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.
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:
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
Data Visualization using Seaborn
Data Visualization using Plotly
Machine Learning
It focuses on developing algorithms that helps computers to learn from data and make predictions without explicit programming.
