Course Details
This Data Analytics course is design to teach you how to collect, process, and analyze data to extract meaningful insights for decision-making. Whether you are a beginner or a professional, this course covers the full data analytics process, from data cleaning and manipulation to data visualization and reporting. You will gain hands-on experience with tools and techniques used by data analysts across industries, enabling you to solve complex business problems, drive growth with data and positively influence your organization.
DATA ANALYTICS
This Data Analytics course is design to teach you how to collect, process, and analyze data to extract meaningful insights for decision-making. Whether you are a beginner or a professional, this course covers the full data analytics process, from data cleaning and manipulation to data visualization and reporting. You will gain hands-on experience with tools and techniques used by data analysts across industries, enabling you to solve complex business problems, drive growth with data and positively influence your organization.
- Understanding the role of data in business decisions.
- Overview of the data analytics lifecycle.
- Common tools used in data analytics (Excel, Python, SQL).
- Understanding data sources (databases, APIs, web scraping).
- Importing and cleaning data for analysis.
- Handling missing data and outliers.
- Data transformation techniques (scaling, encoding).
- Using descriptive statistics to summarize data.
- Detecting trends, patterns, and anomalies in data.
- Correlation and regression analysis.
- Tools for EDA (Excel, Python, Power BI).
- Best practices for visual storytelling with data.
- Creating different types of charts (bar, line, pie, scatter).
- Building interactive dashboards with Power BI.
- Customizing visualizations to enhance insights
- Introduction to probability and statistics.
- Hypothesis testing and A/B testing.
- Confidence intervals and significance levels.
- Practical applications of statistical methods.
- Forecasting and time series analysis.
- Cluster analysis and segmentation.
- Predictive modeling with machine learning algorithms.
- Using Python/Power BI for advanced analytics.
- Structuring and presenting findings to stakeholders.
- Creating clear, actionable data reports.
- Leveraging Power BI and Excel for report automation.
- Tailoring data insights for different audiences (executives, teams).
- Applying analytics to solve real-world business problems.
- Customer segmentation and behavior analysis.
- Market basket analysis for product recommendations.
- Performance tracking and KPI monitoring.
- Using data analytics to inform business strategies.
- Scenario analysis and data-driven forecasting.
- Case studies of successful data-driven companies.
- Tools and techniques for continuous improvement with data.
- Solving a business problem using data analytics.
- End-to-end data analysis project (data collection to reporting).
- Real-world case studies from different industries (finance, healthcare, retail).