skilldevelopment.cpe@lpu.co.in

What you'll learn

Course Description: This course introduces the fundamentals of Data Science, covering data collection, pre-processing, exploratory data analysis, data wrangling, feature engineering, and basic machine learning concepts. Students will gain hands-on experience using tools like Python, Pandas, NumPy, Matplotlib, and Seaborn to analyze and interpret real-world data effectively. Course Learning Outcomes (COs): At the end of the course, students will be able to: CO1: Understand fundamental concepts, scope, and tools of data science. CO2: Collect, clean, and pre-process data for analysis. CO3: Perform exploratory data analysis using visualization tools. CO4: Apply data wrangling and feature engineering techniques. CO5: Implement data science methods in real-world applications and case studies.

  • Definition and scope of data science, history and evolution of data science, applications and case studies, the data science process, tools and technologies for data science (Python, R, SQL).
  • Data types and sources, data collection techniques, data cleaning, handling missing values, data transformation, data integration, and data reduction.
  • Descriptive statistics, data visualization techniques, univariate analysis, bivariate analysis, multivariate analysis, using visualization tools (Matplotlib, Seaborn).
  • Techniques for data wrangling, handling, and transforming raw data, feature engineering, the importance of feature selection, methods for feature extraction, tools and libraries for data wrangling (Pandas, NumPy).
  • Introduction to Machine Learning, Traditional Programming vs. Machine Learning, Types of Machine Learning, Machine Learning Workflow, Common Algorithms (Conceptual Understanding Only), Applications of Machine Learning.
  • Real-world data science applications in business, healthcare, finance, social media, case studies, ethical considerations in data science, and future trends in data science.
  • Definition and scope of data science, history and evolution of data science, applications and case studies, the data science process, tools and technologies for data science (Python, R, SQL).
  • Data types and sources, data collection techniques, data cleaning, handling missing values, data transformation, data integration, and data reduction.
  • Descriptive statistics, data visualization techniques, univariate analysis, bivariate analysis, multivariate analysis, using visualization tools (Matplotlib, Seaborn).
  • Techniques for data wrangling, handling, and transforming raw data, feature engineering, the importance of feature selection, methods for feature extraction, tools and libraries for data wrangling (Pandas, NumPy).
  • Introduction to Machine Learning, Traditional Programming vs. Machine Learning, Types of Machine Learning, Machine Learning Workflow, Common Algorithms (Conceptual Understanding Only), Applications of Machine Learning.
  • Real-world data science applications in business, healthcare, finance, social media, case studies, ethical considerations in data science, and future trends in data science.

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Azhar Abass Malik
Assistant Professor

Assistant Professor