Data Analytics Course

  • Introduction to Data Analytics
  • Importance of Data in Decision Making
  • Overview of Data Analytics Tools

  • Descriptive Statistics
  • Inferential Statistics
  • Hypothesis Testing

  • Python for Data Analysis
  • Libraries: NumPy, Pandas
  • Data Visualization with Matplotlib and Seaborn

  • Sales Data Analysis for Trend Prediction
  • Business KPI Dashboard using Tableau
  • Customer Segmentation using Clustering
  • Sentiment Analysis on Social Media Data
  • Website Traffic Analysis for UX Optimization

  • Data Analyst
  • Business Intelligence Analyst
  • Data Scientist
  • Data Engineer

  • Data Cleaning Techniques
  • Data Transformation Techniques
  • Introduction to Data Wrangling
  • Data Engineer

  • Introduction to SQL
  • Querying Databases
  • Data Aggregation and Joins

  • Principles of Data Visualization
  • Tools: Tableau, Power BI
  • Creating Interactive Dashboards

  • Introduction to Machine Learning
  • Supervised vs. Unsupervised Learning
  • Basic Algorithms: Regression, Classification

Graduates can pursue roles such as:
  • Data Analyst
  • Business Intelligence Analyst
  • Data Scientist
  • Data Engineer

  • Proficiency in data analysis tools and programming languages
  • Ability to interpret and visualize data effectively
  • Strong analytical and problem-solving skills

  • The demand for skilled data analysts is increasing, making this a promising career path for those interested in data-driven decision-making.
  • User Can you provide some examples of projects that could be included in a data analytics course?
  • Projects in a data analytics course might include analyzing sales data to identify trends and make recommendations for improving sales strategies, creating a dashboard to visualize key performance indicators for a business, or conducting a customer segmentation analysis using clustering techniques.
  • Other examples could involve performing sentiment analysis on social media data to gauge public opinion about a brand, developing a predictive model to forecast future sales based on historical data, or analyzing website traffic data to optimize user experience. Students may also work on case studies that require them to present their findings and insights to stakeholders, simulating real-world data analysis scenarios.