Introduction to Data Analytics
Overview of data analytics and its applications Data lifecycle and workflow
Overview of data analytics and its applications
Data lifecycle and workflow
- Data Cleaning and Collection
Data sources and types (structured data vs. unstructured data)
Data collection techniques (surveys, APIs, web scraping, etc.)
Data cleaning methods: missing values, duplicates, outliers, and inconsistencies handling
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- Exploratory Data Analysis and Visualization
Exploratory Data Analysis (EDA)
Summary statistics and distribution analysis
Data visualization methods and tools
- Statistical Analysis
Probability and statistical distributions
Hypothesis testing and confidence intervals
Correlation and regression analysis
Inferential statistics
- Tools and Technologies
Excel for simple analysis
SQL for querying databases
Python or R for advanced analytics
Introduction to Jupyter Notebooks or RStudio
Supervised vs. unsupervised learning
Model evaluation metrics
- Data Ethics and Governance
Data privacy and security
Ethical considerations in data usage
Regulatory compliance (e.g., GDPR)
- Capstone Project or Case Studies
Real-world datasets
End-to-end project using data cleaning, analysis, visualization, and reporting
Presentation of findings
Please visit our website:- Data Analytics Course in Pune
Overview of data analytics and its applications
Data lifecycle and workflow
- Data Cleaning and Collection
Data sources and types (structured data vs. unstructured data)
Data collection techniques (surveys, APIs, web scraping, etc.)
Data cleaning methods: missing values, duplicates, outliers, and inconsistencies handling
- Exploratory Data Analysis and Visualization
Exploratory Data Analysis (EDA)
Summary statistics and distribution analysis
Data visualization methods and tools
- Statistical Analysis
Probability and statistical distributions
Hypothesis testing and confidence intervals
Correlation and regression analysis
Inferential statistics
- Tools and Technologies
Excel for simple analysis
SQL for querying databases
Python or R for advanced analytics
Introduction to Jupyter Notebooks or RStudio
Supervised vs. unsupervised learning
Model evaluation metrics
- Data Ethics and Governance
Data privacy and security
Ethical considerations in data usage
Regulatory compliance (e.g., GDPR)
- Capstone Project or Case Studies
Real-world datasets
End-to-end project using data cleaning, analysis, visualization, and reporting
Presentation of findings
Please visit our website:- Data Analytics Training in Pune