Data Science with Python Tutorial

Data Science with Python focuses on extracting insights from data using libraries and analytical techniques. Python provides a rich ecosystem for data manipulation, visualization, statistical analysis and machine learning, making it one of the most popular tools for data science.

Before starting this tutorial, it is important to have a clear understanding of Data Science.

• Introduction
• Data Science vs Data Analytics

Python Concepts

Python is a high-level, interpreted programming language that is simple to learn. It's basics are:

• Installation
• Input and Output
• Variables
• Keywords
• Data Types
• Operators
• Conditional Statements
• Loops
• Functions
• String
• Lists
• Dictionary
• Tuples
• Sets
• Exception Handling

Libraries

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

• NumPy
• Pandas
• Matplotlib
• Seaborn
• Plotly
• Scikit-Learn

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

It involves cleaning and transforming raw data into a usable format for accurate and reliable analysis.

• Data Processing
• Data Preprocessing
• Working with Missing Data
• Removing Duplicates
• Scaling and Normalization of Data
• Aggregating and Grouping Data
• Feature Selection
• Categorical Data using Label Encoding
• 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

It is the process of inspecting data to discover meaningful insights and trends to make informed decision.

• Exploratory Data Analysis
• Univariate, Bivariate and Multivariate Analysis
• Calculating Correlation
• Sampling distribution
• Hypothesis testing
• T-test
• Z-test
• Chi-Square Test
• ANOVA (Analysis of Variance)
• MANOVA (Multivariate Analysis of Variance)
• Mann-Whitney U Test
• Shapiro-Wilk Test
• Wilcoxon Signed-Rank Test

Data Visualization

It 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
• Joint Plot
• Reg Plot

Data Visualization using Plotly

• Scatter Plot
• Bar Chart
• Line Chart
• Animated Data Visualization
• Choropleth Maps

Machine Learning

It focuses on developing algorithms that helps computers to learn from data and make predictions without explicit programming.

• Machine Learning Tutorial
• Deep Learning Tutorial