Data science enables organizations to make informed decisions, solve problems, and understand human behavior. As the volume of data grows, so does the demand for skilled data scientists. The most common languages used for data science are Python and R, with Python being particularly popular as:
- Easy to Learn: Python’s readable syntax makes it accessible to beginners.
- Rich Library Ecosystem: Python provides extensive libraries such as Pandas and NumPy, essential for data analysis and machine learning.
- Strong Community Support: Python boasts a large and active community, offering ongoing support and learning opportunities.
The Data Science with Python tutorial will guide you through the fundamentals of both data science and Python programming.
Before starting the tutorial, you can refer to these articles:
- What is Data Science?
- Why Do We Need Data Science?
- Python for Data Science
- Setting Up a Data Science Environment
Python Libraries for Data Science
- Pandas for Data Manipulation
- NumPy for Numerical Computing
- Scikit-learn for Machine Learning
- Matplotlib for Data Visualization
Data Loading
- Loading a CSV File into a DataFrame using pandas.read_csv()
- Loading Data from an Excel File using pandas.read_excel()
- Loading Data from JSON Files using pandas.read_json()
- Loading Data from SQL Databases using pandas.read_sql()
- Web Scraping using BeautifulSoup to Scrape Data
- Loading Data from MongoDB into a Pandas DataFrame using pymongo
Data Preprocessing Using Python
- What is Data Preprocessing?
- Working with Missing Data using Pandas
- Detecting Duplicate Rows in a DataFrame
- Removing Duplicates using drop_duplicates()
- Scaling and Normalization of Data
- Feature Transformation of Data Columns
- Feature Selection using Sklearn
- Handling Categorical Data using Label Encoding
- Handling Categorical Data using One-Hot Encoding
- Handling Categorical Data using Ordinal Encoding
- Identifying Outliers in Data
- Detecting outlier using Z score
- Detecting outlier using Interquartile Range
- Box-Cox Transformation to Normalize Skewed Data
- Handling Imbalanced Data
- Splitting Data into Training and Test Sets
- Efficient Preprocessing for Large Datasets
Data Analysis
- What is Data Processing?
- Exploratory Data Analysis
- Univariate and Multivariate Analysis
- Using Pandas describe() to Summarize Data
- Identifying Skewness and Kurtosis
- Calculating Correlation using pandas.corr()
- Hypothesis testing using Python
- One-sample t-test using Python
- Two Sample t-test using Python
- ANOVA Analysis using StatsModels
- Aggregating and Grouping Data Using groupby()
- Statistical Tests for Categorical Data: Chi-Square Test
- Applying PCA for Dimensionality Reduction in Python
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Data Visualization
Importance of Data Visualization in Data Science
- Data Visualization using Matplotlib
- Data Visualization using Seaborn
- Using Plotly for Interactive Data Visualization in Python
- Interactive Data Visualization with Bokeh
Data Visualization using Matplotlib
- Line Plot
- Bar Plot
- Histogram
- Box Plot
- Scatter Plot
- Pie Chart
- Stacked Bar Plot
- Step Plot
- Hexbin Plot
- 3D Plot
- Quiver Plot
