Data Science with Python Tutorial

Data Science has become one of the fastest-growing fields in recent years, helping 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.

Before starting the tutorial you can refer to these articles:

• What is Data Science?
• Setting Up Data Science Environment

Python Libraries for Data Science

To gain expertise in data science, you need to have a strong foundation in the following libraries:

• Pandas for Data Manipulation
• NumPy for Numerical Computing
• Matplotlib for Data Visualization
• Seaborn 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 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.

• What is Data Processing?
• Exploratory Data Analysis in Python
• Univariate and Multivariate Analysis
• Calculating Correlation
• Hypothesis testing using Python
• One-sample t-test using Python
• Two Sample t-test using Python
• ANOVA (Analysis of Variance) in Python
• Mann-Whitney U Test in Python
• Z-test in Python
• Chi-Square Test
• PCA with 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

• Line Chart
• Bar Plot
• Histogram
• Heatmap
• Box Plot
• Scatter Plot
• Pie Chart
• 3D Plot

Data Visualization using Seaborn

• Pair Plot
• Count Plot
• Violin Plot
• Strip Plot
• KDE Plot (Kernel Density Estimate)
• Joint Plot
• Reg Plot

Interactive Visualization

• Scatter Plot
• Bar Chart
• Line Chart
• Animated Data Visualization
• Choropleth Maps using
• Interactive Visualization using Bokeh
• Visualizing Geospatial Data using Folium

Machine Learning

Machine learning focuses on developing algorithms that helps computers to learn from data and make predictions or decisions without explicit programming.

• Machine Learning Tutorial
• Deep Learning Tutorial

Introduction To Data Science

Introduction To Data Science

Introduction to Linear Regression - Machine Learning

Naive Bayes Classifiers

Decision Tree in Machine Learning

Random Forest Algorithm in Machine Learning

K-Nearest Neighbor(KNN) Algorithm in Machine Learning