Evaluating model performance: R-squared, MSE.

Day 12: Classification Algorithms

• Logistic Regression
  • Binary classification using logistic regression.
  • Understanding the sigmoid function.
• Decision Trees
  • Concept of decision trees and how they are used in classification.

Day 13: Clustering Algorithms

• K-Means Clustering
  • Introduction to unsupervised learning.
  • Clustering data points into groups based on similarity.
• Hierarchical Clustering
  • Concept of hierarchical clustering.
  • Visualizing clusters with dendrograms.

Day 14: Project Day: Machine Learning Basics

• Project: Implement machine learning models on a real-world dataset.
  • Clean and preprocess the dataset.
  • Apply linear regression, logistic regression, or clustering.
  • Evaluate and present the results with visualizations.

---

Week 3: Advanced Data Science Concepts

Day 15: Feature Engineering

• Creating New Features
  • Extracting new features from raw data (e.g., date/time).
• Feature Selection
  • Methods for selecting important features (correlation, mutual information).

Day 16: Model Evaluation and Hyperparameter Tuning

• Cross-Validation
  • K-fold cross-validation.
  • Model validation strategies to avoid overfitting.
• Hyperparameter Tuning
  • Grid search and random search techniques to tune model parameters.

Day 17: Introduction to Deep Learning

• Neural Networks Basics
  • Understanding neurons and layers.
  • How deep learning differs from traditional machine learning.
• Frameworks
  • Introduction to TensorFlow and Keras.
  • Building a simple neural network for classification tasks.

Day 18: Natural Language Processing (NLP)

• Text Preprocessing
  • Tokenization, stopwords removal, stemming, and lemmatization.
• Sentiment Analysis
  • Using NLP techniques to analyze sentiment in text data.

Day 19: Time Series Analysis

• Components of Time Series
  • Trend, seasonality, and noise.
  • Moving averages and smoothing techniques.
• Forecasting Models
  • ARIMA and exponential smoothing.

Day 20: Ensemble Methods

• Bagging and Boosting
  • Understanding ensemble techniques.
• Random Forests and Gradient Boosting (XGBoost)
  • Implementing random forests and gradient boosting models.

Day 21: Project Day: Advanced Machine Learning/Deep Learning

• Project: Build a machine learning/deep learning model on a complex dataset.
  • Preprocess and feature engineer the dataset.
  • Apply deep learning or advanced machine learning techniques.
  • Present results and evaluation metrics.

---

Week 4: Data Science Applications and Industry Readiness

Day 22: Big Data Technologies

• Introduction to Big Data
  • Overview of big data concepts.
• Tools for handling big data: Hadoop and Spark.
• Working with Large Datasets
  • Strategies to handle datasets that don’t fit into memory.

Day 23: Data Science in Industry

• Real-World Case Studies
  • Use cases of data science in healthcare, finance, and marketing.
• Industry-Specific Tools
  • Specialized tools used in different industries (e.g., healthcare, retail).

Day 24: Ethical Considerations in Data Science

• Data Privacy
  • Introduction to data privacy regulations (GDPR, CCPA).
• Bias and Fairness
  • Avoiding bias in algorithms and ensuring fairness in machine learning.

Day 25: Model Deployment

• Model Deployment Techniques
  • Using Flask and FastAPI to deploy models as APIs.
  • Cloud deployment: Heroku and AWS.
• Monitoring and Maintenance
  • Monitoring model performance post-deployment.

Day 26: Introduction to Power BI

• Creating Dashboards
  • Introduction to Power BI interface and dashboard creation.
  • Connecting to datasets and building interactive reports.

Day 27: AI and Future Trends in Data Science

• AI Advancements
  • Recent breakthroughs in AI (e.g., GPT models, self-supervised learning).
• Future Trends
  • Emerging trends in data science and artificial intelligence.

Day 28: Career Paths in Data Science

• Roles in Data Science
  • Overview of roles: Data Scientist, Data Analyst, ML Engineer.
• Building a Career
  • How to build a portfolio, apply for jobs, and advance in the field.

Day 29: Final Project: Comprehensive Data Science Project

• End-to-End Project: Apply everything learned from the course.
  • Data collection, cleaning, analysis, model building, and deployment.
  • Showcase results with visualizations and reports.

Day 30: Review and Future Learning Resources

• Recap Key Concepts
  • Review the core concepts from the course.
• Additional resources for continued learning and growth.