Python offers a rich set of libraries that make working with data faster and more efficient, even when the data is large or messy. In Data Science with Python, you mainly work with tasks such as:
- Collecting data from files, databases or APIs.
- Cleaning and preparing data for analysis.
- Exploring data to find patterns and trends.
- Visualizing data using charts and graphs.
- Building models to make predictions or classifications.
Libraries like NumPy and Pandas help in handling data, Matplotlib and Seaborn are used for visualization and Scikit-learn is widely used for machine learning.
Getting Started with Data Science
Before starting this tutorial, it is important to have a clear understanding of the fundamental concepts that form the backbone of Data Science.
Basic Python Concepts
Python is a high-level, interpreted programming language that is simple to learn and widely used in areas such as data science. So having a strong foundation in Python is important.
- Download and Install Python 3
- Input and Output in Python
- Python Variables
- Python Keywords
- Python Data Types
- Python Operators
- Conditional Statements in Python
- Loops in Python
- Python Functions
- Python String
- Python Lists
- Python Dictionary
- Python Tuples
- Sets in Python
- Python Exception Handling
Python Libraries for Data Science
To gain expertise in data science, you need to have a strong foundation in the following libraries:
- NumPy for Numerical Computing
- Pandas for Data Manipulation
- Matplotlib for Data Visualization
- Seaborn for Data Visualization
- Plotly for Data Visualization
- Scikit-learn for Machine Learning
Data Loading
Data loading means importing raw data from various sources and storing it in one place for further analysis.
- Loading a CSV File into a DataFrame
- Loading Data from an Excel File
- Loading Data from JSON File
- Loading Data from SQL Databases
- Web Scraping using BeautifulSoup to Scrape Data
- Loading Data from MongoDB into DataFrame
Data Preprocessing
Data preprocessing involves cleaning and transforming raw data into a usable format for accurate and reliable analysis.
- What is Data Processing?
- What is Data Preprocessing?
- Working with Missing Data using Pandas
- Removing Duplicates using drop_duplicates()
- Scaling and Normalization of Data
- Aggregating and Grouping Data
- Feature Selection using Sklearn
- Handling Categorical Data using Label Encoding
- Handling Categorical Data using One-Hot Encoding
- Detecting outlier using Z score
- Detecting outlier using Interquartile Range
- Handling Imbalanced Data
- Efficient Preprocessing for Large Datasets
Data Analysis
Data analysis is the process of inspecting data to discover meaningful insights and trends to make informed decision.
- Exploratory Data Analysis in Python
- Univariate, Bivariate and Multivariate Analysis
- Calculating Correlation
- Sampling distribution Using Python
- Hypothesis testing using Python
- T-test using Python
- Z-test in Python
- Chi-Square Test
- ANOVA (Analysis of Variance) in Python
- MANOVA (Multivariate Analysis of Variance)
- Mann-Whitney U Test in Python
- Shapiro-Wilk Test in Python
- Wilcoxon Signed-Rank Test in Python
Data Visualization
Data visualization uses graphical representations such as charts and graphs to understand and interpret complex data.
Data Visualization using Matplotlib
Data Visualization using Seaborn
Data Visualization using Plotly
Machine Learning
Machine learning focuses on developing algorithms that helps computers to learn from data and make predictions or decisions without explicit programming.