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Our Course Content
- Overview of Data Analytics
- Importance and Applications
- Types of Data: Structured, Unstructured, Semi-Structured
- Data Sources and Types
- Data Collection Techniques
- Data Cleaning: Handling Missing Data, Outliers, and Data Transformation
- Descriptive Statistics (Mean, Median, Mode, Standard Deviation)
- Probability Theory
- Hypothesis Testing
- Correlation and Regression Analysis
- Introduction to Data Visualization
- Tools: Tableau, Power BI, Matplotlib, and Seaborn
- Creating Effective Dashboards and Reports
- Introduction to Python/R for Data Analytics
- Data Manipulation with Pandas (Python) or Data Frames (R)
- SQL for Querying Databases
- Identifying Patterns and Trends
- Data Summarization Techniques
- Visual EDA with Graphs, Histograms, and Scatterplots
- Introduction to Machine Learning Algorithms
- Supervised vs Unsupervised Learning
- Regression, Classification, and Clustering
- Introduction to Big Data Technologies (Hadoop, Spark)
- Data Analytics on Cloud Platforms (AWS, Google Cloud)
- Predictive Modeling
- Time Series Analysis
- Text Analytics
- Real-World Project: Applying Data Analytics to Solve a Problem
- Presentation of Findings
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- Accredited Training Partner
- Certified Training Partner
- Diversified Training Modules
- Tailored Courses
- Round-the-Clock Learning Access
- Weekly Assessment
- Placement Facilitation
