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Data Science Basic
Explore and master the fundamentals of Data Science Basic
You've not yet enrolled in this course. Please enroll for free to listen to audio lessons, classroom podcasts and take practice test.Chapter 1
Introduction to Data Science
Data science is a multidisciplinary field that combines mathematics, statistics, programming, and domain knowledge to extract meaningful insights from both structured and unstructured data. The role of a data scientist encompasses a wide range of tasks from data collection to model deployment, facilitating informed decision-making across various industries. The data science workflow involves several crucial phases including problem definition, data cleaning, exploratory analysis, modeling, and deployment.
Chapter 2
Data Types and Data Structures
Understanding data types and structures is crucial in data science. Various forms of data can be classified as structured, semi-structured, or unstructured, each with its own characteristics. Python provides a range of data types, such as integers, floats, strings, and booleans, and offers essential data structures like lists, dictionaries, and data frames for effective data management.
Chapter 3
Python for Data Science
Python's simplicity and robust ecosystem make it a fundamental tool for data science. The chapter covers basic programming concepts, essential libraries for data manipulation and visualization, and the setup of a Python environment using Jupyter Notebook.
Chapter 4
Data Collection Techniques
Data collection is fundamental in data science, involving methods for acquiring information from various sources, including files and online platforms. Techniques such as reading data from CSV, Excel, and APIs are crucial, along with web scraping and database interactions. Understanding these methods equips individuals to handle and analyze data effectively.
Chapter 5
Data Cleaning and Preprocessing
Data cleaning processes are essential for ensuring data accuracy, consistency, and usability. Techniques such as handling missing data, removing duplicates, and detecting outliers play crucial roles in data preprocessing. Moreover, converting data types and normalizing features enhances the performance of analytical models.
Chapter 6
Exploratory Data Analysis
Exploratory Data Analysis (EDA) is a critical method used to analyze data sets, revealing their main characteristics through both statistical and visual techniques. The key aspects of EDA include understanding data structure, detecting patterns, and preparing for subsequent modeling tasks. Utilizing tools such as Pandas, Matplotlib, and Seaborn facilitates effective analysis and visualization, allowing practitioners to derive meaningful insights and make informed decisions based on data anomalies and trends.
Chapter 7
Data Visualization
Data visualization is crucial for transforming data into meaningful insights, utilizing various Python libraries such as Matplotlib, Seaborn, and Plotly. The chapter covers different types of visualizations, their appropriate contexts, and best practices for clarity and effectiveness. By applying these techniques, learners can effectively communicate complex information and trends to diverse audiences.
Chapter 8
Introduction to Statistics
Statistics plays a crucial role in understanding and interpreting data. It covers descriptive and inferential statistics, measures of central tendency and dispersion, probability, distributions, and hypothesis testing, all essential for data science applications.
Chapter 9
Introduction to Machine Learning
Machine Learning focuses on creating algorithms that can learn from data and make predictions or decisions autonomously. It covers types of learning, including supervised and unsupervised, alongside the basic workflow for building models using tools like scikit-learn. The importance of splitting data for training and evaluation, as well as understanding key evaluation metrics, are also emphasized.
Chapter 10
Regression Analysis
Regression analysis is a statistical method employed to predict continuous outcomes by examining relationships between variables. It covers both simple and multiple linear regression techniques using Python, emphasizing model fitting and evaluation metrics for effective predictive performance.
Chapter 11
Classification Algorithms
Classification techniques are essential for predicting labels or categories within datasets, utilizing algorithms such as Logistic Regression, Decision Trees, and K-Nearest Neighbors (KNN). These methods are critically evaluated using metrics like accuracy, precision, recall, and F1-score, alongside the confusion matrix to visualize prediction results. Proper selection of classifiers is vital based on the complexity of the problem, the data size, and the interpretability of the results.
Chapter 12
Capstone Project & Career Path
This chapter emphasizes the importance of practical experience and demonstrates how to apply the data science process through a capstone project. It covers building a robust portfolio to showcase skills and outlines various career roles within data science. Additionally, it provides guidance on preparing for job interviews and offers resources for continuous learning and certification.