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Python Data Science Real Python

Kenji Sato
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python data science real python

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.

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