Data Analytics Course

Data Analytics Course Syllabus

• Introduction to Data Analytics
  • Understanding the role of data analytics in various industries
  • Exploring the data analytics lifecycle
  • Differentiating between descriptive, predictive, and prescriptive analytics
  • Introduction to key tools and software used in data analytics
• Data Collection and Cleaning
  • Importance of data quality and integrity
  • Techniques for collecting and importing data
  • Exploring common data issues and anomalies
  • Data cleaning and preprocessing using basic tools
• Exploratory Data Analysis (EDA)
  • Overview of exploratory data analysis techniques
  • Visualizing data using graphs, charts, and histograms
  • Identifying patterns, trends, and outliers in data
  • Generating insights from initial data exploration
• Data Visualization
  • Importance of data visualization in conveying insights
  • Creating effective visualizations using tools like Excel or Python
  • Choosing the appropriate visualization types for different data scenarios
  • Design principles for clear and impactful data visualization
• Statistical Analysis
  • Introduction to basic statistical concepts
  • Descriptive statistics: mean, median, mode, and standard deviation
  • Hypothesis testing and p-values for data-driven decision-making
  • Applying statistical tests to real-world data
• Introduction to Python for Data Analytics
  • Setting up the Python environment for data analysis
  • Using libraries like NumPy and Pandas for data manipulation
  • Loading, cleaning, and exploring data with Python
  • Basic data analysis tasks using Python
• Data Wrangling and Transformation
  • Techniques for data transformation and feature engineering
  • Handling missing data and outliers
  • Merging, joining, and reshaping data tables
  • Preparing data for analysis and modeling
• Introduction to Data Visualization with Python
  • Using Matplotlib and Seaborn for data visualization
  • Creating various types of charts and graphs with Python
  • Customizing visualizations to convey insights effectively
  • Telling a story with data using Python visualizations
• Introduction to Data Analysis with Excel
  • Utilizing Excel for basic data analysis tasks
  • Using functions and formulas to summarize and analyze data
  • Creating pivot tables and charts for data exploration
  • Building simple data models and scenarios
• Introduction to Data Analytics Tools (Tableau, Power BI)
  • Overview of data analytics tools: Tableau or Power BI
  • Connecting to data sources and importing data
  • Creating interactive dashboards and visualizations
  • Presenting insights and findings using data analytics tools
• Case Studies and Practical Applications
  • Analyzing real-world datasets and scenarios
  • Applying data analytics techniques to solve practical problems
  • Identifying opportunities for data-driven decision-making
  • Presenting findings and insights from case studies
• Final Data Analytics Project and Course Conclusion
  • Applying all learned concepts to develop a comprehensive data analytics project
  • Planning, designing, and implementing a data analysis project
  • Reflecting on the learning journey and future applications of data analytics
  • Exploring potential career paths and further study in data analytics and related fields