Data Science And Analysis | Ikaze Inc🎈

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Introduction to Data Science 4 Topics

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What is data science?

The importance of data in the modern world

The data science process: data collection, cleaning, analysis, and visualization

Data science tools and technologies.

Data Collection and Preparation 4 Topics

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Types of data (structured, unstructured, semi-structured)

Data sources (databases, APIs, web scraping, social media)

Data formats (CSV, JSON, XML).

Data cleaning techniques (handling missing values, outliers, inconsistencies)

Exploratory Data Analysis (EDA) 4 Topics

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Descriptive statistics (mean, median, mode, standard deviation)

Data visualization techniques (histograms, scatter plots, box plots)

Correlation analysis.

Outlier detection

Statistical Modeling 4 Topics

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Probability distributions

Hypothesis testing

Regression analysis (linear, logistic)

Time series analysis

Machine Learning 4 Topics

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Supervised learning (classification, regression)

Unsupervised learning (clustering, dimensionality reduction)

Common machine learning algorithms (decision trees, random forests, neural networks)

Model evaluation metrics (accuracy, precision, recall, F1-score)